Copyright (c) by Esri Press Do not distribute except t

Copyright (c) by Esri Press
Do not distribute except to students using the book in a course.
Table of Contents
Introduction 1
Acknowledgements 6
PART I
Chapter 1. Fundamentals of Location Value 8
Chapter 2. Fundamentals of Spatial Technology 23
Chapter 3. Fundamentals of Location Analytics 43
PART II
Chapter 4. Growing Markets and Customers 63
Chapter 5. Operating the Enterprise 87
Chapter 6. Managing Business Risk and Increasing Resilience
106
Chapter 7. Enhancing Corporate Social Responsibility 120

PART III
Chapter 8. Business Management and Leadership
138
Chapter 9. Strategies and Competitiveness 156
Chapter 10. Themes and Implications for Practice
173
References 191
Introduction
Technology and Location
A quarter-century ago, Frances Cairncross (1997) proclaimed
the “Death of Distance”
and a future not bound by location but connected via the
electronic revolution. In the ensuing
decades, it has undoubtedly been the case that individual
lifestyles, the economy, and the
world have been transformed by ongoing digital
transformations.
But, alas, 25 years later, location is not dead, but deeply
intertwined with technology.
We live in a global economy, but that economy varies widely by
region and location. We live in
a high-tech world that allows for unparalleled virtual
connections, and yet these high-tech
companies tend to cluster in certain regions of the world. We
live in a world where shopping

can be done entirely online, but these products are sourced
through intricate supply chains that
deliver the product to one’s doorstep.
Location intelligence is embedded in these contemporary
dynamics—that is, businesses
need to know where to source, where to operate, where to
market, where to grow, and so
forth. This book is intended to inform business professionals as
well as business students about
this new world of location intelligence and how to utilize this
intelligence to achieve business
success. It also aims to inform geographic information system
(GIS) professionals and students
about how location analytics can be considered and utilized
within business functions and
strategies. Indeed, the book unites these domains (business,
GIS) into a sphere we call Spatial
Business.
To support business progress in this expanding space, the
geospatial industry is growing
in its capacity to support location analytics, GIS, web and
cloud-based processing and display,
satellite and drone imagery, LIDAR scanning, and navigation
and indoor positioning tools. The
total size of the geospatial industry is estimated to be $439
billion (US) by 2024 and at a
compound annual growth rate of 13.8% (GMC 2019).
With this level of location digitization and growth, location
intelligence has become
foundational to business in its marketing, operations, services,
risk management, deployment
of assets, and many other functions. Through location analytics
and location intelligence, a firm

can leverage location information to make better-informed
decisions and ultimately create
value to the business and often to society as well. There are
numerous examples of companies
that have successfully built-up location analytics capacity and
have been able to use the
ensuing insights to better serve consumers, operate more
efficiently, and achieve competitive
advantage. What has been needed is an integrated perspective
on these developments and
that is the aim of this book.
Spatial Business Organization
This book seeks to provide a contemporary foundation for
understanding the business
and locational knowledge base to solve spatial problems,
support location-based decision-
1
making, and create location value. Our approach is to do so can
be seen in Figure 1, which
provides an overview of the book’s organization and key
concepts. The opening segment
(Chapters 1-4) introduces spatial business foundations.
Following these foundations, the book
dives deeper (Chapters 4-7) into achieving business and social
value in four areas (growing
markets and customers, managing the organization, managing
risk, and resilience, corporate
social responsibility). The book then turns (Chapters 8-9) to the
management and strategy
elements aimed toward spatial excellence. The book concludes

(Chapter 10) with a summary of
key themes and a set of implications for practice for each of the
themes. What follows is a brief
preview of key concepts, applications, and company cases that
are examined in the book.
Figure 1. Organization of Spatial Business Book, showing how
each of the ten chapters fit within three sections
(Source: Author)
Spatial Business Fundamentals
Spatial business refers to concepts, techniques, and actions that
enhance the use of
locational insights to achieve business and broader societal
goals. Spatial Business
Fundamentals (Part I) begins by considering the fundamental
principles of locational value and
how understanding location value chains can inform various
business functions such as
marketing, operations, and supply chains. Chapter 1 also
outlines levels of a company’s spatial
maturity as well as the process of gaining maturity. The
Shopping Center Group is provided as
an example of a company with high spatial maturity and
strategic use of location analytics.
These business and locational concepts provide an underpinning
for describing the
Spatial Business Architecture, which is outlined in Chapter 2.
The architecture begins with the
business goals and needs, then addresses business users and
stakeholders who have
responsibilities for addressing these business goals and using
location analytics to do so. The

2
architecture continues with a series of location analytics tools
to be applied to business areas,
tools that depend on various forms of location data. Supporting
all of these functions are the
various platforms that host spatial business processes, such as
the cloud, the enterprise, or
mobile services. The final component is the net consequence in
terms of location intelligence
that can be used to provide business insights, inform decisions,
and have an impact on business
performance. Companies such as Zonda, OverIt, and Walgreens
are described as examples of
effective architectural deployments.
Location Analytics lies at the heart of the Spatial Business
Architecture. Chapter 3
provides a deeper presentation of the use for descriptive,
predictive, and prescriptive analyses.
Descriptive location analytics provide exploratory spatial
analysis of business patterns as well as
visualization of patterns. Predictive location analytics
encompasses spatial statistics to detect
and predict business patterns, clusters, and hotspots.
Prescriptive location analytics are often
the most complex and can include spatial forecasting, space-
time analysis, and GeoAI
(geographic artificial intelligence). Examples of business use of
these analytics are provided
throughout Part II of the book.
Achieving Business and Societal Value

Building on these Spatial Business Fundamentals, Part II
explores the use of spatial
analytics across the business goals, featuring growing markets
and customers operating the
enterprise, and managing risk and resilience. It also considers
the role of spatial business
applications to understand and track a company’s social
responsibility or what has been termed
the “new purpose of the business”.
The role of location intelligence is evolving rapidly as geo-
marketing is leveraged by
organizations to generate deep locational insights about
customers and markets. Chapter 6
analyzes the role of location analytics in market and industry
cluster analysis to identify
business opportunities, determine consumer preferences and
buying patterns with customer
segmentation, scrutinize geotagged social media streams to
examine patterns and relationships
between consumer sentiment and actual sales, and determine
best locations for new facilities.
The chapter also discusses the linkage of location analytics with
the “7 Ps” of marketing. Acorn,
Fresh Direct, Heineken, and Oxxo are provided as examples of
location analytics used for
growing markets and customers.
Effective management of business operations is a highly varied,
process-oriented part of
the organization and its functioning is critical to achieving
business goals. Chapter 7 outlines
how location analytics spans to include situational awareness to
facilities, ensuring business
and service continuity, and achieving efficiencies in supply

chains and logistics. Chapter 8
focuses on risk and resiliency. Using location analytics,
companies can gain a new way to
measure and initiate operational actions action ahead of time,
gaining the advantage of being
proactive in managing risk. With improved visibility via
dashboards, there is the capacity to
quickly adjust to events such as natural disasters and COVID-19
related closures. Cisco, CSX Rail,
and Travelers Insurance are provided as examples of location
analytics using operational and
risk management.
3
Corporate Social Responsibility (CSR) calls for a company to
be socially accountable in
ways that go beyond making a profit. The company takes a
broader view of its goals, thinking
not only of its stockholders, but also of the benefits to its
employees, customers, community,
the environment, and society as a whole. This expansive role of
the business to address social,
racial, economic, health, and educational inequities has been
heightened worldwide by the
COVID-19 pandemic. As corporate leaders navigate their
businesses through increasingly
uncertain business and geopolitical environments in the post-
COVID world and are pressured to
achieve growth, they are also being called to shape their
organizations' role in confronting and
addressing these items. Chapter 7 outlines “shared value”
strategies and actions by companies
to use location analytics to address issues such as climate

change impacts, sustainable supply
chains, UN (2030) sustainable goals, and economic
advancement of underserved communities.
Marx and Spenser, Nespresso, Natura, AT&T, and JP Morgan
are provided as examples of how
location analytics can contribute to these important societal
goals.
Toward Spatial Excellence
A driving theme is that location analytics should not be
considered an isolated GIS
undertaking, but rather an integral analytical function for
creating business success. Given the
importance of management and senior leadership in an
enterprise’s spatial transformation,
Part III details the application of management principles allied
with spatial business strategies
and building the location analytics workforce to accomplish this
transformation. It concludes
with implications for practice that serve as action items for
those engaged in spatial business.
Chapter 8 outlines critical dimensions of spatial leadership
needed to achieve spatial
maturity, where location analytics become intertwined with
business strategies and business
gains. Core activities that are discussed include demonstrating
the value of location analytics to
key business goals, championing spatial initiatives, and
developing the workforce capacity to
achieve these goals. Companies such as CoServ Electric, and
British Petroleum (bp) are provided
as examples of effective spatial management and leadership.
Chapter 9 moves from leadership and management into strategic

and competitive
actions. Geospatial strategic planning is characterized as having
both external and internal
elements. The external element focuses on how location
analytics can be used to strengthen
the firm’s competitive position or modify forces affecting
competition, such as customer
relationships or new products. Internal planning emphasizes
improving the firm’s own
geospatial infrastructure and processes. The internal element
focuses on the alignment with
business needs, technological capacity, and human resource
requirements to achieve desired
location and business value. These strategic actions are
demonstrated by both a large company
example (Kentucky Fried Chicken) and a small company
(RapidSOS) example.
The concluding chapter (10) moves to Implications for Practice
from all that has been
presented in the book. This discussion is centered around 10
themes that can guide spatial
business actions:
1. Identify and Enhance Location Value Chain
2. Enable Spatial Maturity Pathway
4
3. Match Analytical Approach to the Business Needs
4. Build a Spatial Business Architecture

5. Use Market and Customer Intelligence to Drive Business
Growth
6. Measure, Manage, and Monitor the Operation
7. Mitigate the Risk and Drive the Resiliency
8. Enhance Corporate Social Responsibility
9. Inspire Management to Capture Vision and Deliver Impacts
10. Solidify Spatial Leadership for Sustainable Advantage
A set of Implications for Practice are provided as specific steps
that be taken to achieve
an effective Spatial Business strategy and operation that will
contribute to business success in
today’s competitive and complex environment.
We hope you will find the following chapters to be informative
about principles,
concepts, and practices of Spatial Business and will inspire
their use for business and societal
gain. For leaders, it represents an important opportunit y to
leverage location intelligence for
strategic leadership and competitive gain. For analysts, it is an
exciting opportunity to deploy
innovative location technologies and applications that can have
a demonstrable impact. For
students, it is a growing field of study and profession that
complements and widens traditional
business education and professions. For all, spatial business has
elements that broaden the
space of inquiry to consider related societal outcomes,
challenges, and benefits for
communities and the world.

5
Acknowledgments
The publication of Spatial Business represents a product of four
years of work
undertaken by the authors, who each made an equal contribution
to the book. This book is a
part of a Spatial Business Initiative conducted in cooperation
with Esri. The partnership has
been invaluable in providing a forum for investigating trends
and developments in business
location analytics. We are deeply grateful to Jack and Laura
Dangermond for their support of
this initiative. Special thanks are due to Cindy Elliott for her
strong partnership as the
designated lead at Esri as well as her insightful reviews of draft
chapters. Nikki Paripovich Stifle
and Karisa Schroeder provided expert input on several chapters,
especially Chapters 2 and 10.
We are also appreciative of the guidance provided by so many
at Esri Press, especially Catherine
Ortiz, Stacy Krieg, Dave Boyles (in the early stages of the
project), Alycia Tornetta, and Jenefer
Shute. The book has also benefited from our participation in
Harvard Business School’s
Microeconomics of Competition (MOC) network. Several key
concepts in the book (e.g.,
location value chain, cluster mapping, and shared value) were
inspired by materials,
presentations, and collaborations made possible through the
network.

During the book project, various forms of research and outreach
were conducted to
inform the concepts, methods, and cases outlined in the book.
The most intensive of those
efforts was the case study research for which several private
sector leaders in charge of
geospatial strategy and location intelligence provided keen
insights about their respective
organizations. We want to thank Gregg Katz, formerly of The
Shopping Center Group, Ben
Farster of Walgreens, Enrique Ernesto Espinosa Pérez of
OXXO, Kurt Towler of Sulphur Springs
Valley Electric Cooperative, Joe Holubar of Travelers
Insurance, Brian Boulmay, formerly of
British Petroleum, Lawrence Joseph of KFC, Martin Minnoni of
RapidSOS, and Andy Reid of
Zonda. Each of them agreed to be interviewed as part of our
spatial business research, shared
nuanced insights on how location analytics is shaping
competitiveness and strategies in their
respective organizations, and were generous with their
geospatial industry perspectives. Esri's
Cindy Elliott, Helen Thompson, and Bill Meehan were
instrumental in connecting us to several
of these experts, and their roles are gratefully acknowledged.
We convey our thanks to several former and present graduate
students who provided
research assistance at various stages of the project. Anuradha
Diekmann, Lauren Salazar,
Simisoluwa Ogunleye, Jahanzeb Khan, and Burt Minjares
helped to record and transcribe case
study interviews, and to collect relevant secondary information
from various companies,
businesses, academic journals, and business information
databases. They distilled key findings

and corroborated the findings with the research team members
and knowledgeable business
contacts, and also helped us procure permissions for the
project's artwork. In addition to these
talented students, we would also like to thank Kian Nahavandi
in our school for her valuable
administrative support for the book.
Finally, the University of Redlands provided a hospitable
environment for the project.
We are grateful to colleagues at the University for their
steadfast encouragement and support
for this project.
6
SPATIAL BUSINESS: COMPETING AND LEADING WITH
LOCATION ANALYTICS
PART I
7
CHAPTER 1
Fundamentals of Location Value
Introduction
Creating Value
If we begin with the premise that the purpose of a business is to
create value, how do

we identify specific value? Focusing on the private sector, this
value is typically revealed in
products and services that are successful in the marketplace.
Technology companies provide
products that are purchased, real estate companies provide
homes and office buildings that are
purchased or leased, consultants provide advisory services that
are procured. Every sector of
industry, including government and nonprofits, has a range of
specific value that it creates.
From a competitive perspective, this value is framed within the
context of a company’s
unique “value proposition” to its customers. Anderson, Narros,
and Rossum (2006) identified
three types of value proposition: all benefits, comparative
advantage, and resonating focus. An
all-benefits value proposition represents the comprehensive set
of customer benefits a
company provides, while a comparative advantage value
proposition highlights its value relative
to the competition. A resonating value proposition—considered
the gold standard of value
propositions—identifies the key points of difference that will
deliver the most compelling value
to the customer.
The challenge and opportunity of location analytics is to
provide business insight into
how location affects these value propositions, taking into
account a host of geographic,
economic, technological, environmental and societal actors.
Sustainable Value
While many companies rightly focus on their value proposition

to customers, broader
value considerations affect their business activities and
decisions. In the five decades since
economist Milton Friedman famously proclaimed that the sole
responsibility of business is to
make a profit, there has been a growing recognition that the
purpose of a company entails yet
transcends its profit-making capacity. On August 22, 2019, in
recognition of this expanded view
of the role of business in society, the prestigious US Business
Roundtable announced a revised
articulation of the purpose of a business. (Business Roundtable
2019) This broader perspective,
backed by 181 of the top US companies, includes the following
dimensions: delivering value to
customers, investing in employees, dealing fairly and ethically
with suppliers, supporting
communities, embracing sustainable business practices,
generating long-term value for
shareholders, and effective engagement with shareholders. As
Darren Walker, President of the
Ford Foundation observed at the time of the announcement,
“This is tremendous news because
it is more critical than ever that businesses in the 21st century
are focused on generating long-
term value for all stakeholders and addressing the challenges we
face, which will result in
shared prosperity and sustainability for both business and
society” (Business Roundtable,
2019).
8

These developments are often framed within the context of
corporate social
responsibility (CSR), and more recently Environment, Social,
and Governance (ESG) factors.
KPMG has conducted an annual survey since 1993 on global
corporate CSR/ESG activities and
reporting. At the time of the 1993 survey only 12% of the
(N100) top companies in surveyed
companies were reporting on their CSR-ESG activities. As of
2020, this reporting had grown to
85%. Moreover, the growth in the top global corporations
(G250) which they started surveying
in 1995) as gown to 90% (see figure 1.1) (KPMG 2020).
Companies are clearly seeing the
connection between their actions and the surrounding world,
and the need to track and
address societal and environmental factors that could inhibit
their success. For example, that
same 2020 survey found that top global (G250) reporting on the
threat of global climate change
as a financial risk had grown from had grown dramatically for
both groups, with 43% of top
global companies (G250) noting the risk and 53% of top
national companies(N100) noting this
financial risk.

Figure 1.1 Growth in ESG Reporting by Top National and
Global Companies (trends for top national and global companies
are
displayed in dark blue and light blue lines respectively)
(Source: KPMG)
The COVID-19 pandemic has only served to intensify the
interlinkages between
companies and societal conditions. During the pandemic
business have had to radically change
employee work patterns and relationships with customers and do
their part to safeguard to
health and safety of all of those within their business
ecosystem--all this amidst dramatic
economic and employment contractions. It has become clear
that the health and safety of
employees is not only of great consequence when they are “at
work” but depends upon the
conditions of the environments and communities they live in
and travel to.
9
Turning to the focus of this book, it is also the case that
business location analytics has a
role to play in advancing this broad purpose of business in
delivering value to customers,
communities, and the global environment. Such as role can be
best introduced by considering
the spatial decision cycle that enhances business value.
Spatial Decision Cycle
Given these various dimensions of value (ranging from a

product to a societal impacts),
how can one start to spatially think about enhancing such value
through location analytics.? It is
useful to consider a cycle of four elements in a spatial decision
process: value, spatial thinking,
location analytics, and data (see Figure 1.2). The cycle begins
with understanding the value
created by a company’s products and services. It then considers
the spatial dimension of the
value created, followed by the appropriate location analytics
suggested by this spatial thinking.
The cycle then turns to the data requirement for achieving the
desired location-analytics
insights and concludes with the value added by these insights
for business priorities.
Figure 1.2 Spatial Decision Cycle, showing linkages between its
key elements: Value, Spatial Thinking, Location Analytics, and
Data
(Source: Author)
10
Element 1: Value (proposition)
From a strategic perspective, spatial decision-making begins
with business goals to deliver a
company’s “value proposition” through market and customer
growth, achieving competitive
advantage in offerings, driving operational efficiencies, and
managing risk and regulatory
compliance. Taking into account the dimensions outlined by the
Business Roundtable, these

goals can also include upgrading employee skills, ensuring
effective and sustainable supply
chains, supporting local communities, and improving
environmental conditions.
For example, the case of gourmet coffee maker Nespresso
illustrates a company strategy that
embraces these objectives and embraces location analytics as a
means to achieve them. As the
company notes in its Business Principles, their value
proposition is to "promise consumers the
finest coffee in the world that preserves the best of our world"
(Nespresso, 2021). Similar to the
Business Roundtable new statement of purpose, Nespresso notes
that " If we are to be
successful – not only as a business, but in delivering on this
promise – we know we must earn
the trust and respect of our people, our customers, our suppliers
and wider society." As well be
outlined in the Nespresso Case Study (in Chapter 8), a key
aspect to delivering on this promise
is the use of location analytics, to monitor and manage
achieving a variety of business,
environmental and community goals under their "Positive Cup
Framework" (Nespresso, 2021).
Of course, not every company operates in the same context as
Nespresso, but a key value
proposition can usually be discerned, with priorities that set the
stage for spatial thinking.
Element 2: Spatial thinking
This second stage of the cycle focuses on utilizing spatial
thinking to translate business
objectives into spatial considerations. Spatial thinking is
considered a form of intelligence, along

with other forms of intelligence such as logical and
interpersonal (Gardner, 2006). The National
Research Council (2006) noted that there are three components
to spatial thinking: spatial
attributes, representations, and reasoning. In spatial business,
spatial attributes refers to the
ways to measure and assess location dynamics such as in trade
areas, supply-chain
transportation and so forth. Representations include various
means of rendering spatial
dynamics, such as customer cluster maps, business space-time
trendlines, and supply network
visualizations.
Perhaps the most important component is spatial reasoning. In
spatial business, this calls for
constructing a line of inquiry that reveals the influence of
locational factors on business success.
For example, a hospital can examine its supplier network to
determine where in the supply
chain interruptions are occurring. A retail company can examine
trends in sales across different
customer markets to determine where new stores should be
opened because such locales have
a strong presence of desired customer profiles.
A classic example of strategic spatial reasoning is the case of
the investment company Edward
Jones. Edward Jones started as a “small-town” investment firm
in Missouri. The company
viewed its comparative value proposition as providing a single
investment service to more rural
11

communities, compared to Merrill Lynch, which provided full -
service portfolios in large
metropolitan areas (Collins & Rukstad, 2008). In the early
1980s, Edward Jones conducted a
series of analyses and consultations and discovered that its
resonating value proposition was
that it offered a highly personalized investment service to those
individual customers who
wanted to delegate investment decisions. It further discovered
that it could competitively offer
these services in select rural and select metropolitan locales
where such customer profiles were
strongly represented. The company then proceeded to
operationalize the new market mix. This
spatial reasoning resulted in the rapid growth of Edward Jones
from 400 to 1,000 locations in a
seven-year period and remains its driving focus today (Edward
Jones, 2020).
Element 3: Location analytics
Clear spatial thinking drives the choice of location analytics. If
a business is mostly interested in
a general understanding of spatial trends in customers, assets,
suppliers, and so forth, then
descriptive analysis can provide situational awareness through
maps and infographics. If a
business desires to carry the spatial analysis further, to
understand how spatial insights can
help achieve business value priorities, then explanatory analysis
can be conducted to help
explain dynamics such as why growth did or did not occur, or
why certain sites or locations
were or were not successful. If a business wants a predictive
analysis of the likely success of a

service, product, or location, it can conduct predictive spatial
analysis. And if a business wants
to know where to locate new locations or serve new markets, it
can conduct prescriptive
analysis. These spatial analysis approaches are reviewed in
detail in Chapter 3.
Many industries are advancing their analytical capacities to
move from descriptive to predictive
and prescriptive analytics. As one example, the insurance
industry is rapidly evolving to adjust
to the more extreme climate conditions brought on by climate
change and other socio-
demographic and economic changes. Companies such as
Travelers Insurance now employ a full
range of location analytics to assist a range of business-critical
functions (Travelers Insurance,
2021). These include predicting the location of natural disasters
(for underwriting purposes),
analyzing damage locations (for claim purposes), and
identifying high priority locational impacts
(for disaster response). These tools have been used with great
success for recent hurricanes on
the east coast and wildfires on the west coast (Claims Journal,
2019).
Element 4: Data
The fourth element in the spatial decision cycle is data. As the
expression goes, “You are only as
good as your data.” A business may have a driving need to use
location analytics to enhance its
success but will be hampered without the appropriate data.
Typical data types include sales,
profit, customer, cost, asset, and network data. In addition to
this proprietary data, numerous

governmental and commercial datasets can inform location
analysis—for example, trade-area
analysis, business transactions, supply chain network data and
social, economic and
environmental trends. Companies are leaning into digital
transformation and, as part of that,
aligning their various business intelligence enterprises, which
includes enhanced
interoperability of these data sources.
12
In addition to the need for “location stamped” data, three other
data issues that deserve
attention are the level of geographic specificity, the availability
and consistency of data over
time, and the policies surrounding data use. Regarding the first,
the greater the level of
granularity the better the analysis, although many publicly
available data sets will limit the
granularity for reasons of privacy and anonymity. Regarding the
second, temporal data is
critical for looking at spatial changes over time, such as the
growth or decline of customers,
sales, inventory, etc. And third, various policies can affect the
use of data within a company or
its ability to share the results of the data. For some industries
such as healthcare, these privacy
conditions are well established (e.g. HIPAA), while for other
industries such as retail there are
emerging privacy issues around location-based services.
Element 5: Value (added)

The cycle concludes with the consequence of location analysis
in contributing to business
success. This may include value added to business priorities
such as driving growth, improving
operations, managing risk, and ensuring regulatory compliance.
To the extent possible, this
contribution should be documented in terms of the type and
amount of value contributed and
the stakeholders who received this value. In determining this
value, there are a number of
dimensions to consider: depth, breadth, use, internal
stakeholders, external stakeholders, and
financial contribution, as summarized in Table 1.1.
Beginning with depth, this value type refers to the value
delivered to a specific business
function such as marketing or operations (discussed below).
Breadth refers to the value
delivered across business functions, as the organization
increases its spatial maturity. Then,
there are different use values. This can include the value that
location analytics has in informing
stakeholders (e.g. situational awareness), decision-making,
contributing to business goals, and,
ultimately, contributing to the business mission.
Like beauty, value is the eye of the beholder. There are internal
stakeholders who perceive
value, ranging from employees carrying out specific
organizational functions to the ladder of
middle, senior, and executive managers and leaders. There are
external stakeholders who
perceive value, including customers, partners, suppliers,
distributors, and the public. Finally,
there is the traditional estimation of value, often framed within
the context of “return on

investment.” This can be in the form of a formal/quantitative
return on investment or a more
qualitive summarization of the value elements noted above. The
former can be particularly
appropriate when the costs and benefits can be easily parsed.
Most importantly, this is but one iteration of the spatial
decision cycle. The cycle should be
considered ongoing and integrated into decisions regarding key
business priorities. A case
example of this tight integration is The Shopping Center Group,
which we consider next.
Case Example: The Shopping Center Group
The Shopping Center Group (TSCG) is a leading national retai l-
only real estate service
provider in the United States. It has 20 offices in the United
States, 215 team members, and 28
GIS specialists (known as “mappers”). Over the last decade the
company has come to tightly
13
integrate the use of business-focused location analytics that
deliver business value to its
customers and its organization (The Shopping Center Group,
2021)
TSCG has four main service lines: tenant representations,
project leasing, retail property
sales, and property management of those retail properties. As
TSG's Chief Strategy Officer Greg
Katz noted, “At the core of everything is GIS research. We
consider GIS research to be the

heartbeat of the organization. It allows all four of those service
lines to tell a story” (ArcWatch,
2017). Each of the four service lines engages in ongoing spatial
decision cycles, i.e.: What is the
property in question? What are the business objectives for the
property? What locational
analytics will inform decisions about the property? What data
can be applied to this analysis?
What recommendations come out of this cycle of analysis?
As such, it is clear that location analytics is providing
considerable value to the TSSG.
Table 1.1 provides a summary of this value along the
dimensions described above. Beginning
with value to key business functions, location analytics provides
value to the company’s
marketing and sales support. This features deep spatial insight
into consumer and trade-area
markets. For example, an analysis conducted on behalf of
Columbus Mall in Georgia combined
trade-area, drive-time, GPS, and psychographic analyses to
pinpoint key market considerations
to guide the selection of potential tenants for that commercial
property.
Table 1.1 Value of Location Analytics for The Shopping Center
Group (TSCG), with each
row showing different dimensions of value and how they are
manifest at TSCG
Value Domain The Shopping Center Group
Depth

Value Within Marketing and
Sales Support
Deep spatial insight on relative consumer
and trade-area markets for commercial
properties
Breadth Value Along Key Business
Priorities
Drives overall value proposition as an
information-focused technology enabled
commercial real estate company.
Success of brokers and growth of
company
Use
Inform, Decide, Grow, Avoid Used to inform brokers, decide on
commercial selections, and avoid
mismatches between commercial
property types and surrounding markets
Who:
Internal
Internal (Analysts, Managers,
Sales, Operations, C-suite)
1:4 “mapper” to broker ratio
C-suite vetting and management of
commercial properties

14
Who:
External
Clients and Partners
Multilayer commercial real estate maps
for clients and partners
Results|ROI
Direct input to competitive
advantage and growth
Key contributing component to 30%
growth of company, and mission as an
analytics-focused commercial real estate
company
As Table 1.1 shows, location analytics is part of the overall
TSSG value proposition as an
information-focused, technology-enabled commercial real estate
company, and it spans a wide
range of value types. It informs brokers and partners, it aids in
their decision-making process,
and it helps the company grow while avoiding costly market

assumption errors in retail
commercial transactions.
These location analytics insights and products are utilized by a
wide range of
stakeholders. Internally, brokers and other analysts have ready
access to the 28 “mappers” who
fuel the analysis. This utilization rises to the C-suite level,
where every major deal is required to
have a location analytics review as part of the vetting process.
Given the highly integrated
nature of location analytics into the TSSG mission, processes,
and product lines, its ROI value is
considered within the context of overall corporate success. In
this case, company executives
consider it to be a key contributor to TSSG’s 30 percent growth
and its emergence as a
commercial retail and information company.
Location Value Chain
The use of location analysis across business dimensions can be
considered the “location
value chain.” The concept is a variant of Porter’s seminal
“value chain,” which outlines the
various business processes that combine to create value in terms
of products and services
delivered (Porter, 1998). A business’s location value chain
captures those business functions
that benefit from location analytics and thus contribute to the
overall value of the company.
Depending on the business value being pursued, location
analytics can be deployed
across a range of business functions (see figure 1.3).

15
Figure 1.3 Location Value Chain, comprised of business
functions and business needs within each function that prompt
the
use of location analytics
(Source: Author)
Request this Figure to be re-drawn.
In term of basic business functions, this covers the following:
product development, new
market development, acquisition due diligence, and location
siting.
segmentation, customer retention.
-out,
mergers and acquisitions,
sales growth.
management.
-area analysis, competitive
analysis, facilities layout.
analysis, tracking, and simulation.

recovery, and resiliency
social equity, community
impacts, and shared value creation.
A variety of studies have documented the range of location
analytics use across this
value chain. In 2018, the University of Redlands conducted a
survey of 200 businesses that had
at least initial adoption of location technology to determine
patterns of location analytics use.
(University of Redlands, 2018.) The survey found that an
overwhelming majority (86%) of
16
surveyed businesses report moderate to high use in more than
one function (Figure 1.4).
Overall, 51% of businesses use GIS in 1–3 functions, 35% use
GIS in 4–6 functions, and the
remaining 14% use GIS in 7–9 functions. Figure 1.4 shows
levels of use 9 major business
functions, with GIS usage highest for R&D (58%)., followed by
operations (50%), services (48%),
IT (48%), sales and business development (47%), and marketing
(43%).
Figure 1.4 High and Moderate Use of Location Analytics in
Different Business Functions along the Location Value Chain
(High

Use in Maroon and Moderate use in Blue)
(Source: Author)
In terms of the overarching business motivations for using
spatial analysis, 46% of the
survey respondents reported moderate to high motivation for
improving the competitive
posture of the business. This was followed by GIS use to
optimize business performance (39%),
for effective risk and disaster management (31%), and finally
for regulatory compliance (28%).
Looking more closely at customer-centric activities, GIS use is
highest for analysis of
spatial patterns of customers (46% indicate moderate to high
use), yet lowest for tracking and
measuring sales activities (29%), pointing to a gap in GIS use
for these purposes. In the middle
are GIS use for customizing marketing strategies (38%),
predicting future customer trends
(36%), and optimizing sales territories (31%). With the
exception of tracking and measuring
sales, GIS use for customer and sales activities seems to dec line
as the purpose of deriving
location intelligence shifts from descriptive to predictive to
prescriptive in nature.
Turning to operational activities, GIS use is highest among the
following activities: space
and location decisions (58% indicate moderate to high use),
spatial field data collection (56%),
tracking and managing asset allocations (43%), predicting
future operational needs (36%), and
managing logistics and supply chains (20%). As in customer and
sales activities, moderate GIS

use surpasses high use for operational activities.
17
Pandemic Influences on Location Value Chain
The COVID-19 pandemic of 2020 profoundly influenced the
location value chain of many
businesses. With the disruption of operations, location analytics
played an important role in
assessing the challenges to business continuity, which varied by
location. Business had to move
quickly to close, modify or continue with operations, depending
on a variety of federal, state,
and local conditions. The pandemic also had major impacts on
business supply chains, most
visibly on the healthcare supply chain, where the crisis exposed
risks associated with the global,
just-in-time systems that had come to dominate the medical
device and supplies industry. In
addition, businesses needed to have locational information on
employees to ensure their safety
and to take appropriate action if they or their coworkers were
exposed to infection.
As businesses are emerging into a post-pandemic era, new value
propositions are
emerging that will affect the location value chain of businesses.
Within the retail sphere, online-
on-ground hybrid models are being extended. McKinsey & Co
(2020) reported that many
customers have also tried new omnichannel models. For
example, buy online, pick up in store
(BOPIS) grew 28 percent year- over-year in February,2021 and

grocery delivery was up by 57
percent. They further note that many of these new engagement
models are here to stay.
Consumers report high intention to continue using models such
as BOPIS (56 percent) and
grocery delivery (45 percent) after the pandemic. As these
business models evolve, it will create
new opportunities to integrate location analytics into the new -
normal of business operations.
Drivers of Spatial Maturity
Spatial maturity involves the deepening of location analytics
use across the locational
value chain. Looking at the range of use, the University of
Redlands survey estimated that
roughly 1 out of 5 businesses (22%) use GIS enterprise-wide,
spanning multiple departments. At
the other end of the spectrum, roughly 1 out of 5 businesses
(20%) report GIS usage to be very
limited. In the middle, 28% of businesses report their GIS usage
to be currently limited but
poised to grow soon, while another 25% indicate GIS usage to
be moderate and steady. Overall,
it is clear that business use of GIS has the potential to grow in
the near term. However, charting
a pathway for spatial business transformation is essential.
In determining the factors that contributed to achieving spatial
maturity, the survey
found five that played an influential role. The more advanced,
spatially mature companies had:
1) perceived the value of location analytics; 2) a clear and
coherent business strategy; 3) C-suite
sponsorship and support; 4) availability of best-in-class
technology; and 5) clear articulation of

Return on Investment (ROI).
Turning to inhibitors of spatial maturity, IDC/Esri Canada
identified several factors as
“challenges” to achieving deeper spatial maturity. Factors such
as cost, culture, appropriate skill
set, integration challenges, and data quality issues were
identified as inhibitors that could
impede the growth and use of location analytics in companies.
Various forecasting reports provide a generally bull ish outlook
on the future use of
location analytics. This is due to at least these eight drivers of
location analytics use:
18
1. Growing geospatial ecosystem
2. Deepening use across a range of industry applications and
verticals
3. Increasing availability of spatial analytics tools
4. Widening range of location services
5. Integration of location analytics with business intelligence
6. Growing indoor locational analytics
7. Rise of associated advanced technologies
8. Rise in global environmental, societal, and health challenges

Driver 1: Geospatial ecosystem
Location analytics is part of a larger geospatial ecosystem that
includes Global Navigation
Satellite Systems (GNSS), GIS/Spatial Analytics, Earth
Observation, Light Detection and Ranging
(lidar), Space-Time Visualization, Augmented/Virtual Reality
(AR/VR), and Artificial Intelligence
(AI). The global geospatial solutions market is projected to
reach USD 502.6 billion by 2024 from
an estimated USD $239.1 billion in 2019, at a CAGR of 13.2%
during the forecast period
(Markets and Markets, 2019). The cornerstone of this industry
is GNSS, which provides the
technological backbone for the industry and accounts for
approximately 59% of the total value.
This has fueled the growth of GIS/spatial analytics to be the
second largest segment of the
industry, with growth expected to double by 2022 (GeoBiz
2019).
Driver 2: Industry use
Consistent with the Redlands survey noted above, other industry
outlooks have documented
deepening use across a range of industries. For example,
Dresner Advisory Location Analytics
Survey (2020) found that location analytics was viewed as
critical or very important across a
range of vertical industries. Ninety-three percent (93%) of
survey respondents viewed location
analytics as having some importance to their organization, and
over 53% noted it was critically
or very important to their organizations. In terms of specific
vertical markets, the survey found
this to be especially true for health care, business services,

financial services, consumer
services, and manufacturing. Each of these sectors viewed
locational analytics as critical or very
important to their organizations.
Driver 3: Spatial analytics tools
Spatial analytics tools continue to broaden in their areas of
application and deepen in their
capabilities. In terms of broad solution sets, geocoding (and
reverse geocoding), reporting and
visualization, thematic mapping and analysis, and data
integration/ETL are each expected to
grow substantially between now and 2024 (Markets and
Markets, 2019). The ability to
19
effectively geocode data is particularly helpful in making a
range of industry data available for
location analytics, such as customer, business, product, and
supply chain data. Thematic
mapping and analysis growth will continue with the availability
of increasingly sophisticated
analytics. Reporting and visualization tend to increase demand
both within businesses and from
their external stakeholders. As the volume and value of data
continue to growth, there is a
strong need for integration across different systems, including
business intelligence (BI)
systems.
Driver 4: Business intelligence integration

Further fueling this growth is the increasing appetite for the
business insights provided by
location analytics. Dresner Advisory (2020) reports that
Executive Management and Operations
are expected to experience the highest growth for BI penetration
(which includes location
analytics) over the next three years, and that “better decision-
making” is the primary objective
of BI use, followed by related key business areas such as (in
descending order) growth in
revenues, operational efficiencies, increased competitive
advantage, enhanced customer
service, and risk management. Increased integration into BI
software suites and reports
provides a natural path for location analytics to contribute to
business growth and
competitiveness.
Driver 5: Location services
The rise of location-based services (LBS) has provided
unprecedented opportunities to
customize offerings and customer experiences. The related rise
of Real-Time Location Systems
(RTLS) also provides unprecedented opportunities to track
assets, personnel, and products. As
an industry, the LBS/RTLS services is expected to grow rapidly
(CAGR of 20.1%) to become a $40
billion market by 2024 (Markets and Markets, 2019). GPS-
enabled mobile devices have spurred
an entirely new dimension to retail marketing and customer
services.
Driver 6: Indoor location analytics
Related to the growth of LBS and RTLS, indoor location

analytics is growing rapidly as industries
begin to appreciate its value, especially in operational
efficiencies and risk management. For
example, healthcare is considered a prime use of RTLS, and
RTLS use in this sector is expected
to grow by CAGR 18% to a $6.84 billion market by 2027.
Looking across industries, the COVID-
19 pandemic has heightened the need to track and monitor
personnel locations for health,
safety, and other risk management measures. LBS/RTLS is also
disrupting traditional
distribution center workflows and processes, as evidenced by
the innovative distribution center
techniques deployed by such retail giants as Amazon, Target,
and Walmart. As the penetration
of the Internet of Things (IoT) and other location-based
technologies deepens, new applications
will emerge. At the same time, privacy and security threats will
condition the extent to which
such solutions are deployed, and this constraint could vary
widely across regions and cultures.
20
Driver 7: Advanced technologies
A range of advanced technologies will provide numerous
opportunities to extend and deepen
the use of location analytics in business and contribute to the
ongoing digital transformation of
business. The IoT has already led to a pronounced rise in indoor
GIS, particularly in the retail

sector. The recent COVID-19 pandemic has heightened the need
for and use of georeferenced
IoT devices to track supply chains, analyze human travel
patterns, and monitor health
conditions. Advances in AI are enabling machine and deep
learning across a range of business
domains, such as analyzing and predicting customer buying
patterns, operational
improvements, and threats to business continuity. These and
other AI applications comprise
what is known as “GeoAI”. Of course, IoT, AI, and related
technological advances would not be
feasible without continued advances in Big Data platforms and
applications. Specific to
locational analytics, the geospatial industry is moving to highly
cloud-based and “Web-GIS”
platforms with integration to big datasets and location analytics
applications.
Driver 8: Global environmental, societal, and health challenges
The eighth driver is the changing environmental, health, and
societal context under which
businesses operate. In terms of the environment, the private
sector is increasingly treating
climate change as a contextual condition that can have a
significant impact on business success.
This impact is across the location value chain, affecting
companies' ability to source sustainable
suppliers and retain resilient supply chains through increasing
volatile climate conditions.
Societal issues range from racial equity, income disparities,
broadband access and other factors
that can affect a company’s performance and success in
different communities and region. The
COVID-19 pandemic has raised awareness of the massive

impact that such an outbreak can
have on all aspects of the economy, and the need build
resiliency into supply chains and
operations.
At the macro level, Porter and Kramer have emphasized the
concept of “creating shared
value—pursuing financial success in a way that also yields
societal benefits” (Porter and
Kramer, 2016). They note: “Collective impact is based on the
idea that social problems arise
from and persist because of a complex combination of actions
and omissions by players in all
sectors—and therefore can be solved only by the coordinated
efforts of those players, from
businesses to government agencies, charitable organizations,
and members of affected
populations.” There are many examples of such shared value
initiatives which rely on locational
information. Examples include locationally targeted
partnerships for economic development,
training suppliers on sustainable practices relative to their local
community, and public-private
collaborations on relief during the COVD-19 pandemic.
Global Location Analytics Outlook
These eight drivers, as well as other influences, are expected to
result in considerable
growth in location analytics across the globe. Location analytics
as an industry is expected to
rise from $10.6 billion (2019) to $22.8 billion (2024),
representing 16.6% CAGR (Markets and
21

Markets 2019). This growth is expected to be worldwide.
Currently, the major regional markets
are North America (34.8%), Europe (28%), and Asia Pacific
(20.4%). Looking to 2024, these will
continue to be major markets with the largest growth (17.1%
CAGR) expected in the Asia Pacific
region. The Middle East and Latin America are expected to
remain smaller regional markets,
though each region is expected to have noteworthy growth
(Markets and Markets 2019).
Looking more closely at the types of business use that will
provide this growth (Figure
1.5), the strongest growth is projected in supply chain planning
and optimization (17.3%), sales
and marketing optimization (17.1%), customer experience
management (16.7%), remote
monitoring (16.3%), and emergency response management
(16.3%).
Figure 1.5 Global Outlook for Location Analytics across
different business functions, with the base year (2017) in light
blue
and the forecasted year (2024) in dark blue
(Source: Sreedhar and Bhatnagar, 2019)
In summary, the “Location Value” foundation outlined in this
chapter serves as an
organizing set of concepts, principles, and examples for
understanding the business location

value of any given company, a value that includes
organizational success within a societal
context. As organizations broaden and deepen their use of
location analytics to achieve
business priorities and goals, location analytics can become
more integral to a company’s
mission. Various market forecasts suggest that such deepening
use will indeed be the case
around the globe and across a wide range of industries and
business functions. This growth, in
turn, contributes to and benefits from the need to integrate
across business intelligence
systems, geospatial platforms, and various new technological
systems and products as they
arise. These technology issues are taken up next in terms of a
“Spatial Business Architecture.”
22
CHAPTER 2
Fundamentals of Spatial Technology
Introduction
Achieving location intelligence depends on a process that
delivers valuable insights to
business users. Though other technologies may specialize in
informing “who,” “what,” and
“why,” location intelligence makes information actionable by
adding in the element of “where.”
Organizations increasingly focus on this location intelligence to
drive strategic decisions on all

levels of the enterprise.
This chapter aims to define the different components of the
technological backbone of
location analytics by outlining the six essential elements of a
“Spatial Business Architecture” --
Business Goals and Needs, Human Talent, Location Analytics,
Data, Platforms, and Location
Intelligence (see Figure 2.1).
23
Figure 2.1 The components of spatial business architecture are
shown, including business goals, human talent, location
analytics, data, platforms, and location intelligence
(Source: Author)
The architecture begins with the business goals and needs, then
the human talent
needed to accomplish these business goals. The architecture
continues with a series of location
analytics tools and the data upon which the analysis is
performed. Underlying all of these
functions are the various platforms that host spatial business
processes, such as the cloud, the
enterprise, or portals. The final component is the net
consequence in terms of location
intelligence that provides insights, informs decisions, and
impacts business performance. The
following sections describes each of these Spatial Business
Architecture elements and the

examples are taken from case studies throughout the book.
Element 1: Business Goals and Needs
Location analytics are performed to enhance business value, in
terms of business goals and
needs associated with achieving this value. Business goals that
location analytics can contribute
to are diverse, and they can be focused anywhere across the
location value chain. The
architecture highlights three goals that are generally central to
any business: drive sustainable
growth, strengthen operational effectiveness, and enhance
business resilience.
There are many factors associated with sustainable business
growth: having valued products
and services, attracting and retaining customers, operating as a
socially and environmental
responsible manner to name a few. The appropriate location
analytics tools can contribute to
business and broader goals. For example, John Deere is a leader
in location-based precision
farming that is responsive to climate changes and enables more
sustainable farming practices.
A second goal is to strengthen operational effectiveness. There
are several factors associated
with achieving this goal, such as operational performance,
supply chain management, and
logistics. For example, Cisco uses a customer location
dashboard to ensure timely service
support.
A third goal is to enhance business resilience. Factors
associated with this central goal include

risk management, risk recovery, and risk reliance. This can be
seen in the locational risk and
response algorithms developed by Travelers Insurance, to
predict hurricane directions and risk,
and by Mid-South Synergies tree analysis to protect against
power outages due to fallen trees.
Element 2: Human Talent
The success of spatial business depends on having the human
talent to accomplish the strategic
and tactical actions needed to achieve business gains.
Employees and stakeholders act in
varying capacities at each stage of the spatial business cycle. To
identify business needs, a
variety of people contribute -- business internal users of
location-based systems, executives,
managers, spatial analysts and technical specialists. Trained
developers design and build
location analytics, consulting with managers. Location-driven
decision making is done by
24
managers, senior executives, and business unit managers. Staff
at all levels may use the
applications, such as in sales, operations management, and
service data collection.
This list is not exhaustive. The point is that the successful
execution of a spatial business
strategy depends on people at varied levels in the organizational
hierarchy, with different skill
sets, and disparate levels of technical and business knowledge.

Walgreens is an example of
how technical and business unit managers collaborated to
swiftly roll out Covid-19 pandemic
testing application as well advance their overall spatial
technology platform.
Element 3: Location Analytics and Applications
Location analytics may be divided into descriptive, predictive,
and prescriptive tools.
Descriptive analytics describes locational phenomena. For
instance, a map of electric car
charging locations with car capacity and charging intensity is
descriptive. Predictive location
analytics predicts future business phenomena based on
forecasting of geo-referenced data and
space-time prediction techniques. An example would be to
predict in one year’s time the
locations of customers placing e-commerce orders s based on
trend analysis or geographically-
weighted statistical regression models. Prescriptive location
analytics seeks the provide optimal
locational and network arrangements to achieve a business
objective. An example UPS has
developed a routing tool for optimized routing of deliveries that
also produces considerable
fuel savings.
Underlying location analytics are algorithmic techniques such
as overlay, buffers, drive-time
analysis and sophisticated methods such as visualization,
decision trees, text analysis, data
mining, spatial cluster methodologies, spatial statistics,
location-allocation modeling, network
analysis, artificial intelligence, and machine learning.

Spatial Visualization and Hotspots
At a basic level, mapping is a form of visualization that
simplifies understanding of
geographic differences. For instance, the thematic map of the
density of Airbnb properties in
New York City (Fig 2.2) visualizes the geography of the city
and the density levels of Airbnb
properties indicating the highest levels in lower Manhattan and
north sections of Brooklyn. This
visual utilizes geography, colors, labeling, and scale to create
impact and tell a story that the
equivalent table of data would not do easily.
25
Figure 2.2 A thematic map exhibits the zip code areas of New
York City by categories of Airbnb property densities, with the
highest densities in lower Manhattan and central Brooklyn
(Source: Sarkar, Koohikamali, and Pick, 2019)

Visualization offers a way to simplify massive data and its
processed outputs to highlight
the important overall outcomes and bring out important details
which may have been
overlooked in a tabular format. Visualization can help users to
identify patterns, such as
density. For instance, perhaps an organization's marketing team
is putting together an
advertising campaign targeted at tourists visiting the Manhattan
area. Rather than canvas the
entire metropolitan area, the marketing team can add value to
the organization by spatially
thinking about the solution. In running location analytics, the
team can discover density levels
of rental properties within Manhattan by visualizing location-
enabled data. In reviewing the
map in Figure 2.2, it is not difficult to see the most effective
place to enact the advertising
campaign.
Cluster and hotspot analytics are most advanced form of pattern
detection. For
example, cluster mapping can be done on industry locations to
identify geographic
concentrations of specific industry clusters, such a medical
device industry cluster. Hot spot
analysis can be done to track geographically concentrated
events, such as has been done
extensively during the Covid pandemic.
Indoor Analytics
Indoor Analytics is a rapidly growing form of location
analytics. The segment is estimated to
grow from $3.9 billion in 2019 to $8.4 billion by 2024, at a
CAGR of 16.5% (Sreedhar &

26
Bhatnagar, 2019). Indeed, maximizing the value of the indoor
built environment is increasingly
a strategic differentiator for many businesses. Descriptive
location analytics can track the
movement of people, goods, and assets. Prescriptive location
analytics and be applied to
improve productivity and throughput of an indoor space, and to
provide navigation and routing
services, saving time, effort, and money.
Consider the challenges inside a large warehouse to understand
the spatial location of
inventory, locate the movement of workers, optimize the
movement of pallets of goods,
coordinate arrival and departure of trucks, and adhere to safety
regulations and social
distancing from covid-19. This indoor environment can be
managed by a cloud-driven
warehouse spatial intelligence system (WSI) (Zlatanova and
Isikdag, 2017). Data is input from a
combination of precise GIS 3D location of assets and people,
RFID tags on inventory, and high
definition image and video feeds. The data goes beyond simple
RFID-tagging of inventory to
identify the space-time dynamic movement of vehicles, people,
and inventory (Pavate,
2021). Such a system allows for numerous location analyses.
For instance, the location of
warehouse inventory by product types can be studied by cluster
analysis; visualization can be
applied to understand complex spatial arrangements; and

machine learning can aid in guiding
the movement of warehouse robots.
StoryMaps
Organizations looking to tell a data-driven story often look to
maps to communicate
with location intelligence. Adding additional content alongside
a map can help to strengthen a
map’s persuasive storytelling. A StoryMap helps to
communicate a story by creating an
interactive experience that features maps, text, images, and
videos. Functioning like a
templated website, a StoryMap includes adaptable widgets that
enable a user to quickly build
an information tool without having to learn code.
StoryMaps have become quite popular, with over 450,000
published (Semprebon,
2021). The interactive capabilities of StoryMaps allow users to
interact and engage with
location information, unlocking possibilities otherwise
impossible with static maps, tables, or
charts. StoryMaps can be used internally for idea sharing, proof
of concept, or financial
reporting. Externally, StoryMaps can be branded and used in
replacement of time consuming
web landing pages. Users can even incorporate forms or survey
links to aid in the storytelling
engagement and continue to collect data into the organization.
One example is a StoryMap in the area of corporate social
responsibility. Countries from
around the world have committed to meeting the 17
environmental sustainability development
goals (SDGs), many of which have implications for industry

actions. The country of Ireland has
published online a StoryMap to explain progress in 2011-2018
toward important goals of
reducing poverty (SDG 1) and achieving decent work and
economic growth (SDG8).
(Government of Ireland, 2018). One page of the StoryMap
shows unemployment in the Irish
statistical regions in 2018. (see figure 2.3) The StoryMap
narratives provides this and other
mapping insights on the dynamics of changing unemployment
over the seven years in
relationship to the two UN SDGs.
27
Figure 2.3 A map image from a storymap of unemployment
patterns in Ireland displays regional patterns of unemployment
across the nation’s regions
(Source: Government of Ireland, 2018)
GEO-AI
The rapid collection speed of big data has forced rapid
adoption of artificial intelligence (AI)
and advanced analytical modeling. As the data has evolved, so
have the tools needed to
manage it. Big, unstructured, fast-moving data from a variety of
sources have necessitated
business investment in advanced analytics. GeoAI, or
geographic artificial intelligence, is an
advanced form of location analytics designed to provide
intelligence at scale. GeoAI may be
streamlined from many structured and unstructured data
sources. It can be used to identify

real time location-specific patterns, predict likely outcomes, and
provide statistical projections.
Such data analysis may be used to predict fluctuations in
population, places, or environment,
and may be applied as a means of “knowledge work”. The
integration and embedding of AI with
GIS technology has accelerated the pace of making predictions
and business decisions at scale.
Companies are increasingly capturing new insights on their
consumers which can
provide deep intelligence on the lifestyle and preferences of
various audience segments. When
combining location-specific customer data, including web
engagement, buying history, or
common movement patterns, with news, social feeds, and
current events, an organization can
begin to define the unique attributes of their consumers – where
do they go, what do they buy,
and what influences them. This insight about a customer’s
mindset and behavior, can help an
organization to identify other customers with similar behaviors.
When modeled through GeoAI,
consumer behavior can be used to identify new market
opportunities. GeoAI aids in the
28
discovery of loyal customer patterns. It has related uses for
financial AI services. For example,
Visa’s AI algorithm is used by used to detect unusual charges
based on customer purchasing
and geo-patterns. Visa estimates that it has stopped/saved $25
billion in fraud charges annually

as a result of the model (Nelson, 2021).
Digital Twins
Digital Twins may also be used to predict space and time
occurrences within a virtual
replicated environment. A Digital Twin (Grieves and Vickers,
2017) is a 3D virtual replica of
physical assets, processes, or systems that bridges the gaps in
both space and time between the
physical and digital worlds. The lessons learned, issues
observed, and opportunities uncovered
within the virtual environment can be applied to the physical
world reducing expenses, time,
minimizing disruptions and failures, and most importantly
lowering harm to its users (Marr,
2017). In construction, spatial integrations technologies have
elevated the electronic blueprint
from 2D to 3D. Digital Twin environments take real world
environments and create a digital
world that can be used to plan and monitor a project over time.
Digital Twins can be used as a
test environment to add different elements to a structure plan.
As data comes alive within the
Digital Twin environment, analysts can review different
scenarios such as the placement of
trees to avoid heat islands, time and place of heavy traffic, or
interior design test and
manipulation.
In a manufacturing setting, Digital Twin approaches are being
used to inform product
design and integrate it with actual factory environments to
optimize productions. Digital Twins
enhanced by location analytics can optimize warehouse
management systems by providing
decision support and comprehensive outcome analytics on

workflows, energy and resource
utilization efficiency, and overall plant management.
Interactions between different parts of a
dynamic production environment are visualized and analyzed
using Digital Twins and location
analytics and fed back to the design process of products (Lim,
Zheng, and Chen, 2019).
Altogether, by fusing Digital Twins with GIS in innovative
ways, businesses can create dynamic
feedback loops in all stages between product and service design.
Figure 2.4 provides an
illustration of its use in telecommunications, as it allows for a
digital twin virtualization of
proposed 5G and fiber location. The upper left image is an
example of an actual site, the other
images are digital twin that can be analyzed to determine proper
cell tower locations (Esri,
2019).
29
Figure 2.4 Digital Twins for Telecommunications, in which the
upper left photo shows a digital model of a proposed cell 5G
cell tower, the right photo is a digital display of the fiber line it
would run through and the bottom left is a visualization of
the internet service provide (ISP) center that it would enter
(Source: Esri, 2021d)
Element 4: Data for Spatial Business
A driving force in the growth of location analytics is the rise of
big data. It is estimated that 181
zettabytes of data/information will be created, captured, copied,

and consumed in 2025, at an
annual growth rate from 2020 of 17% (Statista, 2021). A
zettabyte is equal to a trillion
gigabytes. Most of this data is either already geo-referenced or
potentially able to be so.
Businesses are increasingl y making use of this big data. For
spatial business, this enormous
stream of data offers opportunity to obtain value and
competitive strength. Location analytics
can utilize data to describe, predict, and prescribe from this data
using techniques that include
visualization, data mining, clustering, network analysis, text
analysis, machine learning, AI, and
deep learning.
Location data is pervasive and growing in size and type. The
Spatial Business Architecture
includes all forms of customer, demograp hic, business,
financial, social, and environmental
data. Table 2.1 provide a summary of primary data types used
to conduct location analytics for
business. The sources of these data are next discussed.
30
Table 2.1 Summary of Spatial Business Data
Type Dimensions Locational Use
Customer
Psychographics

Lifestyle
Preferences
Satisfaction
Customer data includes attributes on lifestyle, brand
preferences, and spending habits, informing marketing, sales,
and customer growth and retention
Points of Interest
POI
POIs
Places
Business POIs include business and supplier locations,
providing locational information for business planning, and
operations and supply chain management.
Movement
Human
Cargo
Networks
Movement data looks at the physical movement of people and
cargo from place to place, facilitating business location services
and resilient supply chains.

Community
Demographic
Community
Demographic, community data can inform business community
strategies and impacts in areas such as social and racial equity.
Imagery and
Remote Sensing
Aerial -
Satellite, InSAR-
Street Level
Remote Sensing
Provides intelligence showing emergency situations in business
facilities and locations, movements of assets, movement of
supply chain materials, and understanding the geography and
players in markets.
Environment
Climate Change
Air Quality
Land Use

Environment data provides authorities indicators key
environmental conditions and can inform. Corporate social
responsibility actions.
Business Data - Enterprise
Many companies already have a vast network of geographically
enabled data available
internally for location analytics and discovery. A cornerstone of
the business data is customer
data. When customer data is analyzed by location, new
opportunities are presented, indicating
31
by analysis exactly where behaviors are occurring and when.
For example, it may be very
common for a suburban family of four to frequent the movie
theater, while a young
professional in a nearby downtown neighborhood may rarely
visit the theater, opting for live
music venues instead. Deep understanding of customer
demographics, behaviors, and lifestyle
preferences presents incredible business value by opening the
door to build products that
resonate, launch go-to-market strategies that are embraced and
extend a product’s lifecycle far
beyond the average sunset length, by understanding who the
customer is, what product and
where a product is desired, and where demand is highest.
Technology advancements enable the deployment of location-

based applications to
understand indoor spatial patterns such as foot traffic and dwell
time. As a customer makes her
way in pathway of locations, her movement data can be tracked
and assigned to her device ID.
This device ID then indicates the behaviors of the customer and
indicates willingness to buy a
product and/or promotions. Other examples of business data
include revenue and sales
metrics, employee profiles, customer profiles, asset logs, and
logistics. Asset management is
reliant on private business data and, in times of crisis, the
transparency of this data helps to
overcome risk. Knowing which assets are in a particular
location, how many trucks are enroute
nearby a hazard, or understanding how much revenue may be
lost if a route does not fulfill its
route, are all spatial questions that a business can answer with
spatial technology and business
data.
Business data may also include product data, research data, and
supply chain
data. Everyday business processes and functions also produce
spatial data. The business value
of this data is high as it directly contribute to product
development, asset tracking, customer
loyalty, and revenue gains. Some business data may be
automated in nature; for example
automated data from customer, supplier or facility movements.
Other data may come from
transactions that are reported. With the rise in automated
collection methods, what may have
previously taken a business several weeks, months, or even
years to capture and store within a
company’s database, may now be immediately streamed into

business applications. It is the
size and fluidity of this data which enables decision maker s to
add business value to the
organization in real time.
Business Data – Commercial
To make location intelligence more powerful, many
organizations seek third-party
private commercial data to enhance first-party business data. By
augmenting enterprise data
with third-party data, organizations create enriched portfolios of
location data. For example, a
company might combine their customer data, with a commercial
geodemographic data to not
only where their customers are located, but to also examine
where potential customers who
are like their customers are also located. This can help
determine a market growth strategy.
The emergence of Data Marketplaces has made commercial data
more readily available.
These self-serving ecommerce platforms have enabled the data
industry to sell commercial
data at scale. Commonly used Data Marketplaces include
Snowflake, Amazon Web Service
(AWS), and Azure. Data as a Service (DaaS ) solutions are now
easily accessed, with streamlined
integration and pricing models. With DaaS, data may be
streamed into organizational databases
32
and dashboards, bringing up-to-date data in, quickening the

process to push location
intelligence out. Beyond demographics, massive files on human
behaviors are often exchanged
through Data Marketplaces. This includes movement data,
which matches device IDs to
location. When a user ops-in a mobile device for location-
sharing, the device will aid in time-
stamping the pathways of the person. For example, over a 4-
month period in the New York City
area, location data from a single user’s smartphone was
recorded over 8,600 times, once every
21 minutes (Valentino-Devries, Singer, Keller, and Krolik
2018).
Community and Environmental Data – Public/Open
Like commercial data, public-open data may be used to augment
an organization’s
existing database. Open data is public data often made available
through a creative-commons
license from government sources or open-crowdsourcing
communities. Many familiar
authoritative U.S. government agencies provide public data as
open data in order to maintain
an open government status. For example, the United States
Census is a source of public people
data made available as open data. The US Census Bureau
provides authoritative demographic
data yearly and every ten years by the decennial census. Other
authoritative U.S. government
agencies providing business related public data include
Department of Commerce (DOC),
Environmental Protection Agency (EPA), and Department of
Labor (DOL). These agencies and
others provide essential community and environmental data that
can be used to track

corporate social responsibility actions and outcomes.
Open data may also be crowdsourced. It may come from
community contributed data,
as part of an open-data initiative to share data openly and freely
in the name of a common
cause. Many organizations choose to utilize hubs to host open-
data initiatives, which will be
discussed later in this chapter. Crowdsourced data can be added
via web or desktop
applications, but the validity of the data is not always cross -
checked. In some cases, open data
may be the only type of data available. When this occurs, an
organization must consider the risk
of this data’s accuracy and consider if other methods may be
used to vet or quality-check the
open data contributions. Many foundational GIS maps will use a
combination of authoritative
and crowdsourced open data. This helps to keep maps up-to-date
by allowing users to
contribute changes to the map in real time, and may allow for
changes that may otherwise be
overlooked, like the addition of a streetlight, road, or detour.
Imagery and Remote Sensing Data
Remote sensing refers to imagery that is not collected directly
but rather is collected at
a distance away from the object. Devices that do remote sensing
include satellites, planes, and
drones. These devices tend to have digital image collectors,
including radar collectors, LIDAR,
multi-spectral collectors, and digital cameras. Each mode of
collection is appropriate for certain
business applications, and each mode has advantages and
disadvantages (Sarlitto, 2020). Digital

cameras are user friendly but limited to the part of the
electromagnetic spectrum that covers
the range of the human eye, while radar can sense large areas
from high altitudes and
penetrate through cloud barriers, but have limited resolution and
are expensive. Local
overflights by small planes or drones with digital cameras are a
less expensive but widely used
imagery gathering approach, and in extractive, agricultural, and
environmental industries.
33
The imagery sector is enriching spatial business by providing
base maps, point clouds,
space-time imagery, AI-enhanced map imagery, and raster
analytics and modeling
(Dangermond, 2021). The rapidity of imaging means that
information can be provided in a daily
or under refresh, yielding timely business intelligence showing
emergency situations in business
facilities and locations, movements of assets, movement of
supply chain materials, and
understanding the geography and players in markets.
Today the earth has thousands of daily satellite overflights and
image gathering by
dozens of governments, international organizations, large well -
known imagery companies and
small firms (Sarlitto, 2020). For instance, in the US, NASA has
been collecting imagery in its
Earth Observing Satellite program for two and a half decades,
including its planetary land
surface imagery from the Landsat series and its Sentinel -6 in

collaboration with the European
Space Agency to measure global sea level rise. Large earth
observation businesses such as
Descartes Labs and Planet Labs in the US, Skyrora in the UK,
and Axelspace in Japan create
commercial satellites for earth observation, while smaller
specialist companies such as Zonda
design and manage small satellites and provide location
analytics services.
Zonda is a firm that produces business intelligence, advanced
imagery, and analytics
solutions for the business needs of home builders, land
developers and financial institutions.
Zonda brings a location analytics approach to monitoring of
residential investment properties
and construction sites (Reid, 2021). Traditionally, most
residential-real-estate construction
monitoring researched the stages of building construction. Prior
to the advent of the covid
pandemic in spring of 2020, Zonda’s field worker crews would
visit home sites quarterly to
observe and record the stage of construction, using simple
commercial mapping software.
Covid, however, put a stop to the field visits due to concern for
the health threat to the crews.
As a consequence of the business need created, the company’s
analytics team developed
machine learning algorithms that can be trained with data to
recognize which construction
stage a new home is in. Subsequently, during the pandemic,
Zonda rapidly grew its satellite-
based monitoring in scale and capabilities. It has now set a
standard that 80% of its home
monitoring to be performed by automatic satellite observation
and only 20% by the traditional

driving to sites.
Element 5: Platforms
Location analytics are not limited to a single type of platform.
Depending on the needs of an
organization, spatial technology may take shape through a
variety of platform solutions,
ranging from cloud-based software to enterprise system
deployment, to geo-enabled hubs.
Organizations of all sizes, budgets, and scalability, may find
success in deploying spatial
solutions. As diverse as the nature of business, spatial
technology is adaptable
and personalizable based on desired results and intelligence
goals.
Cloud
The evolution of cloud computing software has accelerated
major advances in spatial
technology. In this mode, GIS software can support a software-
as-a-service or SaaS model. This
revolution of GIS as SaaS has opened new opportunities for
organizations looking to establish a
34
spatial infrastructure. Within this infrastructure, the
organization can utilize a variety of spatial
tools to perform day-to-day business functions. With the
connections enabled from the cloud,
these tools can work in tandem with one another, enabling a
network which supports an

interconnected workflow among processes and people within
different areas of the
organization.
The architecture within the cloud has the capability to manage
all aspects of a
geospatial system, from maps and data, to analytical tools and
applications. The connection of
tools within the cloud environment enables location intelligence
sharing at scale. Millions of
active interactions can be taking place at any given time.
Organizations functioning within the
cloud benefit from the fluidity and open access of data sharing
and management. Open sharing
within a department or across the organization, enables users of
varying job titles to access and
build off one another’s work. Additionally, SaaS-based GIS
software is intelligently designed to
provide seamless updates, removing the necessity of
downloading the latest features and
updates.
Though data security within the cloud may be of top concern,
the evolving geospatial
architecture alongside advances in cloud-secure environments
has helped to alleviate this
concern among stakeholders. In many cases, the benefits of the
cloud outweigh the risk,
especially as organizations build out infrastructures that connect
workers onsite to home office
workers. By automating processes and streamlining the flow of
data, through other emerging
technologies, such as Internet of Things (IoT) sensors, vehicl e
sensors, social media and web
data, geographic information can be obtained in real time.

Enterprise
Enterprise integration is a process of expanding location
analytics and intelligence to
serve spatial needs across the entire organization. It needs to
serve all departments in the
organization that have need for locational systems (Woodward,
2020). Standalone spatial
systems in separate departments that have their own separate
databases create obstacles to
the consistent and rapid sharing of the same data. These
separate systems may serve a
department well in the early spatial maturity stages of an
organization, since the users are
limited and mainly interested in data from their own
department. Moving to enterprise
integration of data has many advantages including eliminating
redundancy in the same data
and enabling users in diverse areas of the organization to be
consistent in their use of location
analytics and intelligence. Having the common platform also
can be helpful from an information
security standpoint, since security protection can be
strengthened for one central data-base,
rather than scattering the security protection to the separate
“silos.”
Another advantage of an enterprise system is that any user of
location analytics
throughout the company has the potential to access all the
spatial data available organization-
wide. The user might not have present need for all this data, but
future needs might arise to
add data from a distant department and it will be easily
accessible. Because of widening of
spatial users, expanded data, and consistency of enterprise GIS

software, having an enterprise
spatial system adds to the competitiveness of the company.
35
A GIS enterprise system offers decision makers options as the
enterprise platform is a
robust system that connects with the full set of company GIS
solutions. Leaders increasingly are
choosing to base the enterprise system in the cloud or retain it
on an internal infrastructure, for
security and control reasons. Users looking to perform advanced
spatial analysis through
desktop or mobile applications can benefit greatly from an
enterprise system.
Desktop
While GIS started out with a strong desktop (stand-alone)
component, the massive
movement to the cloud over the last two decades have
substantially altered the use of this
platform. Although desktop provides essential high-end GIS
computing to skilled individuals or
teams of professionals, among the weaknesses include
challenges in scaling the number of
users and upping the capacity of desktop or local servers and
challenges in how to provide
simplified user interfaces to the business non-technical user and
vulnerability of desktop
systems to physical failure.
The desktop can be enhanced at low cost through connections to
WebGIS. This WebGIS

platform provides GIS software, data, and processing in the
web, so the user is freed up from
particular devices and installations and needs only a web
browser for access. This has several
advantages, including (Fu, 2020; Longley et al., 2015) that is
available worldwide, there is a low
cost of implementation, it has minimal requirements for the
desktop, and there are a large
variety of related web services and libraries that can be
integrated.
Portals
A spatial portal is a public or private location to share
applications across designated
users that can range from small groups to the entire
organization (Tang and Selwood, 2005,
Esri, 2021). The portal is useful to organize information and
make it available to groups of users.
Private and restricted information can be excluded from the
portal or made accessible only by
approved users. The overall philosophy behind the portal
concept is to make as much
information as possible open and easily accessible. A portal is
designed to share the
information across mobile devices, social media, WebGIS, and
servers.
An example of a business portal is Esri’s Portal for ArcGIS,
which can be installed, along
with its applications, on a company-owned server or in the
cloud (Esri, 2016). Among the
provider’s applications commonly available are: cloud-based
GIS (ArcGIS Online), a user-friendly
mobile app builder (WebAppBuilder), a dashboard creator and
operator (Operations

Dashboard), two applications for collecting information with a
mobile device (Collector for
ArcGIS and Survey 123), software that supports users in
creating imagery including point clouds
(DroneMap), and an app to encourage teamwork and
coordination for field workforce
(Workforce for ArcGIS). These applications can be
supplemented with other applications
available commercially and proprietary ones developed by the
company.
A spatial portal has the aim to foster collaboration among the
users within a business. A
company can offer private portals restricted to segments of its
workforce as well as public
portals for invited customers or open to the general public
(Tang and Selwood, 2005; Esri,
2016). For the portal user, the portal provides a web-based set
of applications, flexible and
36
accessible across devices, to address a problem, develop
location analytics solutions, and
support research or decisions. The web positioning of the portal
does raise potential security
issues but security measures can mitigate or largely eliminate
the risk.
Hubs
Organizations looking to center their workforce around
collaborative initiatives, may
find interest in the cloud-based hub platform. Hubs are much

broader in reach than a portal,
providing a centralized location for the enablement of people,
data, and tools to reach common
goals. The platform enables a single place to communicate and
collaborate on key initiatives
within the organization, including planning activities, sharing
projects, and goal setting. Often
used for community engagement, a hub can be used for a variety
of business functions,
including spearheading a new product launch, strengthening
company culture, or onboarding
new employees. Hubs support the hosting of geo-enabled
databases, allowing quick access to
mapping layers, prepared visualizations, raw datasets, and
StoryMaps.
Functioning much like a website, a hub grants unlimited
possibilities when building up
its contents, allowing components and structure to take shape
around business needs.
Templates are adaptable to brand personalization and unique
initiative aspects. Analytical
components of a hub also allow for the tracking of views and
interactions within the platform.
This added value can help decision makers to evaluate content
engagement and stakeholder
interaction.
With hubs, organizations are enabling open sharing of location
intelligence across the
globe, while centralizing around common themes that connect
people and places on a unified
platform. The Los Angeles GeoHub exemplifies a community
hub with intensive use (see Figure
2.5). The hub has publicly available maps, downloadable data,
and documentation on over

1,000 features for citizens, and business alike, including
analyses of job acceptability,
populations, and a range of demographic analyses (Marshall,
2016). The hub also has features
to create customized experiences, contribute data to the hub,
and participate in, collaboration
and co-creation of work being done on initiatives by internal
teams, and external teams or
combined teams (Esri, 2021).
37
Figure 2.5 Los Angeles City’s GeoHub comprised of sets of
features the public can access to explore, visualize, analyze,
build,
export, and share maps, with an example being a map of job
accessibility by transit type
(Source: City of Los Angeles, 2021)
The application to spatial business is that a data-centric
business that seeks to interact
with the public can install a Hub with the intent to improve
sharing and collaboration of its own
teams inside the company, provide easily accessible maps and
data to certain customers or the
public at large, and encourage collaboration between the
company’s workforce and outside
existing and prospective customers. The firm as a profit-making
organization could maintain a
portion of its data on the inside for commercial sales. For
instance, the hub might be
appropriate for government data providers, statistical data
providers, or environmental

consulting firms, or think tanks.
A general point for platforms and applications concerns data
protection of private
information. Recent movements in consumer data protection
have shifted the use of location
services to now include permission-based options. Many
applications now require opt-
in services, i.e. mandating that the user have the choice not to
agree to provide her personal
data at the time of entering a software service. These changes
have been important to protect
the rights of consumers and in many ways, also have been
crucial to alleviating the ethical
dilemma that organizations often face in utilizing consumer data
tracking. Keeping consumer
38
data private and permission-based, helps to protect personal
information. Ethical data practices
protect both the consumer and the organization. Companies
should have a sound ethical policy
and data implementation strategy can help to minimize this risk
for an organization looking to
implement spatial technology.
Element 6: Location Intelligence
The second level of location intelligence are decisions that are
based on location insights.
Location intelligence can inform decisions to increase
competitiveness, foster new products and
services, and provide new ways to strengthen ties with suppliers

and buyers. For instance, in
the Kentucky Fried Chicken its franchisees with rich and varied
social media and demographic
information at the micro level for decision-making on
competitive locations of retail units.
Decision making also benefits by group collaboration, big data,
and anytime/anywhere
availability of location analytics (Sharda et al., 2018). The wide
dispersion of location analytics
capability in companies means that dispersed team members can
be informed by location
analytics and collaborate on decisions.
A third level of location intelligence are the impacts that result
from location analytics. These
impacts include the performance of a company on number of
dimensions such as market share
growth, customer satisfaction, operational efficiencies, supply
chain reliability, and societal
benefit. As seen in this chapter’s Walgreens closing case study,
impacts occur at the operational
level as well. In this case, it was the quick development of
application to provide testing and
vaccines equitably to the public that also served to enable of
broader enterprise solution.
Closing Case Study: Walgreens
Walgreens is one to the two largest pharmacy firms worldwide,
with 2020 revenues of
$140 billion and assets of $87 billion. It operates 9,000 drug
stores throughout the US and
acquired a majority interest in Alliance Boots in 2014, one of
the largest pharmacy firms in
Europe and parts of Asia. It acquired 1,000 Rite Aid stores in
2017. Walgreens has intensive

competition and must drive sales volume to achieve its on its
profit margins (Morningstar,
2021). The firm also faces regulatory constraints on its drug
products and pharmacy activities.
GIS has been present in the firm for over fifteen years, and its
principal use is for
planning and operations of its store network throughout the US.
It has a spatial enterprise
system for its US stores and employs simpler cloud-based
spatial solutions for certain special
projects. The corporate GIS team for operations in the US
consists of the director of enterprise
location intelligence, several GIS power-users, and specialists
who focus on external facing
provision of spatial data, statistics, and maps. The team
participates in designing the GIS
features for mobile applications of its customers that are used to
make use of the varied
product channels of the firm, including visiting and purchasing
from stores.
39
Figure
2.6
Regional middle managers at Walgreens discuss how to solve a
spatial problem, which is being displayed on a large screen
using the firm’s WalMap software
(Source, ArcWatch, Esri, 2015)
The leading GIS applications are WalMap and WalMap Pro,

locally tailored software that
supports middle and upper-middle management corporate-wide
in group analytics and decision
making. In regional offices throughout the firm, conference
rooms have large screens displaying
Walgreens and competitors’ store locations, demographic
features, and market indicators (see
Figure 2.6). The enterprise system supports WalMap on mobile
devices and the web, so it is
device-independent and centrally controlled. WalMap provides
solutions for (1) deciding on the
locations of stores, and the optimization of the geography of
groups of stores, (2) giving broad
demographic and marketing information to the middle ranks of
management firm-wide, (3)
maintaining and displaying competitive data on a monthly basis,
so merchandising can if a store
location is competitive or not, and (4) offering a range of other
mapping, e.g. pharmacy
information, market share, aerial imagery, while WalMap Pro
provides advanced tools to a
select group with the market planning and research group.
The systems development of these two critical applications was
accomplished through
the coordination of four systems development teams: the
corporate GIS team, internal IT, an
offshore outsourcer, and Latitude Geographics from Canada.
The project success is ascribed to
a talented director of enterprise location intelligence, who kept
everything moving. This key
manager reflected that “the team had to have patience and set
reasonable expectations. They
said it’s going to take a little bit longer to make sure we get it
right and that’s okay, with the
payoff at the end” (Walgreens, 2018). The same team also

commenced a spatial enterprise
project.
Walgreen’s relative slow and deliberate improvement steps w ere
interrupted by the
start of the Covid pandemic in March of 2020. A White House
meeting led to immediate urgent
request to Walgreen’s location intelligence director to decide on
where testing sites would be
40
set up. The director created a web app within hours that could
choose testing locations and
shared it with other key leaders, in a rapid exchange lading to a
prototyped solution. It was
later refined by the spatial team and they started within weeks
designating covid testing sites
throughout the nation, eventually designating a national set of
thousands of locations, and by
the following winter doing a similar designation for stores
giving vaccinations (Walgreens,
2021).
In this rapid and successful site selection, the team was helped
by a robust data-base
already present of store locations and attributes, demographic
and other information. In the
process, the team emphasized equity of distribution and set-up
the socially vulnerable
communities first.
An offshoot of this emergency intervention in the covid crisis is
that the GIS leadership

pushed forward rapidly and complete the spatial enterprise
system that had been on a slow
track before the pandemic (Shah, 2021; Walgreens, 2021). The
firm’s location enterprise portal
resides on a cloud platform from a leading vendor. The user can
enter the portal through an
enterprise location intelligence homepage, which provides
access to all the enterprise modules,
which include the latest versions Wal Map, Wal Map Pro, and
the Asset Protection mapping
module that provides location analytics for sustaining and
protecting assets from
environmental and other threats to assets (see Figure 2.7). The
portal entry page reinforces the
chapter point that varied personnel have different mapping
needs. Here, middle managers use
the simple and data-rich WalMap app; GIS analysts and
company planners utilize the full-
featured WalMap Pro; and emergency and security personal
depend on the specialized asset
protection map. All the interfaces draw on the centralized
enterprise data and analytics.
An example of the Wal Map Pro capabilities is real-time display
of customers, in a time
slice, for three Walgreens stores in the Inland Empire of
southern California, showing a
tendency for clustering of customer residences around each of
the three stores, with relatively
little cannibalization between stores (Figure 2.8). This
exemplifies the power of cloud-based,
real-time situational analytics. With the cloud platform,
Walgreens GIS team is better able to
integrate mapping and location analytics to employees accessing
mobile devices, remote
workers at home, and group decision making in regional offices

and headquarters, while
communicating through integrated web mapping across the
company.
Walgreens GIS management indicated the company has moved
forward to be 80
percent of the way towards the full GIS maturity level.
Although the location enterprise portal
has been the centerpieces so far, GIS management sees a
number of projects needed in the
future to move towards maturity, including supporting the
Internet of Things (IoT) and
introducing AI and machine learning analytics.
In summary, Walgreens illustrates the dimensions of a Spatial
Business Architecture.
There are business goals that drive its use and there is a strong
technical team that collaborates
with management to address needs with solutions. As such, they
demonstrate the
development of enterprise level spatial platform that responds
to business needs, attends to a
41
pressing societal (pandemic) issue, relies on human talent, and
is creating strategic value for the
company.
Figure 2.7 A diagram of Walgreen’s Enterprise Portal shows its
major components of WalMap Pro, Enterprise Location
Intelligence, Asset Protection Mapping, and WalMap Lite
(Source: Walgreens, 2021)

Fig. 2.8 Wal Map Pro: Mapping of a Time Slice of Customers’
Affinities for three neighboring Walgreens stores in San
Bernardino County CA
(Source: Walgreens, 2021)
42
CHAPTER 3
Fundamentals of Location Analytics
Introduction
To achieve competitive success, organizations are increasingly
placing analytics at the
heart of the business. Business analytics has been defined by
Thomas Davenport as the
“extensive use of data, statistical and quantitative analysis,
explanatory and predictive models,
and fact-based management to drive decisions and actions”
(Davenport and Harris 2017, pg.
26). Davenport’s definition not only highlights the importance
of data and analytical modeling
in contemporary organizations but connects it to decision-
making and actions. This connection
is key. Clearly, the analytical process of transforming data into
insights has a purpose: to
provide evidence and support for decision-making. By using
analytics to make better decisions,
organizations generate value that manifests in the form of
business benefits, both tangible and
intangible. These include cost savings, revenue growth via

increased share of wallet, uncovering
business opportunities and untapped markets, increase in
productivity, process improvements
resulting in asset efficiency, enhanced brand recognition,
increased customer satisfaction, and
benefits to the environment and society. In digitally mature
organizations, analytics is not just
an add-on to existing processes and practices; rather, it is fully
integrated into the strategies
and business functions.
This chapter dives deeper into location analytics element
introduced as part of the
Spatial Business Architecture and explores their use throughout
the location value chain.
Location analytics can inform where companies should locate
new stores, stock facilities, and
enhance infrastructure. They can guide employee recruitment,
optimize sales territories, and
maximize loyalty programs. They also help companies appraise
risk emerging from threats such
as competitors, the natural environment, changing business
patterns and trends, and
unanticipated events such as weather emergencies, pandemics,
and social unrest —
information that is critical to a proactive response, mitigating
threats to business continuity and
improving business resilience. Finally, location analytics helps
companies direct their
philanthropic endeavors and shape corporate social
responsibility (CSR) strategy and initiatives.
Principles of Business Location Analytics
Business Location Analytics, which recognizes the critical
importance of location in

business, helps organizations make decisions informed by data
(location as well as non-
location) and using sophisticated analytical methods and spatial
analysis techniques.
Underpinning business location analytics are the following four
principles of location:
43
Contexts.
Location Proximity and Relatedness
As businesses seek ideal customers, their own locations relative
to the locations of
prospective customers and competitors becomes paramount. For
example, a bakery and coffee
retail chain seeks customers with certain consumer preferences
and wants to ascertain where
these customers live, work, and shop. What are their usual
routes as they travel from work to
school, or from home to work, and how far do they live from
various points of interest (POIs)
such as grocery stores, gyms, libraries, and restaurants? What
are their demographic,
socioeconomic, and psychographic characteristics?

The first principle of Location Proximity and Location
Relatedness stems from Tobler’s
(1970) first law of geography. This seminal law states:
“Everything is related to everything else,
but near things are more related than distant things.” “Related”
and “near” are the
cornerstones of this first principle of location. In business
decision-making, proximity—whether
to customers, suppliers, competitors, complementary businesses,
assets, or infrastructure—is
the basis of important strategic, tactical, and operational
considerations. Proximity implies
relatedness, or spatial heterogeneity, and suggests that local
factors can make one area
significantly different from others. In a business context,
nearness or proximity is often
measured in terms of driving distance, driving time, or walking
time, and is fundamental to the
delineation and description of trade areas and service areas,
discussed later in this chapter.
For example, consider a full-service insurance company. To
determine premiums, an
insurer would factor in proximity of businesses and customers
to coastal areas that are prone
to flooding, population density, and crime rates among many
other factors. It is often the case
that insurance premiums of two similar properties, located in
the same neighborhood, close to
each other and equidistant from the coast (nearness) are likely
to be comparable (relatedness)
than to properties located in a different neighborhood
(dissimilar population density and crime
rate) farther inland.
For location analytics modeling and decision-making, nearness

and relatedness have
several implications. Nearness and proximity are often
measured for business locational
decision-making using distances that are factored into various
models for description,
prediction, and ultimately decision-making. However, nearness
could potentially introduce
spatial bias into location-based analytical models which stems
from a pitfall of spatial data,
known as spatial autocorrelation. These implications,
particularly the issue of spatial
autocorrelation (Longley, Goodchild, Maguire, & Rhind 2015)
has methodical implications for
location analytics models.
Location Differences, Linkages, and Contexts
The next three principles that inform business location analytics
are Location
Differences, Location Linkages, and Location Contexts (Church
and Murray 2009). The first
principle, Location Differences, indicates that some locations
are better than others for a given
44
purpose. The second principle is Location Linkages, meaning an
optimal multisite pattern must
be selected simultaneously rather than independently, one at a
time. The third principle,
Location Context, indicates that the context of a location can
influence business success
(Church and Murray 2009).

The principle of Location Differences can be illustrated through
the lens of industry
clusters - geographic concentrations of interconnected firms and
institutions in a particular field
(Porter, 1998) that are known to be engines of economic
development and regional prosperity.
For example, Napa Valley in northern California is a leading
wine production cluster worldwide
due to a climate that is conducive to grape production and
consequently the presence of
hundreds of independent wine grape growers among many other
factors. On the other hand,
Carlsbad near San Diego in southern California has a
specialized cluster of golf equipment
manufacturers that has its roots in southern California's
aerospace industry which spawned
manufacturing businesses specializing in metal castings and
other advanced materials. As a
consequence of location differences between the two regi ons,
businesses engaged in sports
equipment would have more affinity for the Carlsbad region,
while manufacturers or parts
suppliers of irrigation and harvesting equipment are more likely
to find the Napa region more
attractive. Regional economics uses a measure, “Location
Quotient (LQ),” to measure this
industry concentration of a region.
Next, to understand Location Linkages, consider medical device
manufacturing industry
which is linked to nine other industry clusters:
biopharmaceutical, distribution & e-commerce,
jewelry, recreational goods, electrical wiring, plastics, IT,
production technology, and
downstream metals (Cluster Mapping, 2021). A recent study
(Munnich, Fried, Cho, and Horan

2021) has shown that the U.S. state of Minnesota is a national
leader in medical devices
manufacturing. The development of the medical device cluster
in Minnesota originated from
greater Minneapolis metropolitan area due to Medtronic, a
leader in medical devices
innovation. Now, in Minnesota, almost 500 medical device
companies statewide are
complemented by over 6,000 businesses in the aforementioned
nine linked industries (Munnich
et al. 2021).
In terms Location Context, the Minnesota's medical device
cluster relies on a robust
professional, scientific, and technical services workforce as
well as a reliable transportation
network that enables efficient goods movement. Alongside the
Mayo Clinic, the greater
Minneapolis-St. Paul metropolitan area is home to the
University of Minnesota, a large, flagship
university, producing a readymade workforce comprised of
medical, business, engineering, IT,
and computer science professionals, analysts, and researchers
for medical device and other
companies that offer plenty of high-paying jobs. In addition,
from the perspective of Location
Context, Minnesota's medical device industry benefits from the
presence of the Minneapolis-St
Paul Airport (MSP), which acts as a gateway for high-valued
and often critical of medical devices
and equipment that are exported to various parts of the world.
These principles have the following implications for business
location analytics. To
develop an effective location-based business strategy, it is
essential to adopt a clear

understanding of how locations affect business success.
Locations can provide a competitive
45
advantage due to their distinctive physical, environmental, and
human characteristics.
Locations can have vital business linkages for talent, customers,
talent, and supply chain and
logistics networks. This creates a location context that if
properly understood can be a key
factor is business strategy and success. Location analytics
provides to tools to assess such
factors.
Hierarchy of Location Analytics
Stemming from three well-known categories of analytics –
descriptive, predictive,
prescriptive--the hierarchy of business location analytics is
comprised of Descriptive Location
Analytics, Predictive Location Analytics, and Prescriptive
Location Analytics, as shown in Figure
3.1. Each step of the hierarchy is informed by increasing levels
of sophistication of analytical
(mathematical and statistical) methods. The hierarchy of spatial
analysis techniques – spatial
data manipulation, spatial data analysis, spatial statistical
analysis, and spatial modeling
(O’Sullivan and Unwin 2014) informs the hierarchy of Business
Location Analytics. As the extent
of sophistication of mathematical and statistical modeling as
well as spatial analysis increases,
the extent of location intelligence and consequently location-

based insights increase as well.
This can be leveraged by a business to design spatially-aware
business strategies that yield
competitive advantage.
46
Figure 3.1 Hierarchy of Business Location Analytics, comprised
of descriptive, predictive, and prescriptive location analytics
(Source: Author)
Descriptive Location Analytics
Descriptive Location Analytics is often used to analyze current
or historic business data
to understand what has or is occurring and where. Descriptive
location analytics is
characterized by spatial visualizations such as maps, reports,
and dashboards. These show
where customers, employees, stores, competitor locations,
suppliers, distribution centers,
transportation hubs, critical infrastructure and assets, and points
of interest are located, often
relative to each other. They also reveal spatial patterns and
changes of important business Key
Performance Indicators (KPIs) such as sales, profits, revenues,
and consumer preferences, as
well as geographicall y referenced characteristics of areas of
interest, such as socioeconomic
attributes of trade areas.
In other contexts, maps, reports, and dashboards may show
important parts of a supply

chain. These may include connections, linkages, and routes
between customers, suppliers, and
distribution or transshipment hubs, as well as territories
vulnerable to business disruption—for
example, due to a natural disaster or other emergency such as
the failure of critical
infrastructure. Descriptive location analytics provides important
visual cues, uncovers patterns,
and reveals location-specific insights previously unknown to a
business.
The techniques of descriptive location analytics often include
various forms of spatial
visualization and mapping. It involves descriptive data
modeling of features that are relevant to
a business, showing patterns and trends that form the basis for
exploratory analysis. Spatial
data manipulation and rudimentary spatial analysis functions
such as intersection, union,
overlay, querying, and basis summary statistics characterize
descriptive location analytics. Apart
from these techniques, distance and proximity analysis,
buffering, density analysis, and 3-D
modeling are often part of descriptive location analytics. As
mapping provides visual cues of
change, descriptive analysis is an efficient way to turn large,
complex datasets into effective
visualizations. These maps bring valuable spatial context to
decision making. In many cases,
descriptive analytics may be the greatest depth needed to draw a
statistically backed
conclusion.
The process of moving from exploratory to more advanced
analytics is considered
“enrichment” and can introduce several layers: a customer

layers, a geo-demographic layer, a
facilities layer and so forth. Visual patterns may emerge as
obvious or may necessitate
descriptive spatial statics such as hotspot and cluster analyses.
Descriptive analytics may be used to further prepare data for
predictive and prescriptive
analytics. It is often a manual process and may be seen as a first
pass of data discovery. A series
of spatial layers may be overlapped to observe correlations
among varying datasets.
Additionally, spatial layers may be used as the foundation to
deeper analysis. Whereas the
information products created in descriptive analysis can then be
applied as the foundation for
predictive and prescriptive analytics.
47
Illustration of Descriptive Location Analytics
Consider the example of Sephora, a beauty and personal care
specialty brand, which is
considering business development and expansion in the greater
Houston, Texas, metro market.
To examine market saturation and analyze opportunities, it has
created a web-map comprised
of several layers, shown in Figure 3.2.
Alongside existing Sephora locations (black circle with Sephora
logo) and Sephora-at-JC
Penney locations (red circle with Sephora logo), market
opportunities are shown as five
categories: proposed locations of new stores (red circle with a

number), locations with ongoing
negotiations (blue asterisk), areas of interest for business
development (red asterisk), locations
of interest but no opportunity to develop at the present time (red
square), and locations that
have been evaluated but ultimately not selected for development
(black square). Geo-enriching
the map are some of the external variables such as major
shopping center locations with
attributes including gross leasable areas, anchor stores, year
opened, and annual sales, as well
as various population and demographic variables.
On an ongoing basis, these map layers can provide descriptive
insights to Sephora’s
business development team. In terms of location analysis, the
map layers are rich with
possibilities. For example, drive-time buffers can produce trade
areas to be analyzed for market
potential. Proximity analysis to competitors, nearby shopping
centers, and retail locations can
inform sales forecasts; and co-tenancy, i.e., two tenants leasing
the same property, is a useful
metric for site comparison and site suitability analysis.
Figure 3.2 Web-map layers of Sephora locations and
opportunities for business development, greater Houston TX
metro area
(Source: Esri, 2021c)
NOTE TO EDITOR: Prefer this Figure in LANDSCAPE MODE
48

Predictive Location Analytics
Descriptive location analytics provides organizations with
location intelligence about the
current state of business or about past business patterns. While
insightful, descriptive location
analytics only scratches the surface to what is possible with
location analytics. Predictive
location analytics provides a deeper understanding of what is
happening in current and past
states, while simultaneously detecting meaningful statistical
patterns to predict future business
outcomes. Through the identification of spatial patterns,
business outcomes may be forecasted
which can help decision makers to see opportunities for growth,
profitability, and risk.
With the availability of very large and timely data sets, many
organizations increasingly
rely on predictive location analytics to discover meaningful,
statistically significant spatial
patterns, spatial clusters, and outliers, related to the prediction
of business outcomes such as
profitability, growth, risk of customer defection, and incidences
of fraud. Whereas standard
(non-spatial) predictive analytics determines what is likely to
happen in the future; predictive
location analytics determines not only what is likely to happen
in the future, but where. In this
way, predictive location analytics provides guidance to
organizations about the likelihood of
future events, contingent on location.
Predictive location analytics methods are more sophisticated
that descriptive location

analytics. These techniques involve the application of
traditional statistical and geostatistical
approaches to analyze spatial clusters that may become evident
from descriptive mapping. For
example, clustering models may confirm the importance of a
region of parts suppliers in an
organization's supply chain. Analysis of spatial clusters of
internet users in a region may provide
insights about a region's preference for e-commerce; this has
implications for an organization's
business development strategy. Statistical models may show
hotspots and coldspots of
customer activity on social media. For example, the use of
social media data may be used to
discover pockets of expressive sentiments about companies and
their products across the US.
Geotagged sentiments can be mined to uncover a range of
customer experiences and emotions
and predict regions where customer churn is likely and therefore
customer retention efforts
need to be initiated or redoubled.
Another form of spatial statistical analysis involves regression-
based modeling that
establishes associations between a dependent variable of
interest and several independent
predictor variables. For example, an insurer may model
insurance premiums based on location,
type of client, type of insurance, proximity to risk factors, etc.
Such models that factor in
locations relatedness, nearness, and differences may be prone to
spatial bias. Diagnostic tests
that measure spatial bias are essential for such models.
With the increasing availability of geospatial big data, spatially
mature organizations are

deploying data mining and geo-Artificial Intelligence (GeoAI)
based predictive models that use
machine learning to explore spatial relationships between
dozens of factors and an outcome of
interest for a business. For example, GeoAI models can uncover
potential threats to a firm's
supply chain in the event of a natural disaster or an emergency.
Illustration of Predictive Location Analytics
49
In the agriculture sector, effective weed control is essential to
that minimizes damage to
crops spread over tens of thousands of hectares. To accomplish
this, farmers need to know the
precise location of each crop. They also need to accurately
pinpoint the application of
herbicide, as well as of fertilizer and fungicide. Each crop has i t
its own set of conditions, and
spraying herbicide outside precisely defined buffers can not
only destroy crops but render
entire fields unfit for farming while reducing costs related to
overused herbicides and wasted
water. Another consideration is how aggressively to treat the
weeds. Growers need information
on the types of weeds, as well as their number and location, so
that herbicide programs can be
tailored accordingly.
To address this problem, cameras and sensors outfitted on
tractors capture geotagged
images every 50 milliseconds from different angles, creating
enormous volumes of

spatiotemporal big data on crop and weed growth. The cameras
and sensors on these “see and
spray” machines (Figure 3.3, Peters 2017) use deep learning
algorithms that are in many ways
similar to facial recognition.
Figure 3.3 Blue River Technology’s (acquired by John Deere)
LettuceBot with See and Spray Technology, at work, in Salinas
CA
(Source: Chostner, 2017)
To recognize weeds from crops, deep-learning-based neural
networks are trained by
tens of thousands of images––unstructured big data––stored in
huge image libraries (Chostner
2017). With sufficient training, these systems are able to
recognize different types of weed with
high degrees of accuracy. Matching weeds to locations, “see and
spray” machines spray precise
amounts of herbicide within precisely defined crop buffers.
Deep learning algorithms are
enriched to take into account other location variables such as
gradients of hillsides. This deep-
learning-based location intelligence approach has been shown to
save tremendous amounts of
herbicide compared to conventional spraying technology,
enabling farmers to grow more with
less. The automation of weed control also results in cost savings
for labor, especially on large
farms.
Prescriptive Location Analytics
Prescriptive location analytics is the most advanced form of
location analytics. Reliant

on historic data and trained models, prescriptive location
analytics can calculate the probability
of events occurring, which consequently produces a suggested
course of action. While
50
predictive location analytics generate a forecast or predict the
likelihood of an event occurring,
the output of a prescriptive model is a decision, much like a
prescription written by a doctor to
treat an ailing patient. When viewed through a spatial lens,
prescriptive location analytics
model business scenarios by factoring in locational attributes
and constraints of specific
location to prescribe the best course of action to determine
optimal business solutions and
outcomes. Prescriptive location analytics satisfy business
objectives by factoring in
mathematical equations which connect data based on proximity,
relatedness, and calculation of
spatial differences.
Prescriptive location analytics are widely used in facility
location decisions, supply chain
network design, and route optimization. It is particularly
relevant for optimally siting
manufacturing facilities relative to the locations of suppliers,
customers, transportation options
and environmental considerations, and for siting retailers’
warehouses, fulfillment centers, and
distribution centers in coordination with complex and/or
shifting demand patterns. Spatial
objectives may include the minimization of transit costs,

maximization of population coverage,
or a combination of several objectives. Non-spatial constraints
may include schedules of
deliveries to be made, delivery time windows, trucking
capacity, and regulations imposed by
state and federal departments of transportation. Spatial
constraints may occur as a result from
barriers imposed by the physical geography of a region (for
example, mountainous terrain, or
temporary road closures), street attributes (for example, one-
way streets in central business
districts, historic traffic patterns, speed limits), and distance or
travel time restrictions.
Prescriptive location analytics can support organizations to
address strategic, tactical,
and operational problems and can be especially helpful when
problems are repetitive in nature.
Often underpinned by optimization approaches that are
grounded in operations research and
enriched by spatial analysis, prescriptive location analytics can
provide optimal or close-to-
optimal solutions for large, complex problems. Apart from site
location analysis and distribution
system design, such models are used for routing optimization,
demand coverage, analysis of
cannibalization, and for informing relocation strategies in
myriad settings.
Illustration of Prescriptive Location Analytics
Consider the example of CIDIU S.p.A., an Italian company that
works in the sector of
environmental services, dealing with all aspects of the waste
management cycle: collection,
treatment, disposal, recycling and energy recovery, integrated

sophisticated optimization
modeling with GIS to schedule the weekly waste-collection
activities for multiple types of waste
without imposing periodic routes.
The company's main objective was to generate efficient weekly
shifts of garbage pickup
by reducing operational costs and minimizing the total service
costs, including environmental
costs. Main decisions to be made included the weekly
assignment of a vehicle to a garbage type
and the daily route of each garbage pickup vehicle for each shift
(Fadda et al, 2018).
By innovatively using the IoT paradigm, the company outfitted
dumpsters and garbage
pickup vehicles with sensors that would monitor the capacity of
garbage in dumpsters and
vehicles. As soon as capacity of dumpsters approached 80%,
depending on the type of trash, an
51
appropriate vehicle from the company's daily operational fleet
working three shifts of 6 hours
each would be routed (or, re-routed) to pick up the trash,
depending on location proximity,
capacity of the truck, and several other factors. The service area
was an urban area near Turin,
Italy. By integrating IoT, GIS, and optimization modeling and
using a location-aware approach
(see Figure 3.4), CIDIU S.p.A. was able to completely eliminate
the third shift for the entire
service area. In addition, the number of vehicles used during a

test period decreased by 33%
reducing waste-collection operational costs and increasing the
company’s competitiveness
(Fadda et al, 2018).
Figure 3.4 CIDIU S.p.A.,
Solution
Architecture comprised of field components, middleware, route
optimization algorithm,
web portals, and apps
(Source: Fadda et al, 2018)
Location Analytics Across the Value Chain
The application of descriptive, predictive, and prescriptive
location analytics are spurred
by specific business needs. Table 3.1 and the subsequent
descriptions serve as an overview of
various types of business needs chain that necessitate the
application of location analytics.
They are offered as a starting point for assessing an
organization's value chain and how
different parts of the value chain can stand to benefit from the

deployment of location
analytics.
Research and Development
Recent location intelligence market studies have revealed that
location intelligence is
critically important for the Research & Development (R&D)
function, more so than for other
organizational functions such as operations, marketing, and IT
(Dresner 2020; Spatial Business
Initiative 2018). Drivers of this trend include the proliferation
of mobile geolocation data and
52
the development of mobile-friendly location-based services.
Such studies have also shown that
R&D interest in location intelligence is highest for data
visualization purposes, followed by
middling interest for conducting real estate investment and
pricing analysis, geo-marketing, site
planning and site selection, territory management and

optimization, and fleet routing and
tracking.
Emerging areas of R&D interest in location intelligence are for
business purposes such as
supply chain optimization, indoor mapping, and IoT. Unlocking
location intelligence from
massive mobile data to understand patterns of human movement
is another critical
contemporary area of R&D activity in many sectors. By
understanding human mobility,
businesses can forecast supply and demand efforts to account
for locational preferences and
seasonality differences. Knowing when to deploy new products
and services is just as important
as knowing where.
Table 3.1 Applications of Location Analytics along the
Organizational Value Chain
Value Chain
Location Analytics

Descriptive Predictive Prescriptive
R&D and
Market
Research
Customer Preferences
Mapping of customer
behaviors to
understand product
preferences.
New Product Prediction
Analyzing demographic,
income, and spending
behaviors to predict the
likely success of a product
in different locations.
Feature Segmentation

Use of Telemetry data to
understand feature and
functionality response, which
is funneled into a dashboard to
indicate where to first launch a
new product feature.
Marketing
and Sales
Audience Targeting
Identifying target
areas for the purpose
of marketing to select
audience types,
developing sales
territories to service
the needs of a region.

Customer Prediction
Identifying common
consumer behaviors over
time to predict where you
will find more customers
like them to increase lift to
a retail location.
Campaign Attribution
Discovering the right
advertising channel to focus
marketing efforts on based on
location-based target audience
behavioral response to
different media types.
Location
Planning and
Real Estate
Strategy

Expansion Analysis
Comparative
economic, talent,
Site Selection
Trade Area analysis to
determine location of new
Virtual Site Creation
Creation of a Digital Twin that
mimics the characteristics of
53
customer, community
attributes of business
sites for expansion.

facilities. Analysis of
supply chain network to
determine location and
needed capacity of a new
distribution center.
the real world site, that can be
manipulated with varying
datasets to analyze design and
implementation plans and
identify risks.
Operations Monitoring
Dashboards
Dashboard that
monitors activities
across key business
locations.
Employee Safety
Management
Analyzing employee safety

trends to improve health
and safety management in
high risk locations.
Supply Chain Optimization
Projecting incremental store
traffic lift to aid in
procurement efforts to keep
shelves stocked with product
during seasonal high demand.
Corporate
Social
Responsibility
Diversity Progress
Mapping business
and community data
to tell a story of
diversity and the
impact on the local
community.

Environmental Impacts
Dashboard that visualize
and predict environmental
impacts of operations in
order to assess needed
changes.
Health Targeting
Understanding the likelihood
of a population to receive a
vaccine, deploying more
communication efforts in areas
where response may be low.
Descriptive Location Analytics may look at store locations with
consumer attributes
attached such as overall sales, distance to store, and so forth.
Predictive Location Analytics may
take this a step further, by utilizing historic consumer behavior
data, such as demographics,
purchase behaviors, and foot traffic to predict the likeness of a
consumer to buy a certain
product within a certain market. Prescriptive Location Analytics

will project consumer behavior
data, while estimating the likelihood of certain outcomes, to
help the business hone new
product features to defined market segment and locations.
With location at the forefront of decision making, R&D efforts
can sway with the ebbs
and flows of different trade areas, while also taking into
consideration consumer demographics,
behaviors, and store proximity. It can also help retailers to
understand which areas are more
likely to buy online vs. brick-and-mortar. How to advertise to
audiences while en route to a
location. And how to restock supply based on shelf
replenishment needs, in the quickest, most
efficient ways possible. Beyond retail, urban mobility patterns
can also be used to determine
risk - particularly in the insurance industry, which may be
experimenting with new terms and
policies based on location. By performing location intelligence
research and analyzing data
through the various location analytics techniques, businesses
can stay ahead in understanding
how to deploy critical business developments in the right place,
at the right time.

Other areas of R&D inquiry include strategic location planning
at scale, involving
networks of stores, competitors, traffic, demographics,
psychographics, urban mobility
patterns, and other factors. Beyond location planning,
manufacturers and utility companies are
using digitalized representations of facilities and critical
infrastructure to create digital twins. A
54
GIS can then simulate movements of people and parts and track
the use of machinery,
equipment, and other assets using sensor data. This can help
predict equipment breakdowns
and facilitate preventive maintenance.
Marketing and Sales
Taking customer data a step further, businesses that seek to
understand their markets
and audiences for expansion find great insights through location

analytics. Descriptive mapping
of an organization's customer data provides insights about
customer segments that may
otherwise be locked up within Customer Relationship
Management (CRM) databases and
Customer Data Platforms (CDPs). By enriching demographic
data with socioeconomic attributes
and business data, businesses can segment customers into
audiences based on consumer and
lifestyle preferences. This can be combined with trade areas
analysis to determine locations of
new facilities.
Thinking of the typical data a business collects on its consumers
in relation to place,
many outcomes may unfold based on how the data is joined
together. Through geoenrichment,
location data, or perhaps in this case – shopping locations--can
be enriched with rich attributes
such as customer demographics, ability to pay, basket size, time
of purchase, and product
upsells. When tied to marketing and sales efforts, businesses are
now able to deploy trackable
advertising campaigns – from the moment a target audience sees
an advertising, to the

moment the audience crosses a store threshold, to the moment
the audience purchases a
product.
All marketing and sales outcomes can be measured and tied
back to marketing
campaigns by utilizing enriched location data to understand
consumer behavior. This
information is then funneled back to the organization to analyze
market behaviors. As it has
been found, market behaviors are not always fixed to regional
location, but may sometimes
shift based on proximity to similar population characteristics,
environmental surroundings, and
overall neighborhood tendencies. The processes mentioned
above can help businesses to
identify new customers, based on existing customer behaviors,
to consider to cross- or up-sell,
and predict the risk of churn. With these insights, a company
can decide how to optimize the
prices of products and/or services based on spending and
income/wealth profiles, determine
hyper-local product assortments, and develop mitigation
strategies to retain high-risk, yet
profitable customers.

Real Estate and Site Selection
Location analytics can assist in understanding current and
future business locations. One
of the significant objectives of business development is to
estimate the future sales potential of
markets. Forecasts of sales or any other business KPIs can be
modeled using statistical
regression models. Such models take into account demographic
and psychographic attributes
of sites, population density, past sales, and foot traffic, and use
them as independent variables
to explain current and predict future site performance current
and site performance.
At a more advanced level, sophisticated GeoAI-based machine
learning and deep
learning models can be used for predicting risk exposure and
related KPIs. Location intelligence
55

derived from such predictive models is critical for executive
leadership in making decisions on
capital-intensive business development projects. Such models
can also be leveraged to make
merger and acquisition decisions and to accelerate strategic
expansion in both domestic and
international markets. Advanced techniques such as Digital
Twins can create a virtual replica of
the physical entity or network, allowing for virtual simulations
and assessments. Creation of
such a virtual site that mimics the characteristics of the real -
world site can be manipulated with
varying datasets to indicate how to develop blueprints,
landscape, wayfinding, indoor
navigation, and emergency evacuation.
Operations
Operational activities rely heavily on spatial monitoring
systems. Tied closely to the
location value chain, operational intelligence allows for
businesses to understand business
needs on large-scale enterprise levels and also within more
granular segments of day-to-day
business function. Dashboards inform operations strategies,

with descriptive analytics
communicating data through mapping and visualizations.
Whereas in the past, managing
decision making of multiple business locations may have been a
more manual process, spatial
technology has enabled a new generation of business
management practices. As operational
data is geo-visualized and analyzed, operational awareness is
heightened, and this facilities
timely decisions to ensure consistent operational performance.
This data also enables predictive and prescriptive decisions to
be made. To aid in
employee safety and comply with regulations, data can be
tracked over time to predict future
occurrences. Operations managers may see safety violations
regularly occurring within a
specific time frame, within a specific area. Perhaps spills are
regularly occurring on a particular
platform or equipment is consistently damaged when accessing
a particular route, thus
lowering productivity. Spatial manipulation of data can predict
the likelihood of occurrences to
happen and can help to price out circumstantial risk. Predi ctive
analytics can demonstrate the

likelihood of the occurrence to happen again and prescriptive
analytics will inform the optimal
solution, operational decision makers can mesh spatial
techniques together to understand why
violations are occurring and how to solve the issue from
resurfacing again.
Supply chain network visualization, transparency, and product
traceability are often
informed by detailed network maps, while supply chain
resilience – an issue of great
importance in light of the COVID-19 pandemic, can be modeled
using geostatistical approaches.
Modern supply chains are complex and consist of a network of
facilities. Facility locations along
a supply chain and overall supply chain network design can be
informed using optimization
approaches which can also facilitate efficient routing and
navigation of people and assets. This
is critical for business given the rapid growth of e-commerce
deliveries. Spatial mapping and
modeling of risk for situational awareness, real-time tracking
and monitoring of assets, people,
and processes, as well as potential hazards. Business disruption
can be prevented by accurately

and spatially modeling risks, frauds, and other disruptive
events. This can help generate risk
mitigation strategies that ensure business continuity, enhance
disaster preparedness, and
ensure regulatory compliance.
56
Corporate Social Responsibility
The value of the company in the community is a critical
component to contemporary
business environments. For Corporate Social Responsibility
(CSR) elements, timely information
is needed across a broad range of areas, including diversity,
community impacts, and
environmental performance. Mapping business and community
data to tell a story of the
economic impact of the business in various locations. Diversity
data can be spatially analyzed to
track the diversity in the company relative to the surrounding
communities. Dashboards can be
utilized to understand and predict the level of corporate

environmental sustainability.
Prescriptive analysis can be done to guide efforts during a
public health crisis, such as has been
experienced during the COVID-19 pandemic.
Though aspects of CSR requires data beyond the company’s
data, when integrated he
output may be informative and groundbreaking. Spatially
understanding a business's impact on
a local community, exploring matters at the heart of employee
culture, and deploying location
solutions set to an ethical standard example, can positively
impact the sentiment in which a
business leaves on its customers, which will affect loyalty of a
brand. Spatial knowledge leads a
business to make impactful decisions, that with the support of a
community, can do great
things for all – from disruptive change to devoted customer
communities
It may feel like a microscopic lens has been placed on the
behaviors of the 21st century
business. However, this lens has forced necessary change which
has impacted the planning and
management of operations. Whether it is the changes within the

natural earth, such as climate
or event hazards, the changes in economic security, or changes
in overall resources to combat
health and human services impacts; businesses have an
opportunity to create “shared value”
solutions to these challenges.
To conclude this review of the location value chain
applications, it is important to note
that the value proposition of location analytics depends on the
breadth and depth of
applications. Broad application, spanning multiple business
priorities, and deep analysis using
sophisticated, context-appropriate combination of descriptive,
predictive, and prescriptive
location analytics is likely to maximize the value of location
analytics. This can shape decisions
and actions in different parts of an organization spanning
multiple levels of the organizational
hierarchy facilitating enterprise-wide spatial transformation.
The analytical methods underpinning these applications are key;
they may range from
simple descriptive mapping-based visualizations, smart
mapping, and geo-enrichment with

external data, to sophisticated dashboards that enable reporting,
disclosures, or organizational
deliberations. Predictive models that range from traditional time
series, regression models to
more sophisticated geo-artificial intelligence-based (GeoAI)
based machine learning and deep
learning models are increasingly popular as data science teams
in organizations mine structured
and unstructured geospatial big data to uncover and decipher
patterns and relationships
among dozens of variables. Finally, optimization models such as
location-allocation, demand
coverage, product mix, vehicle routing are informing site
selection, last mile logistics, supply
chain optimization, and related strategic, tactical, and
operational decisions and actions.
Alongside, these methods, it would be remiss to not reiterate the
critical role of location data
57
(internal and external to the organization) in framing,
developing, testing, and validating

location analytics models and approaches.
Closing Case Study: John Deere
As the global population continues to expand, sustainably
growing enough food to feed
every person on the planet is a fundamental challenge. The
challenge of increasing farming
productivity while at the same time addressing climate change
and extreme weather conditions
confronts not only farmers but also manufacturers of farming
equipment, such as John Deere.
Founded in 1837, John Deere is well known as a global
manufacturer and distributor of a
full line of agricultural, construction, turf, and forestry
equipment (Deere, 2021).
Headquartered in Moline, Illinois and operating in 27 countries
with a corporate presence in 19
US states, Deere operates 23 manufacturing plants, with more
plants overseas. Deere products
are sold by its vast network of dealers in over 100 countries
(Deere, 2021). A cornerstone of
John Deere's is advanced location analytics across its range of
products and services (Esri,

2020).
A major area of development has been "precision farming"
(Deere, 2022). Now, with IoT
sensors embedded in John Deere equipment, customers within
the global farming ecosystem
are now more connected than ever, with the ability to produce
enormous volumes of
geographic data and imagery. With John Deere equipment
primed and ready for the collection
and management of massive data streams, the company is able
to derive location intelligence
to build better products, provide customers with the tools to
streamline farming operations,
and support the farming community with sustainable
development efforts (Kantor, M., & van
der Schaaf, F.,2019, Deere, 2022a). Agribusinesss intelligence
translates to increased efficiency
and productivity to farm and cultivate crops, with attention to
granular details for process
improvement and product enhancements.
Location Intelligence for R&D in Precision Farming
As with all spatial problems, having access to timely and
relevant data can make all the

difference in running location analytics. Data as it is streamed
onto a dashboard will provide
descriptive location intelligence, but data that is collected over
time and enriched with
additional attributes, can advance agribusiness through
predictive and prescriptive location
intelligence (Esri, 2020). With John Deere integrating data
collection into farming equipment,
data collection can be automated in the field, and efficiently
managed to make better business
decisions. A connected equipment network aids customers in
planning and managing growing
seasons, from precisely planting seeds to maximizing harvest
yields (Deere,2022a). John Deere
Operations Center (Figure 3.5) is activated by field data which
can indicate overall equipment
performance and inform research and development efforts.
58
Figure 3.5 John Deere Operations Center, monitoring and

directing operations, and aiding research and development
(Source: John Deere, 2022b)
In today’s sustainable precision farming movement, farmers
need access to reliable geo-
referenced information. To gain intelligence, a variety of data is
brought into GIS to analyze and
make descriptive, predictive, and prescriptive decisions. This
data may focus on the weather
and environment. It may look at soil type and nutrients,
precipitation, groundwater level and
runoff, air pollution, and other factors (Deere, 2020) . In this,
farmers can make informed
choices to maximize limited budgets and predict change over
time across vast areas of
farmland. To help farmers georeference field data, analysts at
John Deere collect Landsat
satellite imagery that is useful for understanding changes in the
land over time. Every few days,
farmers can see, for example, if flooding due to a rainstorm has
damaged a certain crop, or
determine if additional fungicide needs to be applied in a
certain area (Esri 2018).

John Deere Operations Center then leverages immense volumes
of satellite imagery and
weather data to enrich the customer farming data (Deere,
2022b). The imagery provides
location intelligence which may then be mined using artificial
intelligence and deep learning
algorithms. As data is mined, intelligence that is uncovered may
include optimal planting time
and growing time for a given location. This intelligence may
indicate which type of crop to grow
each year and help to determine the type of farming equipment
that may be required to
produce a quality harvest. Farmers can intuitively take action
based on their location, knowing
the precise level of nutrients to put into the soil, the correct
amount of water to release, how
much fertilizer, seed, and tillage is needed and when the
optimal time is to take action. This
ensures sustainable use of resources, maximizes productivity,
and minimizes soil erosion and
chemical damage to the subsurface, thereby protecting precious
farmland for future
generations (Esri 2018, Deere, 2020).
Location intelligence also facilitates predictive maintenance of

farm equipment. Deere’s
equipment and machinery consist of parts sourced from various
plants all over the world. Its
advanced telematics systems remotely connect agricultural
equipment owners, business
59
managers, and dealers to agricultural equipment in the field,
providing real-time alerts and
information about equipment location, use, maintenance, and
performance (RPMs, oil
pressure, etc.). In the event of breakdown, locations are
determined. For example, did the
equipment break down over a steep hillside? Is a cluster of
breakdowns tied to a particular
location? In one such instance, there were repeated issues with
fuel pumps. After analyzing
location data, analysts determined that fuel coming from a local
refinery was the culprit,
adversely impacting a critical component within the pumps (Esri
2020). Accordingly, measures
were taken to service equipment proactively before failure

occurs.
Location intelligence for business development and sales
John Deere is a technology and data company as much as it is
an agribusiness company
(Deere, 2021). Using a location-scientific approach, Deere’s
data science team examines
thousands of variables during the early stages of market studies
to identify geographic areas of
potential growth (Kantor and van der Schaaf, 2019, Esri, 2020)
This includes land cover analysis
using satellite imagery, which helps decision-makers estimate
how various grasslands, crop
fields, or lawns correlate with consumer purchases in rural
communities. Ultimately, about 20
variables such as land cover, historical customer sales, income,
demographics, existing dealers,
competitive dealers, and distances to all of those features are
incorporated into an AI-driven
regression model to predict the commercial potential of US
Census blocks. The predictive AI
models also factor in market characteristics, for example, the
presence of more lawns than crop
fields in a target market, to refine sales forecasts (Esri 2020)

Location intelligence for real estate strategy and store
operations
To help dealers expand their retail footprint and build additional
dealership locations,
Deere’s data science team provides a wealth of psychographic
insight for target locations. With
products that range in price from $1,600 to $600,000, customer
segmentation is a must—both
in the private user segment (lawn and garden maintenance) and
the commercial customer
segment (golf clubs, sports facilities, etc.) (Kantor & van der
Schaaf, 2019). Psychographic
analysis is deployed to determine the lifestyles of potential
consumers so that dealers can make
site selection decisions and determine appropriate marketing
channels for their potential
customers. For instance, online marketing campaigns could be
targeted at consumers living in
higher-acreage homes in affluent areas, versus direct mail to
target customers who prefer to
pay bills in person and avoid using the internet for financial
transactions.

In addition, depending on location-based psychographic
intelligence, dealers can stock
appropriate products and product mix at stores. Because any
given Deere product line could be
arranged into tens of thousands of configurations, detailed
location intelligence regarding
consumer preferences can help Deere’s product marketing
group, sales leadership, and dealer
council steer customers towards an optimal set of product
configurations for the local market.
This can help dealers avoid inflated overhead costs due to
expansive product lines without
disappointing customers or sacrificing profits (Yunes,
Napolitano, Scheller-Wolf, and Tayur
2007).
60
Environmental and Societal Elements
Climate change is one of the greatest threats of the 21st century.
Scarce rainfall,
extreme droughts, and shrinking farmland are becoming

commonplace. Despite slowing
population growth, the United Nations projects global
population to approach 10 billion by
2050. To combat challenges to food security and prevent hunger
and malnutrition, smart
precision agriculture that is environmentally sustainable is
critical to maximize crop yield and
produce enough food while preserving the land for future
generations of farmers.
Location intelligence at John Deere is poised to catalyze
innovation in every part of the
company's value chain impacting farmers, dealers, and
consumers while transforming the
company to a techno-centric sustainable agribusiness (Deere,
2020). As farm land contracts
worldwide and farmers age, spatial intelligence is central for a
newer, younger, and
technologically-savvy generation of farmers to make sure that
their farms are operating at
maximum potential at sub-inch accuracy with optimal use of
scarce natural resources and
minimal use of fertilizer, fuel, herbicides, and pesticides.
Among other benefits, location
intelligence is expected to help the new generation of farmers

meet the challenges and needs
of the "business of modern-day farming" in which they have to
wear multiple hats - those of
brokers, bankers, chemists, agronomists, and technologists, all
at the same time (Esri, 2018). In
each of these roles, John Deere is leveraging geo-AI based
modeling approaches to help farmers
improve yield, increase productivity, lower costs, and achieve
more precision while factoring in
shifts in weather patterns and other uncertainties such as
commodity prices and grain prices,
sometimes a year or more in advance of the growing season
(Deere, 2022a).
While advancing precision farming and agriculture, geo-AI
powered innovations are
poised to assist farmers to become stewards of the land through
data-driven decision-making
and strive for a symbiotic relationship between those linked to
the land and the land itself.
Using location as a guiding principle and advanced location
analytics, John Deere is enabling
sustainable precision farming that is integrated with the
environment while driving growth and
profitability for all stakeholders.

61
LOCATION ANALYTICS
PART II
62
CHAPTER 4
Growing Customers and Markets
Introduction
Understanding customers and markets has always been a key to
business success.
Whether a firm's customers are individual consumers or other
businesses, understanding their
needs, preferences, attitudes, value propositions, priorities,

challenges, and purpose provides
insights about customers that can inform the development of a
differentiated business strategy
compared to peers and competitors.
Many of these attributes of customers are tied to location. In the
case of individuals,
where they live, work, shop, engage in professional or social
activities provides location-specific
insights to companies about their lifestyles and consumer
preferences. This is especially key at a
time when there is an unprecedented acceleration in e-commerce
growth. This has resulted in
shift from physical stores to online shopping. This digital -first
shift is impacting many consumer-
facing industries, creating competitive advantage for some,
while the laggards are left
scrambling.
When the customer itself is a business, the geographic locations
of their operations, key
business assets such as personnel, spare parts, and inventory,
critical facilities along the supply
chain (for example, warehouses and distribution centers)
relative to service and fulfillment

territories provides clues about processes and workflows,
allocation of resources, prioritization
of key business objectives, risk tolerance, and overall business
resilience. During the COVID-19
pandemic, the lack of a location-based view of business
operations, supply chain locations,
network connectivity, and other contributing factors has
exposed loopholes in business strategy
and severely disrupted business continuity in organizations
across several sectors and
industries.
Understanding Business Markets
As introduced in Chapter 2, industry clusters play a critical role
in individual business
growth as cumulated growth of the regions they operated in.
(Porter 1998a; Porter 1998b) The
Porter cluster is based on competitiveness of firms within a
geographical unit, that could be a
state, region within a state, or metropolitan region. It is
dynamic -- clusters sprout up and do
not necessarily last forever. Internal or external forces can lead
to the decline or death of a
cluster (Porter, 1998a).

Industry clusters are defined as a geographic agglomeration of
firms and institutions in a
similar economic sector, interconnected in multiple ways
(Porter 1998a, 1998b). Businesses and
institutions in clusters share infrastructure and often a shared
pool of human resources. They
relate upstream to a common set of suppliers and downstream to
related companies and
institutions, such as universities, think tanks, trade
organizations, and specialized government
63
offices. Porter’s regional cluster constitutes an agglomeration of
firms of regional, national, and
worldwide impact of such strength overall that the cluster is
considered a world leader
A variety of cluster mapping tools have been developed to help
business understand we
certain clusters are developing. This includes a cluster mapping
tool developed by the Harvard

Business School in collaboration with the Economic
Development Agency (Porter, 2021) (Figure
4.1)
Figure 4.1 Employment Specialization in IT and Analytical
Instruments Cluster across US Counties, 2016 (blue indicates
counties with high employment specialization and share, green
indicates counties with high employment specialization, and
yellow indicates high employment share)
(Source: U.S. Cluster Mapping Project, 2021)
64
Figure 4.2 San Diego’s Business Clusters, 2021
(Source: Author)

Data from this tool can be enriched by other locational data. For
example, the University
of Redlands has created an enriched map of San Diego Industry
Clusters using Esri’s Business
Analyst (see Figure 4.2). This enrichment, for example provides
more economic and community
layers to be assessed as well as more about the cluster including
data on individual firms in the
cluster.
The Porter cluster concept has bearing on spatial business.
First, spatial business
involves decision- making on locations of companies, and that
is influenced by the draw of
being located in or near an industrial cluster. Among the
reasons to do so is the marketing
benefit. Marketing may gain potency by emphasizing, for
example, “Silicon Valley firm” or
“Hollywood talent agency.” Second, for leading businesses
located in a cluster region, spatial
business marketing can be strengthened by the dynamics,
visibility, and expanded customer
target pools associated with the cluster.
Environmental Scanning

Environmental scanning is the process of obtaining, examining,
and disseminating
marketing information for tactical or strategic objectives, such
as improvement of competitive
65
position by analyzing competitor supply chains, determination
of where to offer insurance by
examining insurance risks throughout a region, and
advancement of R&D by deciding on where
and why to locate a new R&D center by assessing labor
markets. Environmental scanning can
be done once, several times, or continually. It is achieved by the
use of descriptive analytics,
often involving locational analytics, as described in chapter 3.
Companies justify environmental
scanning as providing a view of the current status of markets,
yielding information that can be
applied to company strategy and decision-making.
Figure 4.3 shows three levels of scanning: internal to the

organization, in the immediate
industry environment, and in the macro environment of external
factors and forces broader
than the industry (Kumar 2019). Scanning a company’s industry
environment includes gaining
current information on the firm’s stakeholders such as
customers, suppliers, partners, and
investors, as well as its competitors.
Environmental scanning extends to the macro environment
within which the company
does business, including political, economic, social, and
technological factors (Kumar 2019).
Scanning of all these elements can involve location.
Figure 4.3 A graphic of environmental scanning has the
organization at the central core, surrounded by a ring of the
industry

environment, outside of which appears the macro environment
of political, social, economic, technological, forces
(Source: adapted from van der Heijden, 2002)
Request this Figure to be re-drawn
Environmental scanning is also used by firms in developing
nations to expand their
customer base. For instance, in India, location-based
environment scanning is done by
companies that seek to send goods into India’s rural villages.
Dabur is the dominant world
provider of ayurvedic goods and also markets consumer product
staples, mostly in India but
also in 120 other nations. It uses GIS mapping for its
environmental scanning of Indian
demographic data from government sources and private vendors,
surveys of indicators of
66
community wealth, and data on different groups’ values,

attitudes, and behavior (Kapur et al.
2014). In this way, Dabur identified 287 well-off rural districts
in ten Indian states as having
potential for markets. Each month, the firm focuses its
marketing on a new rural district, and
deliveries are optimized by using routing features of GIS
software (Kapur et al. 2014). Dabur has
been successful in introducing its goods to rural areas, topping
its original goal of providing
service to 30,000 villages within a year and a half of
commencing marketing to the rural
districts. The initiative has benefited the firm, which now has
over 40 percent more business in
rural than in urban areas (Kapur et al. 2014).
Trade Area Analysis
Knowledge of the target market is an important precursor to
expanding the business.
Irrespective of sector or industry, businesses strive to make
important decisions on expansion
such as site selection, store layout design, merchandise
selection, customization, ability to fulfill
demand, product pricing, and available workforce, based upon
intimate knowledge of target

markets. Target markets, in turn, lead to the question of trade
areas, and how they can be
delineated, described, and modeled through spatial analysis.
In the context of business geography, trade areas define specific
market segmentation
areas and help better understand the existing or potential
customer base. What, then, are the
factors that need to be considered to determine the trade area of
a business that is considering
strategic expansion? A firm's trade area depends on the variety
of goods and services offered
by the business and also by its proximity to competitors.
Different types of businesses have
different trade areas, and customers are more likely to travel
greater distances to purchase
certain types of goods and services and/or buy online with home
delivery. In order to
strategically expand, businesses need to estimate the market or
trade area of a store or
fulfillment center at a specific site by factoring in the
geographic distribution of existing
customers, potential customers within a defined service /
delivery area that encompasses the
site, and potential competitors.

Popular and intuitive methods of delineating trade areas involve
radial distance-based
concentric rings and irregular travel-time-based trade area
polygons, as shown in figures 4.4
and 4.5. Such trade areas are based on customer spotting
(Applebaum 1966) and business's
trade area can be divided into primary, secondary, and tertiary
(or fringe) areas, which are
determined by the distance from the firm's site. From the
standpoint of a business, the primary
trade area is key. To statistically evaluate revenue performance
with respect to opportunity,
the primary trade area is thought of as the geographic core in
which at least 50 percent of the
business's customers live and work (Church and Murray 2009).
This is the area closest to the
store or center of the trade area, as measured by driving
distance or by automobile driving
time, and is expected to contain the highest residential density
of the store's customers and the
highest per capita sales by residence locations. Adjoining the
primary trade area lies the
secondary trade area, from which a store anticipates
approximately 25 percent of its

customers, followed by the fringe, or tertiary, area.
67
In figure 4.4, two restaurant locations of a quick service
restaurant (QSR) chain have
reasonably significant difference in daytime population density
in their primary and secondary
trade areas, defined by 1- and 2-mile distance buffers. Daytime
populations in the proposed
location’s primary and secondary trade areas, seen on the left,
are significantly higher
compared to those of the existing store, seen on the right, while
daytime population in the
tertiary (fringe) areas is not as significantly different.
Figure 4.4 Existing and proposed store locations are mapped,
each surrounding by 1-, 2-, and 3-mile distance rings
representing trade areas, which are superimposed on a thematic
map layer of daytime population densities
(Source: Author)

68
Figure 4.5 shows trade areas for the same two restaurant
locations, defined by 3-, 6-,
and 10-minute drive times. These drive-time buffers are
distorted in certain directions since
travel speeds in certain directions vary, depending on time of
day, or even day of the week. In
fact, in modern GIS software packages, it is possible to refine
such drive-time-based trade areas
and model them, based upon historic traffic data, for particular
times of the day and days of the
week, along with direction of travel. This can be valuable for
analysis of densely populated,
high-traffic urban markets.
Like distance-based trade areas, drive-time-based trade areas
may overlap. For
example, in figure 4.5, the 3-minute drive-time trade areas of
the existing store and proposed
location of a new store do not overlap; however, there is slight
overlap of the 6-minute drive-
time-based trade areas and significant overlap of the 10-minute

drive-time buffers. Overlap can
alert the manager of the proposed store to strong competition
and cannibalization if both
stores belong to the same company.
Figure 4.5 3-, 6-, and 10-minute Drive-time buffers are mapped
for two nearby stores - to represent trade areas that are
partially overlapping, which are superimposed on a thematic
map layer of median household income (2021)
69
(Source: Author)
A firm can approximate the customer base within each travel
time zone, for example in
terms of demographic attributes such as daytime resident
population and characterize them,
for example in terms of economic attributes such as median
household income using simple
spatial analysis functions within a GIS such as overlay, union,

intersection, querying, and
summary statistics.
Often internal organizational data, such as spatially referenced
sales transactions, or
external third-party data, such as live traffic feeds, can be
incorporated within a GIS to geo-
enrich trade area zones. Such analysis can be conducted
efficiently using GIS software, and the
resulting trade area reports, and map visualizations can provide
location-based intelligence that
informs business strategy. Also, trade areas can be compared to
each other, based on the
demographic, psychographic, and socioeconomic attributes of
customers in those areas, or
other local, state, and national geographies and benchmarks.
Such comparisons can inform and
guide senior leaders as they consider strategic expansion
opportunities in competitive markets.
Such environmental scanning of trade areas constitutes
exploratory analysis and
descriptive location analytics, often the first, foundational step
in analyzing trade areas. As the
next step, a statistical analysis using regression-based models

can incorporate ––
ch as population density of trade areas,
extent of competition in
trade areas
square footage, number of
employees, available parking, easy roadway access and signage
nomic, and psychographic attributes
of customers in trade areas
elevations
Regression models can predict the brand share of wallet, overall
sales, revenue, and
profit potential of trade areas, providing guidance for business
growth.
Trade area analysis can also play an important role in
prescriptive analysis of site
location. Consider the quick service restaurant (QSR) with one
of the existing restaurant

locations, shown in Figures 4.4 and 4.5. That QSR is interested
in opening a new location. By
factoring in business constraints such as key performance
indicator (KPI) benchmarks (for
factors such as labor costs), supply capacity constraints,
demand requirements, and local zoning
regulations, plus geographic constraints such as those imposed
by natural barriers (for example,
mountains or rivers) that impact mobility and travel times, the
QSR can optimize a specific
business objective (for example, market potential measured by
sales) and select an optimal
location for a new restaurant. Prescriptive modeling, using
operations research methods, can
aid such sophisticated optimal location modeling and is
incorporated within contemporary GIS
software.
70
Growing Customers
It is often said that the purpose of a business is to create and
keep a customer. This

section examines the customer side of business growth. Market
research and analysis is a
critical function across the value chain. The purpose of
marketing is to identify, attract, and
retain the customer, a goal abetted by today’s technologies,
which provide a multi-faceted and
continually-updated view of the customer. This is seen in
contemporary customer relationship
management (CRM) systems, in which the customer can be an
individual or a business.
Geo-marketing and Location-based marketing utilize an array of
data sets and analytics
that can be used to can a multi-faceted understanding of
customers. They provide a platform to
deliver precise insights on customer segments, interests and
location contexts. These insights
can both inform new products and services as well as fine tune
current strategies. These can
also be used to reach new markets and customers. At the same
time, some forms of location-
based marketing raise ethical-privacy considerations that
businesses cannot afford to ignore.

Market Segmentation: Geodemographics
Customer Segmenting has been a cornerstone technique in
marketing for several
decades. There are varies ways to define segments. Five
common segments are demographic,
geographic, psychographic, behavioral, and firmographic. The
first four are segments for
business to consumer (B2B) and the last about firm
characteristics for business to business
(B2B) marketing. Technological advances coupled with rapid
advance of large data sources has
allowed companies to combine elements of these segments for a
desired on-to -one business
to consumer marketing that closely connects with the
customer’s journey (Elliot and Nickola,
2021).
One approach to this integration is geodemographics. The
foundation for
geodemographics was with the 1970 US and UK censuses,
which produced, for the first time,
massive amounts of computerized information. As censuses
have improved over time, so has
the availability of accurate and extensive geodemographics, not

only for the US and UK but for
Canada and several other nations. Today, software can
characterize every census tract in the US
making it is possible to use geodemographic mapping and
further enrich it with social and
economic variables to get a more refined view of potential
customers.
There are over ten major geodemographics products in the US,
several developed in the
UK, such as Acorn and Mosaic, and others suitable for
developing nations. Commercial
geodemographic software will often have from 50 to 80
neighborhood types; Esri’s Tapestry,
has 67 distinctive neighborhood market segments, which are
estimated by cluster analysis and
other statistical methods, based largely on census data. The
segments can be mapped at the zip
code, census tract, and block group levels. An advantage of
having census tracts of 5,000–
10,000 people is that individual identity can be suppressed,
protecting personal privacy.
In the US, census data that underpins such categories is mostly
accurate but is only

collected every 10 years, so a weakness of geodemographics is
the ageing of data as the
decennial census period nears its end. The model for Tapestry is
rebuilt at every decennial
census, but the demographic balance and set of constituent
neighborhood segments change
71
yearly (Thompson 2020). Updates to the base data imply that a
neighborhood may have a
shifting geodemographic composition and dominant segment
over time.
Another example of a geodemographics tool is Acorn which
provides classification of
consumers for segments in the United Kingdom (UK) by post
code. The post code had an
average population in 2020 of 533,000 (CACI, 2020). The data
sources for the tool are open
data, government data, commercial data, and data collected by
the Acorn firm, CACI. Acorn
classifies each post code into one of 62 types, which can be

aggregated into 18 groups. Groups
in include such categories as city sophisticates, successful
suburbs, comfortable seniors, student
life, striving families, and modest means.
An example of a type is Semi-Professional Families, Owner
Occupied Neighborhoods. It
applies to 1.2 million adults, which is 2.3% of UK adults. This
type belongs to the category,
Successful Suburbs. The typical location is “found in villages
and on the edge of towns” and
“more than average of these couples are well educated and in
managerial occupations, while
the neighborhoods will contain a broad mix of people.” (CACI,
2020). The geodemographic
profiles offer a richer characterization than is possible with a
single variable. As seen in Figure
4.6, this type’s annual household income is 47,000 pounds
(32,082 dollars), which is above the
UK average, and the typical adult age range is 25-34, with two
children per household. The
profile also provides average financial, digital attitudinal,
technology, and housing information.
For instance, 42 percent of households stream TV services and
59 percent indicate “I am very

good at managing money” (CACI, 2020). This segment can be
compared with 67 other types in
the full Acorn set.
Figure 4.6 An Acorn geo-segmentation dashboard displays a
photo of a typical dwelling for the segment of semi-professional
families, as well as the segment’s indicators of the average
family financial situation, digital capabilities, and
demographics
(Source: CACI, 2020)
As a marketing tool, geodemographics provides considerable
insights to businesses and
allows for geotargeted campaigns to customer segments. At

the same time, geodemographics
72
has limitations (Dalton and Thatcher 2015; Leventhal 2016).
Specifically, in addition to the only
once-a-decade public data update, outlier residents are
obfuscated. Emphasis on the average
profile of a neighborhood misses the outliers, at either end of
the scale. This prevents the full
perception of a neighborhood. A further subtle issue with
geodemographics is
“commodification.” This means that naming and branding a
neighborhood followed by
broadcasting the profile may itself affect its composition and
changes. A neighborhood branded
and marketed as “City Sophisticates,” for instance, subtly
encourages outlier persons to leave
and new arrivals to resemble the branded image (Dalton and
Thatcher 2015).
In addition to geo-demographic marketing, businesses are
devising and implementing

more personalized, “relationship” marketing approaches.
Business may prefer to segment its
customers through its own customer feedback and survey inputs.
Another approach is to use
the firm’s internal business-transaction data and customer
relationship information to segment
its customers into “loyalty” categories. Psychographic analysis
can also be applied to
characterize the behavior and attitudes of customers, yielding an
alternative customer
segmentation. Some firms combine these segmentation
approaches: for instance, Nike
segments its customer base by demographic categories,
geographic variables such as
metropolitan/non-metropolitan, and behavioral variables that
emphasize customer feelings
about the firms’ products (Singh 2017).
Location Analytics Across the 7-Ps
For business-to-consumer (B2C) marketing, the well-known
seven Ps of marketing
apply—product, price, place, and promotion, physical evidence,
people, and process (Investopia
2019). For B2C, the seven P’s connect with location as follows:

many products and services,
adding to their value—in services such as delivery, and in
products such as cars, cell
phones, consumer- level drones, and wayfinding devices, among
others. This added
value, in turn, enhances the marketing potential of those
products and services.
or perceived tangible value.
Spatial features of the product/service can enhance or lower
value, depending on user
perception. For instance, for a wholesale store, location-based
inventory and
distribution may marginally lower cost, enabling a comparable
decrease in pricing.
choosing where to place a product
or service. For instance, some fast-food companies, including
Kentucky Fried Chicken,
employ geospatial tools to assist in physical placement of a new
outlet, considering the

locations of competitors, traffic flow volumes and directions,
signage of competitors,
and socioeconomic attributes of the area.
the product or service. It
can occur through public relations, advertising, direct
marketing, media attention, going
viral on social media, all of which can focus on getting the word
out to target markets
across geographies.
73
customers interact with
business representatives. Although such spaces were restricted
during the COVID-19
pandemic, they generally include retail stores, customer field
visits, meetings,
conferences, and other venues. For many companies, use of
Salesforce and other
customer relationship software provide tracking of physical

interactions. CRM software
can be linked with GIS software, which can then apply spatial
analytics to better
understand where a customer has “touch points” with a
company’s employees and
other channels of physical interaction with the firm.
intelligence and location analytics
in their knowledge and skillset are better prepared to conduct
marketing and customer
engagement. This enhances the company’s ability to incorporate
geographies in better
identifying, engaging, and serving the customer.
-designed process steps to
provide goods and services to
customers, and to influence the customer experience. A process
can be made faster,
more efficient, and more satisfying to the customer by including
steps that utilize
location analytics. For instance, the express delivery services by
FedEx optimize the
process of routing using location analytics which results in
faster delivery and,

concurrently, it enhances the customer- delivery inquiries by
providing the customer
with the current tracking location of the package being shipped.
The same process
enhancements occur with B2B deliveries. As a result, location
intelligence has become a
competitive aspect for local niches—as in the below example
for at- home food delivery
in New York City. This example emphasizes the P’s of Product,
Price, and Place.
FreshDirect in New York City
In the nine boroughs of New York City, citizens live in a dense
urban setting and often
prefer not to own their own personal transport vehicles. It is
difficult for many to shop for
groceries at full-sized stores, so they revert to local corner
markets and small venues.
FreshDirect was first to the market as a city-wide fresh food
delivery firm and it holds about 63
percent of the market (Boyle and Giammona 2018).
Founded in 2011, the firm ten years later has over $750 million
in annual revenue and a

400,000-foot distribution facility in the South Bronx. It
dispatches a fleet of delivery trucks that
are GPS-enabled (see Figure 4.7) and monitors the fleet through
a GIS-based control room that
serves to optimize routing and maintenance.
The market, however, heated up during the second half of the
teens decade and
following. Rearing their heads as competitors to FreshDirect in
delivery of perishable food are
Instacart, Shipt, and Amazon Fresh. The perishable competitors
currently together have 23
percent of the market and are chipping away at FreshDirect’s
lead. Instacart started up with $1
billion in funding and is taking the tactic of partnering with
large supermarket chains in the city
(Boyle and Giamonna 2018). Shipt, founded in 2014 in
Birmingham, Alabama, and now part of
Target, focuses on vetted reliable shoppers who partner with
local retailers to procure items for
74

delivery (Shipt 2020). Amazon Fresh, with huge resources, is
aggressively seeking to grow in the
fresh food market. Because New York City is so densely
populated, refrigerated delivery trucks
are not always needed, so competitors seek to deliver in under
two hours to the market of 9
million people. Spatial technologies are used by all three
competitors.
Figure 4.7. A gps-enabled FreshDirect delivery truck rounds a
corner in New York City

(Source: Krendra Drischler)
FreshDirect’s new fulfillment center includes a control center
for inventory and smart
routing of its truck fleet, kitchen facilities, nine miles of
conveyer belts, as seen in figure 4.8,
robotic order picking, and rooms set at temperatures for
different product types (Retaildi2018).
The center also is linked upstream to a distribution and
production network. All this is
calibrated to satisfy projected daily market demand (Wells,
2018).
FreshDirect exemplifies how the P’s of Product, Place, and
Process are affected by
location analytics. Location is embedded in the Product’s
servicing, i.e. location-based home
delivery. Place concerns the location intelligence determining
the location of the fulfillment
center and of the geographic boundaries of the service area.
Process is the supply chain process
which includes the location-based navigation steps in B2B
delivery of food by suppliers to the
fulfillment center, and following sorting for at the center, the
B2C location-based navigation in

delivery to the customer.
75
Figure 4.8 A photo of FreshDirect’s automated order fulfillment
facility shows automated conveyers, which sort inbound
logistics supplies for placement on gps-enabled delivery trucks
(Source: Hiruka Sakaguchi, 2021)
Amazon Whole Foods constitutes the most powerful
competition, with automated
fulfillment centers, offering New York customers two-hour
delivery times through Instacart.
There are many dimensions of competition between these rivals,
one of which is competing in
spatially driven delivery, which depends on integration of each
rival’s fulfillment center with
inbound distribution networks of available foods. Ultimately the
customer will make the
decision, and in New York customers are especially hard to
please. Effective marketing, assisted
by knowledge of customer locations and tastes in this densely

distributed and sophisticated
customer base, is crucial.
Location-Based Marketing
“Rapidly increasing digitalization across industry verticals,
growing penetration of
internet & GPS enabled mobile devices, and increasing
utilization of consumer data by
marketers are the primary factors fostering growth in Location
Based Advertising” (LBM) to
become a $62.35 billion global industry (GrandView Research,
2020, p.x). Location-based
marketing is the strategy that matches opted-in, privacy-
compliance location data received
from smartphones to points of interest such as restaurants,
grocery stores, and shopping malls.
Marketers then use this data to create location-based audiences
and analytics. Marketers
create and reach their desired audiences in order to serve them
more relevant advertising and
content (Handly, 2019).
76

The three main components to location-based marketing are
geofencing, geotargeting
and geoconquesting (Handly, 2019).
Geofencing allows the marketing company to assign boundaries
for a geographic area
where customers of a certain type are expected to visit. Once
the customer is within the geo-
fenced area as measured by her cellphone, she will receive
consumer marketing messages that
are keyed to the expected type of customer for the area. An
example would be for a customer
who enters a geofenced area for auto dealerships. While in the
area, the consumer will receive
marketing messages related to the automotive industry and
related concepts, in the example,
messages for a car brand, car accessories, local travel, vehicular
services, or car insurance.

Geotargetting refers to doing marketing based on geo-
demographic characteristics of an
area, a topic discussed earlier. For instance, consumers in a
geographic area in the UK with the
geodemographic profile shown Figure 4.6 could be geo-targeted
by advertising for moderately
priced, financial management software. Consumers in a zone
that has the geo-demographics of
older, retired people who own their homes, could be geotargeted
for newspapers, hearing aids,
walkers, or in-home healthcare services, and home repair
services.
Geoconquesting is trying through spatial technology to pry
away a competitors’
customers. This would consist of targeting customers who
geographically can be identified as
visitors or nearby to competing firms’ stores and, consequently,
attracting those competitor-
customers to an their store location. For instance, a fast-food
retailers have put a geofence
around its nearby competitors and once the customers enter the
geofence, send them special

offers for food at reduced pricing to try to snare them away.
Benefit of location-based marketing
A company benefits through location-based marketing by (a)
increased access to market
to customers in or nearby their stores, (b) capability to decide
on the best geographies for its
products or services and target-market those geographies, and
(c) market to competitors’
customers with offers to draw them away to become a
company’s own customers. At the same
time, customers may benefit by receiving mobile advertising for
products that are likely to be of
interest and by being alerted to shopping or service
opportunities nearby their locations
throughout the day.
Location-based “on demand” marketing is a type of marketing
defined as the use of
locational knowledge for marketing efforts. It can involve the
internet, mobile devices, and
social media, as well as enterprise analytics and desktop/server
platforms accessing customer
CRM and other data.

77
Location-based marketing has the following goals:
-to-date digital marketing
information.
geographical units.
social media, subscriptions,
memberships, loyalty cards, text mining, and web mining to
increase marketing success.
stimulate marketing progress
and identify what channels are used by which customers to buy
through.
The approach used in location-based marketing depends on the
goal and customer type.

Customers can be typified as local or distant, high or low in
extent of internet and social media
use, and primarily mobile-user or not. In its smartphone app, for
instance, Burger King targeted
mobile users who were within 600 feet of a McDonald’s (Kantor
2018a). A Burger King
customer located inside this McDonald’s radius and having a
smartphone with location turned
on was notified that she was able to order a Burger King
Whopper for a penny and directed to
the nearest Burger King outlet. This marketing approach has
been effective in diverting many
customers away from the competitor.
Location Based Social Media Marketing
Social media has become more and more location enriched, for
purposes of marketing
as well as for data-sharing, customer tracking, and varied
business analytics. Several types of
social media, such as social networking, collaborative projects,
blogs and microblogs, content
communities, and virtual social worlds, include the use of
location.

Locational social media has varied time lags. If it is time
sensitive, it can rapidly inform
spatially referenced decisions. An example is rapid check-in to
a location in Foursquare, read
synchronously by others. If it is not time-sensitive, the message
has a locational tag and is read
asynchronously (Kaplan 2018). By contrast, some messages are
neither time- nor location-
sensitive—for example, reading an article on a mobile device.
Since social media is mostly accessed on smartphones, the
smartphone’s location
becomes a source of information that can be tagged on social
media, which allows others to
identify the location of the smartphone and, by proxy, the
sender. On the positive side,
locational tagging can benefit the user with useful information
about nearby friends, places,
products, and services, or allow her to receive emergency
notifications. It can also help in
monitoring the locations of children or the disabled. With smart
phones now expanded
worldwide, these tradeoffs are becoming more salient and have
led to regulation of locational
privacy in EU countries, and, by contrast, to control of content

in China and other countries.
Social media on smartphones serves as a major source of
location-based marketing
information, such as the location-tagged commentary that
TripAdvisor and other travel apps
receive. Also, in some cases, smartphone users voluntarily act
as location-based sensors on
78
social media, email, and texting (Ricker 2018). An example is
volunteered geographic
information (VGI), a form of crowdsourcing in which a citizen
acts as a sensor by identifying a
phenomenon at a location and communicating it, adding that
information as a map point. VGI
has been used in emergency situations, such as hurricanes and
wildfires. In terms of the spatial
business of marketing, VGI can be a means to build geo-
referenced datasets when technical
means are too expensive or not yet smart enough. It has been
used more in government than

in business but has potential to grow for gathering specialized
marketing data.
Heineken’s use of social media for a marketing campaign
Heineken sought beginning in 2016 to market its beer brand to a
target of 21- to 26-
year-old millennial men, who provided real-time suggestions of
nightspots that were trending
in their cities. A guy could engage with this service,
@WhereNext, via Twitter, Foursquare, or
Instagram. The digital platform was a response to Heineken’s
research showing that its young,
male consumers felt they were missing out on nightlife by not
being informed. The Twitter-
based service sorted and ranked nightspots by aggregated geo-
referenced tweets, Instagram
photos, and Foursquare check-ins to provide prioritized
suggestions (MMAglobal.com 2019).
The campaign expanded public perception of Heineken as being
hip with social media and
youth.
The service allowed followers to have a stream of nightspots in
15 major cities

worldwide. The data- gathering work was outsourced to social
media users on a voluntary
basis, and a complex algorithm was developed by the outsourcer
R/GA London to support the
sophisticated social media service. It was “able to discover new
venues, popups, and parties,
which may never have been found via traditional sources”
(MMA Global 2019). The highest
priority nightspots were summarized in a map form, as seen in
figure 4.9. This application was
successful as a campaign highlighting the Heineken brand as
contemporary and appealing to
millennials. It also went along with a broader “Cities of the
World” campaign of the company at
the time.
79
Figure 4.9 A diagram of a city’s downtown that illustrates five
prioritized @wherenext nightspot locations (A - the best -
through E) relative to a social media user shown in the center.
surrounded by distance perimeters; locations of two more
distant nightspots are shown

(Source: Author)
REQUEST FIGURE 4.9 TO BE RE-DRAWN; INSTRUCTIONS
FOR ILLUSTRATOR -- Please put a city street map
as the "base map" underneath these locations. It does not matter
which city is used and the city will not
be identified. Alternatively, the illustrator could draw a
fictional street map as the "base map." Do not
show the layers, just a fictional map.
A different type of locational social media focuses on social
networking, i.e., using social
media to arrange for meet-ups of family and friends, or for
business and marketing meetings.
These meet-up services include Foursquare and Meetup LLC,
which is now a part of AlleyCorp.
Meetup is website oriented and focuses on people arranging
group meetings and events.
Foursquare, officially Foursquare Labs Inc., has two offerings,
Foursquare City Guide,
which focuses on its customers searching and finding thei r way
around cities drawing on ideas
from an online community of users. Foursquare Swarm is an

active community where users can
check in to a location and meet up with friends or business
associates, even maintaining a
lifelong log of check ins.
Mining locational social media provides a remarkable picture of
what topics people are
interested in. This goes beyond what is possible with
geodemographics, which reflects average
sentiment of a group, rather than of the individual. In one
example, researchers studied the
80
tweets of London Underground passengers while underway.
This study was possible because
the Underground has its own Wi-Fi system, conveying tweets
that are geotagged and time-
stamped (Lai et al. 2017; Kantor 2018b). The goals of the study
were (1) to understand the
dominant topics of tweets at locations throughout the
Underground system, including hourly
patterns throughout the day, and (2) to be able to relate these

topics to the outdoor advertising
appearing near the station exits.
The study showed that popular topics change as an underground
passenger moves on a
line from the Underground in the center of London to its
peripheral arms (see figure 4.10). In
the central stations, dominant topics are Social and Business,
Food and Drink, Sports and
Tourist Attractions. Topics in the intermediate areas include
Movies and Shows, while at the
end of the Underground network’s long arms, stretching up to
20 km from the center, topics
tend toward Work and Home. Dominant topics also relate to
neighborhood sites—for example,
the sports topic dominates in the two stations near Wembley
Stadium, while topics about
museums and galleries dominate around the station areas near
the Science Museum, the
Victoria and Albert Museum, the Natural History Museum, and
the Tate.
Figure 4.10 A map of downtown London’ underground station
locations portrays, for the city’s areas of egress of subway
passengers, the dominant social media topic of the passengers

(Source: Lai et al., 2017)
Hourly patterns of tweets for weekdays reveal a complex pattern
of changes in tweet
interest from hour to hour, and between weekdays and weekends
(Lai et al. 2017). This space-
81
time social media monitoring approach differs from
geodemographics in capturing the ideas of
an individual and “could become a critical element in measuring
and improving the
effectiveness of future out-of-home advertising” (Kantor
2018b). Recent reports indicate that in
the US, huge numbers of consumer daily pathways are being
monitored and recorded not only
by well-known tech giants such as Apple and Google but also
by specialized providers that sell
the information to other companies, activities that are legal as
long as the consumer assents to
her location being turned on (Valentino-DeVries et al. 2018).

The ethical aspects of this type of
data collection are debatable.
Privacy Issues Related to Markets and Customers
While there is rapid growth in location-based marketing, there
are also ethical issues
associated with its use. This section expands on the challenge
and need to maintain location
privacy. Prominent among them is customer privacy. The
consumer included in a location-
based marketing database might have little or no knowledge that
her personal information is
included and thus lose control of this data and the purposes for
which it is used. A sub-industry
has developed in the US that sells location-based databases of
potential customers. The sub-
industry enterprises mostly strive to maintain the personal
privacy of the information. A related
issue is the challenge of protecting the security of locationally
private information. Hackers
have broken into databases of private companies containing
customer addresses and even into
US federal government databases, stealing personal information
that they can geo-locate.

In the US, there is no federal legislation to uniformly protect
personal privacy
information (PPI) with associated geolocation data (Boshell
2019). However, some states,
including California, Massachusetts, New York, Hawaii,
Maryland, and North Dakota, have put
laws into effect that restrict access to PPI for extraction and
selling (Green 2020). In California, a
business is not permitted to sell a consumer’s PPI without
giving visible notice and allowing the
consumer to opt out. Also, US federal law has specialized
restrictions on PPI for certain sectors,
such as healthcare (HIPAA regulations) and data of the US
Federal Trade Commission (Boshell
2019).
While the question of protecting personal information and
locational data is being
debated in US courtrooms and boardrooms, the European
consumer already has greater
protection. Under Europe’s General Data Protection Regulation
(GDPR), passed by the EU
Parliament in April 2016 and put into effect in May 2018,
personal information is protected

unless the consumer opts in. Privacy incursions without prior
assent are subject to large fines.
Social media tagging raises ethical questions because the user
might not be aware that
it has taken place (Angwin and Valentino-DeVries 2011). The
exposure of an individual’s
location without her consent is a violation of personal privacy,
as discussed in chapter 4. And
even if the user is aware of the tagging, social media and
technology companies may still share
and monetize the data (Valentino-DeVries 2018a).
Other ethical concerns are highlighted in a study of the use of
the Foursquare app for
locating retail outlets in Kansas City, Missouri. The study found
that only about a third of Kansas
82
City’s 2,668 accommodation and food outlets were available on
the app (Fekete 2018). The
deficits in access represent a digital divide and raise questions

of corporate social responsibility.
Nonetheless, social media and GIS are converging, not only for
a variety of personal
uses, but also for business marketing purposes, as social
becomes a larger advertising and
marketing channel. Although dynamically changing, location as
part of social media is here to
stay as a potential source of locational value.
Closing Case Study: Oxxo Mexico
Oxxo is a convenience retail chain in Mexico, with thousands of
stores all over the
country, and increasing market penetration aided by rapid
growth, not only in Mexico but also
in South America (Chile, Colombia, and Peru). Oxxo’s 18,000
stores and gas stations (2018) in
Mexico and South America are part of a vast network of stores
and gas stations. Oxxo served
120 million customers in Mexico and the company employed
approximately 225,000 people in
stores, gas stations, and distribution centers in 2018. An Oxxo
store (shown in Figure 4.11)
carries an average of 3,200 SKUs such as food, beverages,

mobile phone cards, and cigarettes
(FEMSA 2018). With the acquisition of fast-food restaurant
chains such as Gorditas Doña Tota
and the introduction of financial and payment services, Oxxo’s
store-level business has
diversified over the years to cater to customer needs for
convenient, efficient shopping
experiences.
Figure 4.11 A photo illustrates the typical storefront of an Oxxo
convenience store in Mexico
(Source: Author)
Understanding markets and customers
Starting in the early 2000s, Mexicans increasingly shopped at
convenience marts on
their way to and from work. This trend reflected a rise in two-
income households as well as
increasing traffic in densely populated urban areas. As
consumers became more time-poor,
83

they demanded convenience and flexibility in their shopping
experiences and were drawn to
bright aisles, longer hours, and varied product selections in
convenience marts, compared to
mom-and-pop corner stores or street concessions.
Location intelligence has been at the core of Oxxo’s expansion
and sustained growth.
Spatial thinking at Oxxo stems from the need to continue to
enhance its value proposition—
provide proximity, accessibility, and convenience to its
customers. With GIS, Oxxo’s location
intelligence team conducts demographic and psychographic
analysis of its markets to better
understand its customers and drive decisions on the type of
store to open in its trade areas.
Location differences drive a differentiated retail approach
Store segmentation is an important strategic function of GIS-
based location intelligence
at Oxxo. For example, Oxxo uses GIS to map population
densities, income, traffic patterns and

directions, the rate of local car ownership, and shifts in
demographics in the new markets
(Elliott 2019). This helps executives decide if small
convenience stores for on-the-go purchases,
larger outlets similar to grocery stores, or stores that combine
both formats are appropriate for
certain markets. In other words, based on location differences
between markets, Oxxo
executives decide the type of store appropriate for a market.
Location analytics provides Oxxo’s real estate and expansion
team with location
intelligence on sites previously deemed unprofitable, for
example, niche stores in smaller
spaces at airports, train stations, and other such locations
(Sandino, Cavazos, and Lobb 2017b).
As GIS drives store segmentation depending on market
conditions and differences, product
placement in stores is optimized and appropriate SKUs are
introduced, depending on local
consumer preferences, yet another manifestation of location
differences and location context.
In addition to store segmentation and product customization,
location differences have

informed Oxxo's differentiated retail approach for its store
locations. Location strategists at
Oxxo realized that, as much as on-the-go customers visit Oxxo’s
stores to take advantage of
one-stop convenience—for example, to purchase a quick drink,
grab a prepared meal, or buy a
household product—they would value additional services.
Accordingly, Oxxo introduced
services such as diverse banking, by partnering w ith ten
banking institutions; cash remittances;
in-store bill payment of phone and electric bills; prepaid gift
cards for streaming online services;
and replenishment of calling cards. By 2016, 70% of daily cash
at Oxxo stores came from
financial and payment services, with the rest from sale of
merchandise (FEMSA 2018). Oxxo has
also provided a solution to Mexican consumers for whom there
are significant barriers to online
shopping, by entering into a “click and collect” partnership with
Amazon that allows customers
to securely pick up their Amazon packages at their local Oxxo
store (FEMSA 2018).
Location intelligence drives business expansion
The first Oxxo store opened in Monterrey, Mexico, in 1978. By

the year 2000, the
company had almost 1,500 stores in Mexico, which ballooned to
10,600 stores in 2012, and
finally 18,000 stores in Mexico, Peru, Chile, and Colombia by
2018, when it became Mexico's
largest retailer. On average, a new Oxxo store opens every six
hours, and the company plans to
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responsibly expand its retail footprint by opening approximately
1,300 new stores per year,
with a goal of 30,000 stores by 2025.
Each Oxxo store is part of a geographic and strategic unit called
a Plaza. In 2020, there
were 52 such Plazas, each of which has an expansion team of 5–
6 people, led by an expansion
manager, responsible for the performance of stores in its trade
area. The expansion manager
collaborates with hundreds of field workers as fieldwork is an
important component of Oxxo’s

expansion strategy. Fieldworkers provide expansion managers
valuable guidance about local
needs and business conditions. As part of its workforce, Oxxo
employs “brokers,” who collect
information on potential sites for stores using mobile data
collection apps. This authoritative
data is uploaded to form layers in Oxxo’s GIS and ultimately
used at the operational level at
Oxxo’s Plazas.
Business expansion at Oxxo is informed by location intelligence
- particularly spatial
statistical analysis. Oxxo’s GIS includes spatial layers of
locational data on various demographic
and socioeconomic attributes, and complementary or competing
businesses such as grocery
stores. Other layers include hospitals, schools, malls, and other
generators of business activity
(location linkages) that are part of the trade area (usually 300
meters from an existing/planned
store location). As a whole, these layers provide location
context and the foundation for
forecasting models of sales potential of an existing trade area
(figure 4.12), generate sales
forecasts, comparison sales potential of potential market

opportunities, and assess the risk of
cannibalization. Ultimately, using location analytics, executives
at Oxxo are able to make faster
decisions about store openings in new markets.
Figure 4.12 A mixture of images portrays Oxxo’s location
analytics, such as maps of drive times and store segmentation
and a
screen shot of a user request for store characteristics
(Source: Esri, 2019)
85
Location intelligence at Oxxo: The future
Oxxo’s sustained growth has been marked by an intimate
understanding of the Mexican
convenience retail landscape and customers’ wants and needs.
From the beginning, proximity,
accessibility, and customer service have been hallmarks of
Oxxo’s value proposition. To remain

flexible and adapt to local customer needs, Oxxo’s department
of expansion and infrastructure
has prioritized store segmentation, product customization, and
an expansion strategy backed
by authoritative data and location analytics. Senior leaders of
Oxxo are strategic consumers of
GIS, driving Oxxo’s continued expansion within Mexico as
well as growth in Latin America. Oxxo
aspires to diffuse GIS adoption and usage more broadly across
the enterprise in departments
such as supply chain, for integrated management of a vast
network of suppliers. Although GIS
adoption and use at Oxxo is not yet enterprise-wide, Oxxo is
positioning GIS to support
management of digital transformation and enhance its value
proposition to customers as
consumer preferences continue to evolve in light of the COVID-
19 pandemic and its aftermath.
86
Chapter 5

Operating the Enterprise
Introduction
Operations is an integral part of an organization's value chain
that is responsible for
producing goods and/or delivering services. The creation of
goods or the delivery of services
requires support and inputs (for example, labor, capital, and
information) from other
organizational functions. The inputs are transformed to generate
outputs (the good or the
service itself). During the transformation process, value is
added by different operational
activities such as product and service design, process selection,
selection and management of
technology, design of work systems, location planning,
facilities design, to name a few.
Intrinsically linked with supply chains, the need to improve
business operations stem
from competitive pressures to offer an expanding array of new
products/services to customers,
shorter product development lifecycles, increased demand for
customization, increasing

customer reliance on e-commerce, and improving the resiliency
and transparency of supply
chains that are prone to risks posed by climate crises, economic
uncertainties, unsustainable
and sometimes unethical business practices.
Given the centrality of the operations function in the
organizational value chain and its
interdependence with supply chain management, this chapter
provides an in-depth overview of
the role of location intelligence to inform decision-making
relative to "operating the
enterprise", beginning with operational considerations and then
broadening to supply-chain
considerations.
The remainder of this chapter is organized into five main
sections. Each section explores
in-depth the role of location intelligence to –
-time situational awareness,

rent supply chains and logistics
systems.
Real-time Situational Awareness
Location intelligence is critical for situational awareness - "the
perception of the
elements in the environment within a volume of time and space,
the comprehension of their
meaning and a projection of their status in the near future"
(Endsley, 1988, p. 97). In the
context of business operations, it is essential to know what is
happening when and where. With
87
distributed networks of assets, facilities, and infrastructure,
breakdowns can happen anywhere,
any time, and in many economic sectors, most of the workforce
may be mobile. Organizations

in sectors such as transportation, logistics, utilities and
telecommunications, to name a few,
need to have real-time knowledge of the locations, condition,
risks, and performance of their
assets to improve decision-making, particularly in emergency
situations. Real-time tracking and
monitoring of asset location and condition can also improve
productivity, prevent breakdowns,
ensure safety, and reduce costs.
GIS-powered spatial platforms provides a holistic visual
overview of the performance of
a system—people, assets, sensors, devices, and other internal
assets—which may be affected
by external factors such as weather, emergencies, or network
and technology disruptions.
Using dynamic maps, apps, and dashboards, firms can track
movements and changes within a
system in real time and ensure that both field personnel and
office staff use the same
authoritative data. This can help an organization boost data
accuracy, reduce errors, adopt
automated workflows, and improve efficiency.
Location intelligence can also provide guidance for the dynamic

navigation of field
assets (people and vehicles), reducing travel time and ensuring
that service time windows are
honored. In the event of breakdowns or emergencies, location
intelligence can reroute drivers
and vehicles, ensure safety, and maintain timeliness of
operations. Beyond navigation, location
intelligence can help trigger predictive maintenance or
interventions such as reducing the
temperature of mobile trucks transporting perishable goods or
medical supplies.
Technologies such as AR and IoT-based sensors and devices
complement location
intelligence in providing real-time situational awareness. Using
IoT-based sensors mounted on
infrastructure and assets, both above and below ground,
additional streaming data may be
generated on KPIs of assets. All this data can be managed,
organized, and geoenriched within a
GIS for visualization and analysis to provide situational
awareness in real time (Radke, Johnson,
and Baranyi 2013).
Real-time situational awareness at an Electric Utility

Sulphur Springs Valley Electric Cooperative (SSVEC) is a
distribution cooperative
providing electricity to consumers measured by more than
52,000 meters over 4,100 miles of
energized line in southeastern Arizona. The cooperative’s
service territory covers parts of
Cochise, Graham, Santa Cruz, and Pima Counties. Apart from
cities such as Tucson AZ in Pima
County and Sierra Vista which is a medium-sized city with
urban and ex-urban areas, other parts
of SSVEC’s service areas are largely rural and include meters
that serve agricultural areas. As a
medium-sized not-for-profit entity with 175 employees,
SSVEC’s annual revenues, generated
predominantly via sale of electricity, have grown steadily at 2-
3% per year in recent years.
SSVEC’s highest annual load in a year is a moderate 250
instantaneous megawatts.
Agricultural meters account for 50% of SSVEC’s electricity
sales, another 45% is
residential. The balance is commercial, industrial, other use
categories. SSVEC’s service area in
southern Arizona's high desert (about a mile above sea level)

has high soil quality making the
88
region attractive for agriculture, but it also needs a lot of water.
In these agricultural areas, the
primary use of electricity is to power irrigation equipment
In the nineties, engineers and technicians relied upon paper
maps to guide field
operations. For almost a decade, service technicians utilized
map printouts to navigate to
customer locations, and find poles, transformers, and meters for
routing and emergency
maintenance and repairs. The first major GIS initiative in 2003
centered on creating an
exhaustive inventory database of field assets to reduce response
times during power outages.
This was especially critical for field crews deployed to conduct
repairs in the middle of the night
when it is hard to locate power lines located 1,000 feet off a dirt
road in a mountainous area. In
2008, SSVEC’s crews started using tablet computers and

subsequently mobile devices to access
digitized maps when conducting field maintenance and repair.
Presently, SSVEC’s enterprise GIS, equipment at any given
moment, has approximately
1,000 open work orders that reflect some change to the asset
infrastructure and power
systems. Some of these involve installing IoT-enabled sensors
on critical assets that stream
geotagged systems performance data in real time. The
company’s operations managers
monitor this “health data" of company assets in real time at
SSVEC’s command center using the
Line Patrol Dashboards (Figure 5.1). In other cases, manual
intervention is needed to monitor
the health of assets such as wooden power poles. When put in
the ground, wooden poles have
to be monitored for rotting on a cyclical basis. This work is
outsourced to a third-party
contractor, which inspects the locations of SSVEC’s power
poles.
The dashboard in figure 5.1 is used by the company to plan
overhead pole inspections. It
shows locations of over 4,000 poles in a part of SSVEC's

service area by pole type along with the
outcomes of inspections (percent passed versus percent failed)
conducted over the past month
and year. This provides the company a simple comparison. If
there is an uptick in failed
inspections for a given month compared to the past year,
underlying root causes can be
examined along with their location characteristics (for example,
if the poles failing inspection
are predominantly in one part of the transmission network). In
addition, the dashboard shows
The number of poles that require inspection are shown along
with their type (light poles,
primary, and secondary). Depending on pole type and their
locations, inspections can be
scheduled and inspectors with the right skills can be assigned
by SSVEC.
Based on the inspections, SSVEC updates the enterprise GIS
layer of assets to reflect
whether a pole is serviceable or needs to be replaced. If
replacement is needed, the enterprise
GIS system automatically produces a work order and notifies
the engineering team. Armed with
mobile devices (Figure 5.2 shows SSVEC's mobile iOS app for

field inspections), repair crews and
service technicians see repair orders and inspection status,
conduct repairs, and update work
orders from their respective field locations. Back at the
company command center, supervisors
see updated status of equipment and work orders in real time
and make adjustments in repairs
and technician schedules as needed. Among other benefits, this
helps SSVEC reduce overtime
and optimize its deployment of service crews. Armed with
spatial intelligence that originates in
the field, decision-makers in various units leverage SSVEC’s
enterprise GIS to seamlessly
automate the predictive maintenance, repair, and customer-
service decision-making processes.
89
This reduces system failures, manages service inefficiencies,
and improves customer
satisfaction.
Figure 5.1 SSVEC's Line Patrol Dashboard, providing a real-

time view of outcomes of inspections of critical transmission
infrastructure along with facilities awaiting inspection
(Source: SSVEC)
Location intelligence for life cycle repairs of electric poles has
an important secondary
benefit for SSVEC. Because its infrastructure has demonstrable
reliability, telecom companies
partner with SSVEC and enter into “joint use attachment”
agreements to attach their telecom
assets and equipment to SSVEC’s poles. These “attachment”
locations are also monitored by
SSVEC using its enterprise GIS, which allow additional revenue
capture to help offset the cost of
maintaining the pole infrastructure.
The next planned phase of location analytics innovation at
SSVEC is real-time location-
based intelligence from the field using a distributed network of
IoT-enabled sensors. Outage
management is an innovation which allows SSVEC to take
phone calls and out-of-power meter
messages as inputs to a predictive analysis of where the outage
is happening. An improvement

over sporadic phone calls from customers, outage management
systems use GIS-maintained
network graphs to identify locations where crews are needed.
Dispatchers manage the lifecycle
of an outage from identification, verification, and repair
through to restoration. When the
outage is verified and later restored, SMS messages are
automatically sent to affected
customers to communicate the incident status and provide better
customer service.
90

Figure 5.2 SSVEC's Mobile iOS App for Field Inspection,
showing inspection status of a particular pole along with the
option
of reporting a failed facility
(Source: SSVEC)
Besides the utilities sector, similar needs arise in other asset-
intensive industries such as
telecommunications, oil and gas, and transportation and
logistics. However, the business need
for dynamic monitoring of system performance transcends
industry verticals. GIS coupled with
other technologies and data such as IoT-based sensors, drones,

augmented reality, mixed
reality, radio-frequency identification (RFID), and machine
learning can provide sophisticated
real-time geotagged data and locational insights of considerable
business value for operational
as well as tactical decision-making in close to real time.
91
Monitoring operations KPIs using Dashboards
Monitoring the fluctuations of operations Key Performance
Indicators (KPIs), often in
real time, is critical for business continuity. Observing and
analyzing the spatial variation of KPIs
such as raw materials availability, supply capacities, demand
requirements, inventory levels,
stockouts, manufacturing productivity, system efficiency,
operations startup and shutdown
times is central to efficient business operations and suppl y
chains. Diagnosing spatial patterns
of customer service needs, including service outages, equipment

breakdowns, customer service
complaints, quality issues, and on-time deliveries improves
customer satisfaction and
consequently customer retention.
Operations managers face the challenge of continually
monitoring system performance.
In a manufacturing setting, this could entail monitoring
operational Key Performance Indicators
(KPIs) such as:
, process
efficiency, capacity
utilization, scrap rates),
Maintenance, startup and
shutdown times, breakdowns),
(supply capacities, demand
requirements, inventory levels, and stockouts on a plant-by-
plant basis), to name a few.

closely tied to operations include:
-
time repairs and
resolution rates, incomplete work orders and their reasons,
extent of delays, on-time
arrivals and deliveries),
product, channel, and market,
sales versus targets), and
Service (Customer loyalty, customer churn,
customer complaints, and Net
Promoter Score), among others.
Like manufacturing KPIs, service KPIs need to be tracked on a
location-by-location basis
to analyze spatial patterns and trends. To track KPIs as well as
for monitoring performance and
reporting purposes, businesses increasingly rely on dashboards.
With the rapid adoption of data
science in both public and private sectors, dashboards have
become ubiquitous and ranked as
the highest-rated type of business-intelligence technology use

(Dresner 2019). Increasingly,
dashboards incorporate a location component to examine
geographic patterns, variations, and
trends over space and time.
In the previous section, SSVEC’s line patrol dashboard was
described. It enabled the
electric utility to monitor the performance of critical assets and
perform predictive
maintenance. A node utilization dashboard (see figure 5.3) for
an internet services provider in
the greater Tampa, Florida, has a chart of real-time performance
of nodes, i.e. devices actively
92
connected to its wireless network. Maps display the provider's
market area including Tampa’s
international airport, railroad facilities, a US Air Force base,
the University of South Florida, and
a variety of businesses, tourist hotspots, and residential
communities. The maps, charts, and
graphs on the dashboard provide operational insights into

historical average bandwidth
capacity over the previous 12 weeks.
With approximately 800 nodes servicing this major metro
market, operations managers
need to closely monitor average bandwidth capacity, compare it
to demand, and quickly
pinpoint any service nodes experiencing issues that might
disrupt service. Maps in the top layer
of the dashboard provide detailed locational insights on
capacity utilization at various units of
geographic resolution. By clicking on any red dot in the node
performance scatterplot, an
operations analyst is able to pinpoint a node experiencing
service issues and connect it to a
trouble ticket on the bottom set of maps in figure 5.3, so that
field crews can be deployed. Also,
by monitoring geographic fluctuations in capacity utilization—a
key operational planning KPI—
managers can decide to split nodes into sub-nodes for areas
experiencing spikes in demand and
predict trouble hotspots ahead of time. Among other things, this
can affect resource allocation
planning, prevent outages, and ultimately improve customer
service.

Besides the utilities sector, similar needs arise in other asset-
intensive industries such as
telecommunications, oil and gas, and transportation and
logistics. However, the business need
for dynamic monitoring of system performance transcends
industry verticals. GIS coupled with
other technologies and data such as IoT-based sensors, drones,
augmented reality, mixed
reality, radio-frequency identification (RFID), and machine
learning can provide sophisticated
real-time geotagged data and locational insights of considerable
business value for operational
as well as tactical decision-making in close to real time.
Distribution System Design
The design of an efficient distribution system comprised of a
network of facilities is a
strategic challenge for many businesses. An efficient
distribution system allows businesses to
meet the needs of their customers, for example deliver products
and services in an e-
commerce setting within tight delivery time windows,
strategically maintain inventory levels,

manage service level agreements, and also reduce impacts to the
environment, for example in
transportation and logistics settings. Integral to the design of an
efficient distribution system is
the strategic location of facilities that are going to serve
customers and allocating customers to
those facilities.
Facilities Location
A critical aspect of designing an efficient distribution system is
to make optimal facilities
location decisions—for example, locating a manufacturing plant
and a set of stores of a retail
business to accomplish a desired objective. Such objectives may
include maximizing market
share, maximizing demand coverage, minimizing transportation
or shipping costs, and in some
cases, minimizing the number of facilities.
93

Figure 5.3 Operations Dashboard of Broadband Service Provider
Showing Average Bandwidth and Average Capacity
Utilization, Tampa FL
Consider a manufacturer that wants to build a set of warehouses
to supply or stock
stores in a target market so that distribution costs are
minimized. Two sets of decisions are
involved: (1) where to locate the warehouses, and (2) how to
allocate demand originating from
stores to the warehouses. This combination of location and
allocation arises frequently in
supply-chain contexts and has been formulated as an
optimization problem, classically known
as the location-allocation problem (Cooper 1963). The general
planning problem may be stated
as follows:
94
Locate multiple facilities in a service area and allocate the

demand originating from the
area to the facilities, so that the system service is as efficient as
possible (Church and Murray
2009).
In a typical location-allocation scenario, some candidate
locations are pre-selected and
pre-specified. For example, when designing a distribution
system as a location-allocation
problem, a set of location-specific criteria such as local rent and
taxes, workforce availability,
labor costs, and distance from highway can help to identify
candidate locations, from which the
optimal ones can be selected, based on demand efficiencies.
Figures 5.4 and 5.5 illustrate the application of location-
allocation modeling to
determine new store locations of a retailer with one existing
store in the San Francisco market.
In the risk-averse scenario, with business expansion budget
restriction in mind, the retailer
wants to open only 3 additional stores with travel times from
208 demand locations that are
not to exceed 5 minutes.

Due to these constraints, close to half of the demand points (115
out of 208) are
allocated to the 3 best locations, as shown in figure 5.4,
achieving a maximum market share of
33% as determined by the popular spatial interaction model
known as the Huff model (Huff
1964). The three new store locations are dispersed and only one
is close to the competitor’s
locations, as seen in figure 5.4.
Due to the retailer's rather low market share, the company
further examines how many
stores might be required to achieve market coverage of at least
70%. As shown in figure 5.5,
the retailer must open 9 additional stores in addition to its
existing store to cover at least 70%
of the demand. This analysis provides guidance to the retailer’s
senior leadership, which can
now prioritize appropriate parts of the market, and also enable
the retailer’s real estate team
to focus on additional market context factors such as possible
co-tenants.
In short, location-allocation modeling is a manifestation of the
use of prescriptive

location analytics for supply chain optimization, demonstrated
here at the demand end of the
supply chain. From siting distribution centers and basing the
transportation plans of goods on
more sophisticated industry-specific modeling of supply chains
(Kazemi and Szmerekovsky,
2016), optimization models provide prescriptive decision-
making guidance and valuable
location intelligence to achieve supply chain efficiency.
95
Figure 5.4 Location-Allocation Model for Retail Site Selection
with travel time (not to exceed 5 minutes) & facility (3
additional stores) constraints, San Francisco CA
(Source: Author)
96

Figure 5.5 Location-Allocation Model for Retail Site Selection
with travel time & facility constraints (cover at least 70% of the
demand), San Francisco CA
(Source: Author)
97
Routing Optimization
Leading package delivery and logistics service providers such
as UPS and FedEx have
leveraged routing optimization using GIS as a competitive
advantage for many years. With the
sustained growth of e-commerce and explosion of delivery
services (meal delivery, grocery
delivery, etc.), there is renewed focus on the topic of routing
optimization in a variety of
sectors.
Routing of delivery vehicles is a natively spatial problem and
routing optimization is at

the heart of designing efficient supply chains. UPS’s ORION
(On-Road Integrated Optimization
and Navigation) popularized the notion that “left isn’t right,”
minimizing unnecessary left turns
on drivers’ routes (Horner 2016). The system was developed
and refined over the years to
guide UPS drivers making dozens of deliveries over dense,
complex traffic networks. In 2016,
55,000 ORION-optimized routes were documented to have
saved 10 million gallons of fuel
annually, reduced 100,000 metric tons in CO2 emissions, and
saved an estimated $300 million
to $400 million in cost avoidance (Horner 2016). The full case
study on UPS in chapter 9 sheds
more light on ORION’s strategic role in shaping UPS as a
spatial business leader.
Another example is Instacart, whose green-shirted “personal
shoppers” are now
instantly recognizable at our neighborhood grocery stores,
especially in the aftermath of the
COVID-19 pandemic. As explained in Chapter 4, Instacart
competes with Amazon’s FreshDirect,
AmazonFresh, Peapod, and services run by big grocery chains
to deliver groceries to an ever-

broadening base of customers. Instacart deploys sophisticated
routing optimization models
(Stanley, 2017) to route its personal shoppers and streamline
their deliveries. For personal
shoppers who make multiple trips to the neighborhood grocery
store to deliver multiple
sequential orders to customers within tight time windows,
routing optimization algorithms
factor in multiple sequential trips made by the shoppers to
deliver groceries to customers using
personal vehicles with limited capacity (in some case, by bikes)
within narrowly defined time
windows pre-specified by customers.
Over the past several decades, tremendous advances have taken
place in routing
optimization. Many of these advances have been catalyzed by
blending location analytics and
optimization modeling. Yet, in real life operations, the quality
of a route – often defined by its
theoretical length, duration, and cost, can be improved by a
driver’s tacit knowledge about the
complex operational environment in which they serve customers
on a daily basis (MIT, 2021).
Location analytics can play a central role in improving last-mile

routing in such environments
leading to the design of safer, more efficient, and
environmentally sustainable deliveries and
overall route planning.
Facilities Layout
Designing facility layouts is essentially a spatial exercise.
Facilities layout involves many
of the same issues of proximity, distance, separation, layering,
and organizing that are
fundamental in GIS-based location analytics. For single
facilities, layout may include spatial
problems such as placing aisles within a warehouse, situating
job shops in a manufacturing
plant, organizing cubicles in an office, locating self-service
kiosks within a restaurant, or co-
98
locating products on shelves in a retail store. In manufacturing,
efficient layouts can improve
the efficiency and productivity of production lines or assembly

lines, reducing costs.
For multi-facility networks, such as networks of stores,
warehouses, and distribution
centers, the layout challenges are more complex. But
descriptive mapping of multi-facility
networks, along with spatial optimization models based on
operations research, can produce
optimal layouts in retail, transportation and logistics, as well as
in industries such as utilities and
telecommunications. For example, in transportation and
logistics, location analytics can help
design efficient networks that consolidate the number of
dispatch facilities and depots, avoid
overlapping service territories, and reduce the time spent
routing and miles per stop—with the
added benefit of narrowing delivery time windows and
improving on-time performance. This, in
turn, helps to minimize costs, enhance customer service, and
reduce emissions.
Indoor GIS for Facilities Layout Design and Management
Industry forecasts indicate significant growth in the indoor
location analytics segment in

the next five years. The segment is estimated to grow from $3.9
billion in 2019 to $8.4 billion by
2024, at a CAGR of 16.5% (Sreedhar & Bhatnagar 2019).
Indeed, maximizing the value of the
indoor built environment is increasingly a strategic
differentiator for many businesses. Whether
for a company campus with multiple connected buildings, a
sprawling multi-floor convention
center or mall, or a very large facility like an airport, indoor
GIS holds promise.
With indoor GIS, companies can track the movement of people,
goods, and assets,
improve productivity and throughput of the space, enhance
situational awareness in real time,
and provide navigation and routing services, saving time, effort,
and money. At the macro level,
indoor GIS can provide location-based intelligence for building
layout planning and space
optimization. In large public transit facilities such as airports
and in healthcare facilities such as
hospitals, multi-purpose spatial data is used for asset
management including underground
utilities, architectural planning, space optimization, and for
tracking the movements of goods

and people. At transportation hubs such as airports, rail and bus
terminals, facility managers
can ensure smoother passenger flow and a stress-free travel
experience by optimizing the
facility layout comprised of parking, check-in, security, duty-
free shopping, and ultimately
departure/arrival. Indoor GIS is also critical for emergency
managers at airports, convention
centers, and hospitals to plan and execute emergency procedures
that can track both employee
and customer locations in real-time to maximize safety and
minimize response time in the
event of a large-scale emergency.
Indoor GIS at Los Angeles International Airport
Consider for example Los Angeles International Airport (LAX)
where geospatial data and
indoor GIS is key for understanding and visualizing changes to
the built environment such as
passenger terminals as well as for infrastructure management so
that the maintenance and
renovation of critical infrastructure such as runways can be
performed efficiently. At LAX,
Indoor Mobile Mapping Systems (IMMS) uses LiDAR (Light

Detection and Ranging) processes
together with 360° imagery (shown in Figure 5.6) to capture
large, complex, and dynamic
interior spaces as digital, interactive visual representations of
data (LAWA, 2019). IMMS
facilitates the management of space leased by airlines, freight
companies, and concessionaires
99
and ultimately inform the management of tenant lease and rental
agreements and related
pricing (Stenmark, 2016). In addition to facilities management
and serving as a wayfinding
platform, indoor maps in LAX’s IMMS leverage business rules,
localization and IoT to create a
smart airport that enables users to visualize spatial data and
create real-time indoor location
intelligence.
Figure 5.6 360˚ Indoor Imagery shows Ghost Figures of
Passengers and Camera Imagery Location Points (prior to post-
processing of imagery)

(Source: LAWA, 2019)
At LAX, indoor GIS provides up-to-date building floor maps
and planning maps,
facilitates asset management, and forms the basis of a variety of
surveys such as emergency
evacuation surveys and planning, signage surveys, design
surveys, surveys of “as-builts”, and
equipment clearance surveys. Indoor GIS also facilitates line of
sight analysis which is critical for
ensuring security within terminals and other important airport
buildings and facilities (LAWA,
2019). LAX’s indoor GIS can be used to identify locations of
security breaches, hazmat incidents,
equipment breakdowns, and maintenance issues, and deploy the
nearest personnel and crews.
Using location intelligence derived from indoor GIS
applications, command centers can be set
up, nearby assets such as surveillance cameras can be queried,
non-operating assets can be
taken offline, and staff, tenants, and occupants can be
evacuated. In short, indoor GIS can be
incredibly effective for designing incident management
strategies and implementing them in

real time. Overall, LAX’s indoor GIS is a hub of authoritative,
accurate, and up-to-date data that
is used by airport staff as well as by first responders and
emergency managers to develop
familiarity with the buildings and plan and execute emergency
procedures.
Combining indoor mapping with existing geospatial data and
workflows provides a
wealth of vital information to several different stakeholders –
Environmental, Operations,
Security, Safety, Commercial, Engineering, Facility
Management, HRM and external
100
organizations such as police and fire departments. This
facilitates coordination, planning, and
airport project management, and accelerates the speed of
decision-making. The value of LAX’s
indoor GIS is multiplied when the geospatial data collected is
mapped, shared, mined, and the
location intelligence consequently produced is consumed

beyond its initial intended purpose by
multiple stakeholders for a wide-range of benefits (LAWA,
2019).
Overall, indoor GIS adoption and use for space optimization and
facilities layout
planning and design is burgeoning in the private sector.
Emerging trends such as coworking and
the resulting coworking spaces will catalyze further innovation
and investment in indoor GIS.
Indoor GIS is well positioned to provide guidance, after the
COVID-19 pandemic, in
reconfiguring the layouts of workspaces, plants, warehouses,
and congregate facilities to
ensure workers' health and safety. To safely reopen the
workplace and restore employee and
customer confidence, indoor GIS is well-positioned to provide
location-data-driven solutions.
Indoor mapping to reconfigure facilities for safe use and
advanced spatial analysis to identify
congregation hotspots, proximity, distancing, and separation are
all critical to business
continuity in the post-COVID era (Chiappinelli 2020).
Supply Chain Management and Logistics

At the systems level, location intelligence is required for
managing and designing robust
and resilient supply chain and logistics networks. Modern
supply chains are critical for business
continuity. Consider this extreme example from March 2011,
when a magnitude 9.0 earthquake
hit Japan, followed by a tsunami. As the disaster unfolded, a
nuclear facility in Japan was
compromised, resulting in severe radiation leakage. In the days
that followed, an international
consulting firm forecast that as a result of the disaster, the
cumulative production of Japanese
automakers would drop by 2.2 million units, compared to 2010,
when annual production of
Japanese-made passenger cars totaled 8.3 million. Post-disaster,
Toyota suspended production
in 12 of its assembly plants and estimated a loss of around
140,000 vehicles. Due to keiretsu,
that is, interlocked supply chains, disruption in parts supplied
by Tier 2 and Tier 3 suppliers
affected Tier 1 suppliers, in turn disrupting the entire supply
chain.
As parts shortages hit and shipping parts from Japan came to a

complete halt,
production stopped in Toyota’s US assembly plants. Honda also
had to stop its operations as
more than 20% of its Tier 1 suppliers were affected by the
earthquake. Nissan was the hardest
hit and lost 1,300 Infiniti and 1,000 Nissan cars to the tsunami,
shutting down operations in five
plants for several weeks (Aggarwal and Srivastava 2016). Each
day of lost production was
reported to cost Nissan $25 million.
In another, more recent example, Wendy’s restaurants all over
the US experienced beef
shortages as employees at congregate facilities such as meat
processing plants contracted and
then spread the COVID-19 virus, resulting in communal
outbreaks. The price of beef increased
significantly and a federal executive order invoking the Defense
Production Act to classify meat
plants as essential infrastructure became the subject of robust
public discourse (Yaffe-Bellany
and Corkery 2020).
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Whether of cars or burgers, the objective of a supply chain is to
be efficient and cost-
effective across the entire network. Hence, supply-chain
management requires a systems
approach. Also, since a supply chain integrates suppliers,
manufacturers,
warehouses/distribution centers, and stores as part of a network,
network planning is essential.
Network planning is the process by which a firm structures,
optimizes, and manages its supply
chain in order to —
manufacturing costs.
and managing inventory
effectively.
(Simchi-Levi, Kaminski,
Simchi-Levi 2004).

Supply chain network planning involves —
optimal sizes of plants,
warehouses, and distribution centers.
points, optimal stocking levels,
and facilities to stock.
produce, procure, or purchase
and where and when to store inventory (Simchi-Levi, Kaminski,
Simchi-Levi 2004).
Consequently, managing a supply chain involves making site
location decisions—a
natively spatial problem that has strategic, tactical, and
operational implications. Alongside this
are inventory management and resource allocation issues. All
three—site location, inventory
management, and resource allocation—have strong spatial
connections to business operations.
Spatial Technologies for Supply Chain Management

Managing a supply chain effectively starts with combining
internal organizational data
with external data from varied sources. Internal enterprise data
may originate from CRMs and
Enterprise Resource Planning (ERP) systems, bills of materials,
business forecasts, schedules,
and project management systems. External data may be sourced
from various governmental
agencies, third parties, business partners, and industry
organizations, as well as digital sources
such as social media. Use cases may originate in different areas
of the enterprise according to
business needs such as inventory management, forecasting
demand, and generating just-in-
time production plans. Other needs may include material
requirements planning (operational),
territory optimization, route planning, warehouse layout and
facilities design (tactical),
outsourcing, and site selection (strategic).
As these disparate data streams reside in individual silos , their
business use needs,
whether upstream- or downstream-facing, are equally siloed.
However, as an interface, a GIS

acts as an integrative platform between the siloed data streams
and use cases, as shown in
figure 5.7. GIS enables users across the enterprise with different
business needs to collaborate,
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drawing upon descriptive visualization of georeferenced
datasets, which provides the
foundation for predictions, decisions, and informed actions.
Figure 5.7 GIS as interface between data silos & supply chain
use cases (adapted from Chainlink Research)
(Source: Author)
Digitizing and mapping a supply chain as part of organizational
digital transformation
can have several benefits. For a global business, it can provide a
reliable operating picture of
how the supply network chain performs and where it might be
failing. In addition, a digital, fully

mapped supply chain can deliver valuable situational awareness
powered by real-time alerts
and notifications, asset tracking, and monitori ng. It can also
identify geographic stress points
that may be prone to risks. This can help accelerate a
company’s response time during the
normal course of business and enable it to plan, prepare and
respond in an emergency.
Concluding Case Study: Cisco
Consider the case of global networking giant Cisco, which
provides networking
hardware, software, telecommunications equipment, among
many other high-technology
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services and products to its customers. A critical issue for
Cisco's customers is network
downtime which may be caused by the failure of a hardware
product, a part, or any related
equipment. When such breakdowns happen and data

transmissions are disrupted, customers
naturally are not able to access the digital world. Therefore,
prompt restoration of networking
connectivity, often in a matter of hours is critical. However, the
networking equipment or part
may not be available in the immediate proximity of a customer.
Even if a replacement part
were available at a parts supplier located within the firm's
territory, logistical issues may come
into play that may slow down the actual transportation of the
part from the supplier to Cisco's
customer. In some cases where specialized training is needed,
such as, for hotswapping
(replacement or addition of parts or components without
shutting down or rebooting a
system), another issue may be the lack of availability of a
nearby field engineer.
Next, Cisco's global supply chain network is incredibly vast and
complex. At any given
moment, Cisco services networking and telecom at roughly 20
million or more customer sites in
138 countries. When breakdowns happen at any of these sites,
parts are sourced from 1,200
warehouses globally, and a delivery driver is assigned to ship

the part from the depot to the
customer. In addition, of approximately 3,000 field service
engineers (FEs), an individual with
the right skills who is within a reasonable service time needs to
be deployed. It is important to
note that the warehouses are not owned by Cisco, but by third
parties. Similarly, delivery
drivers, and field service engineers are part of Cisco's vast
network of partners. In short, this
vast network of business partners adds an additional layer of
complexity to Cisco's operations.
To ensure that customer networks are back up and running as
soon as possible when
breakdowns happen, Cisco has deployed location analytics to
produce two- or four-hour service
level agreements with its customers depending on the locations
of customers and their
proximity to parts warehouses and FEs. To do this, Cisco's GIS
maps its entire supply chain that
produces descriptive visualizations of facilities, and two- and
four-hour drivetime polygons
(based on local traffic and weather conditions) indicating
proximity of customers to parts and
FEs. Plotting these locations and intersecting them with

drivetime buffers allows Cisco to color-
code its customer sites based on whether they are in two- or
four-hour service windows for a
given warehouse, depending on the type of part ordered.
The process of assignment of customer sites to warehouses is
automated. As soon as a
part if requisitioned, the automated assignment system
recommends the appropriate service
level agreement (two- versus four-hour) to a customer and
initiates the delivery of a part from
the warehouse to the customer site. In addition, the automated
system produces real-time
reports of parts inventory, oversubscribed parts that run the risk
of becoming out of stock,
heavyweight parts that may require special shipment, for each
warehouse location. Armed with
this intelligence, Cisco's partners can replenish inventories at
specific locations, or move parts
to other warehouses based on local needs. This location-based
holistic approach minimizes the
risk of stockouts system-wide. Using location analytics, Cisco
also assign the correct FE to a
customer site, minimizing the lead time between the arrival of a
field engineer and a

replacement part and providing notifications to a customer when
a FE is en route.
104
This use case primarily highlights the principles of location
proximity (between
warehouses and FEs to client sites) and location differences
(differences in inventory portfolio
and quantities at warehouses). It also showcases the use of both
descriptive and prescriptive
location analytics steps of the spatial analysis hierarchy.
Using location analytics, Cisco can solve its customers' most
pressing problem: timely
and accurate resolution of networking issues that disrupt
business operations causing losses,
lost revenues, etc. Guided by spatial modeling, the company
sells the right service contract to
each customer and services them in the quickest time possible
by deploying appropriate assets
and resources. This enhances their post-purchase experience.

In summary, Cisco's GIS platform provides a common
operations platform to its complex
and expansive network of customers, parts suppliers,
warehouses, and logistics service
providers. Using location analytics, the company achieves
improved visibility of service
territories, eliminates coverage overlaps, removes service gaps,
and optimizes the service part
delivery network.
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Chapter 6
Managing Business Risk and Increasing Resilience
Introduction
The chapter is an in-depth exploration of the role of location
intelligence for risk

management and mitigation in business operations. The COVID-
19 pandemic ravaged the world
in 2020. Nations around the globe took unprecedented measures
to contain the spread of the
novel coronavirus by closing national borders, stopping
international travel, shutting down
schools and non-essential businesses, and implementing stay-at-
home policies. The economic
fallout has been unlike any in contemporary history and it may
take years for communities and
businesses to fully recover. Pandemics represent the newest
frontier of risk factors affecting
businesses, along with economic and geopolitical uncertainties,
rapidly changing customer
preferences, evolving competitive threats, climate-change-
induced shifts in weather patterns,
and data/IT security breaches.
From business and operational risk, to risks posed by market
factors and competition, to
environmental factors, the timely assessment and mitigation of
risk is key to sustaining growth
and staying ahead of the competition. Strategic risk occurs
whenever a business voluntarily
accepts some risk to generate superior returns from its strategy.

For example, expanding a
business into new territories—domestic or international—is an
avenue for strategic risk. On the
other hand, external risks arise from events outside a company’s
influence or control, such as
climate change, natural disasters, pandemics, economic
fluctuations, geopolitical uncertainty,
regulatory environments, and cybersecurity breaches.
In confronting external risks such as geopolitical crises and
natural disasters, the lack of
a visible plan can render CEOs and their organizations
vulnerable. Recent surveys have shown
that during such times, two out of three CEOs feel concerned
about their ability to gather
information quickly and communicate accurately with internal
and external stakeholders
(Pricewaterhouse 2020). As a framework for gathering,
managing, and analyzing many types of
data on risk, a GIS may be viewed as a unified source of truth.
For example, it can combine
layers of internal organizational data on customer locations,
store closures, supply network
disruptions and compromised infrastructure with public data
such as imagery, regulations,

restrictions, and mandates from governmental agencies. In
addition to organizing data, a GIS
can ground-truth data and identify inaccurate data. Dynamic
mapping, using real-time location
data, can improve situational awareness and help with risk
identification and mitigation. This
can provide reliable, timely guidance to CEOs and senior
leaders needing to mitigate risk by
critical decision making in real time.
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Location Analytics for Risk Management
Understanding risks specific to place is key to reliable risk
assessment, risk
preparedness, mitigation, and crisis response. In the case of a
manufacturer, risk management
entails understanding spatial exposure to risk of facilities along
the supply chain. In non-
manufacturing settings—for example, in transportation,
utilities, and telecommunications—
physical infrastructure and field assets constitute the geographic

exposure to risk. Risk
management using a GIS offers location-based insights on
emergency preparedness. It also
helps companies develop business continuity plans and an
overall appraisal of business
resilience.
A GIS-based risk management process can be synthesized by
the five steps of planning,
mitigation, preparedness, response, and recovery, as shown in
figure 6.1. At each step of the
process, location analytics plays an important role underpinned
by the principles of location
proximity and relatedness, location differences, location
linkages, and location contexts.
Figure 6.1 The Risk Management Process comprised of five
steps, each of which uses location analytics
(Source: Author)
be deployed for descriptive
visualization of areas, facility locations, and assets exposed to
risk. Geostatistical models

could predict areas most likely to be affected by a risk, along
with the threat level and
how risk might propagate spatially.
protective or preventive actions
can be taken in areas exposed to risk. This can be accomplished
using spatial analysis in
a GIS.
deployment in areas likely to be
impacted, to minimize response times. Prescriptive optimization
models could be used
within a GIS to identify optimal locations for assets and
resources to be deployed for
maximum coverage of affected areas, populations, and
facilities.
when business and other
operations are disrupted and do not function normally. Response
activities might
include activation of disaster response plans and
implementation of strategies and

tactics to deploy location-specific assets to assist, protect, and
save employees,
customers, properties, and the community affected by an
emergency. Layers of
107
geospatial data organized in a GIS can be leveraged to obtain
location intelligence that
can ensure safe access, navigation to affected people and
communities, and timely
evacuation. Location-based intelligence can also help local
governments and communal
organizations, federal and state agencies to obtain a common
operating picture
ensuring coordination of response activities.
occur in parallel to regular
operations and activities. Location intelligence can power the
timely and reliable
restoration of utilities, telecommunications and other essential
services, rebuilding

damaged properties, and reducing vulnerabilities stemming from
diseases and other
threats. Location intelligence can also provide guidance on
areas where it is safe to
resume regular business operations and other areas that require
resource-intensive
efforts.
Location Analytics for Supply Chain Risk Management at
General Motors (GM)
GM’s supply chain is a complex network of relationships
between Tier 1, Tier 2, and Tier
3 suppliers, globally dispersed. 5,500 Tier 1 suppliers ship parts
directly to GM manufacturing
plants. Tier 1 suppliers receive their parts from 23,000 Tier 2
suppliers, creating a network of
approximately 53,000 Tier 2 to Tier 1 relationships. There are
tens of thousands of Tier 3 to Tier
2 connections. The breadth, scope, and complexity of such a
vast, geographically dispersed
supply chain makes planning, mitigation, and recovery
extremely challenging in the event of a
disruption. Clearly, the sheer volume of cars, parts, plants,
suppliers, and shipments makes the

management of GM’s geographically dispersed supply chain a
complex task under normal
circumstances. In the event of a disruption, such complexity
poses enormous challenges for
planning, mitigation, and recovery.
GM’s Supply Chain Risk Management (SCRM) platform
consists of a comprehensive
database of suppliers including location, parts supplied,
connections, and information on key
contacts. All locations are mapped, and using a tracing function,
all connections between plants
and Tier 1 suppliers and between Tier 1 and the sub-tiers are
established. This descriptive
location intelligence immediately reveals –
could be vital insight during
a crisis.
Tier 2 supplier provides
parts to many Tier 1 suppliers, it increases risk exposure,
should the Tier 2 supplier’s
operation be disrupted.

In its supply chain GIS, the company also incorporates a variety
of data feeds including
weather, local and international news, and so on. This produces
24/7 notifications about
current events. In the event of a fire or deleterious weather
event, the company’s supply chain
GIS prepares and delivers a report with an overview of the
event as well as high-level statistics,
such as the number of GM plants and suppliers that could be
affected and the part numbers
involved. This critical intelligence helps GM’s SCRM team
answer the following questions to
speed up its mitigation and recovery responses:
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produce?
, and 3 suppliers are potentially impacted?

For example, when Hurricane Harvey was projected to make
landfall in Houston in
August 2017, the SCRM system identified Tier 1 and 2
suppliers in the area and their parts.
After reviewing the suppliers likely to be affected, the SCRM
team had all Tier 1 suppliers ship
parts to GM plants 2–3 days ahead of the hurricane’s landfall.
Similarly, Tier 2 suppliers would
ship parts to Tier 1 suppliers a few days ahead of schedule.
There were two additional benefits.
By implementing a reliable yet nimble SCRM process, GM
dramatically improved its
supplier footprint analysis to better prepare for risks. As a
result, there were significant savings
in the company’s Contingent Business Interruption (CBI)
insurance coverage, which protects
against losses due to supplier issues (Kazemi 2018).
Improved supply chain visibility enabled GM to focus on ethical
sourcing of raw
materials, including minerals such as gold, tin, tantalum, cobalt,

and tungsten. The use of
conflict minerals for parts or product manufacturing may tarnish
the reputation of a company,
and GM’s GIS system has enabled it to remain vigilant about
the origins of the raw materials it
requires (Kazemi 2018).
Real Estate Risk Management
In its 2020 annual report, Macys identified one of its strategic,
operational, and
competitive risks as the following: “We may not be able to
successfully execute our real estate
strategy.” In the same report, Macys further added: “We
continue to explore opportunities to
monetize our real estate portfolio, including sales of stores as
well as non-store real estate such
as warehouses, outparcels and parking garages. We also
continue to evaluate our real estate
portfolio to identify opportunities where the redevelopment
value of our real estate exceeds
the value of nonstrategic operating locations. This strategy is
multi-pronged and may include
transactions, strategic alliances or other arrangements with mall
developers or other unrelated

third parties. Due to the cyclical nature of real estate markets,
the performance of our real
estate strategy is inherently volatile and could have a
significant impact on our results of
operations or financial condition.” (Macys 2020, pg. 8).
The development of a real estate strategy is key to business
success. A location-based
real estate strategy can inform business development, help
uncover underserved markets,
show strategic growth opportunities, model market saturation,
and visualize the risk of
competitive threats as well as cannibalization. Such a strategy
can produce actionable business
intelligence that can inform strategic decisions such as site
selection; operational decisions such
as store design, pricing and promotions, product selection and
placement; and support tactical
decisions, offsetting manufacturing risk by efficient spatial
allocation of scarce resources. Yet,
109

due to ever-changing business environments, shifts in
demographics and consumer
preferences, socioeconomic changes, geopolitical volatilities,
public health crises and climate
change, real estate decision-making in a 21st-century business
is riddled with uncertainties.
The Rise of 3D
Using GIS as a platform, real estate companies deliver location
intelligence to their
clients by fusing together data from multiple first- and third-
party sources. Using powerful
visualizations, including 3D, real estate companies help clients
make sense of data visually to
provide a holistic understanding of markets, customers, spaces,
facilities, infrastructure, and
their spatial interactions. For example, a property management
company can visualize a
redevelopment project using 3D views in a GIS (figures 6.2 and
6.3) which integrates layers
showing streets, neighborhood green zones, parks, and
neighboring property types.
By toggling between the alternative 3D views, the real estate

team of the property
management company is able to visually explore the space,
conduct before-and-after
comparative analysis of places, and zoom into specific buildings
to reveal building data. The
perspective can be enhanced further by using location analytics
to model transportation access
(for example, to underground subway stations and parking
garages), access to amenities such
as parks and restaurants, and overall quality of life indices, such
as noise pollution levels.
Advanced forecasting models can also be incorporated within
3D views to provide clients with
market potential and relevant KPI estimates based on such
factors.
Armed with such 3D visualizations on mobiles and tablets,
brokers are able to provide
clients with a nuanced, realistic view of market opportunities,
advise them about site suitability,
and even offer a broad view of profitability. Using advanced 3D
information products built
within a GIS, the clients of real estate market leaders such as
Cushman and Wakefield can enter
a space they are appraising and conduct a virtual tour, using a

mobile device. Having developed
this immersive virtual experience before the COVID-19
outbreak, Cushman and Wakefield
considered it indispensable during the pandemic in providing
safe, insightful interactions for
clients, from afar (Lowther and Tarolli 2020).
110
Figure 6.2 3D view of a fictive redevelopment project, showing
the built environment in a Philadelphia city block
(Source: CityEngine, 2016)
Figure 6.3 3D view of a fictive redevelopment project, showing
proposed additions to the built environment in a Philadelphia
city block
(Source: CityEngine, 2016)
Business risk mitigation and building business resilience
Location intelligence can also help businesses analyze risks
associated with supply chain

interruptions, which disrupt the flow of raw materials essential
for business continuity. Many
111
businesses such as grocers establish layered and complex supply
chains sourcing raw materials,
such as perishable frozen food items, from geographically
disparate suppliers. Any food safety
event, including instances of food-borne illness (such as
salmonella or E. coli), could adversely
affect the price and availability of raw materials such as meat
and produce. In addition, food-
borne illnesses, food tampering, contamination, or mislabeli ng
could occur at any point during
the growing, manufacturing, packaging, storing, distribution or
preparation of products.
Descriptive mapping with reporting dashboards provides a
unified, up-to-date, reliable “source
of truth,” producing actionable business and location
intelligence.

Business Continuity: The Case for Dashboards
Inclusion of mapping in dashboards was recently found to be the
highest rated feature
of location visualization in an industry survey (Dresner, 2020).
Consistent with this trend,
organizations have embedded smart mapping capability within
COVID-19 business dashboards
to monitor facility status (for example, what factories are open
or closed, what capacity where
warehouses are operating), identify business segments
(customers, trade areas, markets) most
vulnerable to risk, develop contingency plans, manage and care
for a distributed, remote
workforce, improve supply chain visibility, and craft strategies
to safely reopen businesses and
facilities where appropriate. Such dashboards also enabled
businesses to untangle myriad local,
state, and national regulations and guidelines that were
frequently at odds with one another.
Examples include Wal-Mart’s Store Status dashboard and Bass
Pro Shops’ supply chain risk
mitigation and business continuity dashboard (Sankary 2020).
As part of their public health response to the COVID-19

pandemic, governments—
national, state, county, and city—implemented stay-at-home
orders, including curfews, in
places and recommended social distancing. In addition, non-
essential businesses in various
sectors were ordered to close. What was defined as non-
essential varied from place to place,
creating a jumble of local, county, and state regulations for
businesses to sift through. Grocery
stores, pharmacies, and big-box retail were deemed essential,
but operations at Costco, Wal-
Mart, and Target were somewhat altered to allow time for
sanitization work. In many cases on-
premises pharmacies and departments such as optical shops,
photo printing units, tire and auto
centers, and gas stations were closed. Under such
circumstances, business continuity
dashboards are an effective tool in the location intelligence
arsenal of businesses
Consider the case of Bass Pro Shops’ retail operations which
comprised stores in 45 U.S.
states and 8 Canadian provinces. In Alaska, one of the world’s
finest sources for wild seafood,
fishing is a critical for the local economy. At the outset of the

pandemic, the fish retailer was
among essential businesses in the state of Alaska. However,
under more restrictive local orders
at Anchorage, the company’s two main stores in the state were
deemed non-essential. As Bass
Pro executives had been using GIS for making real estate
decisions, its GIS team constructed a
dashboard (Figure 6.4) showing the exact state of store
operations for 169 stores in the United
States. Open, closed, modified, curb-side pickup, and ship only
stores were distinctively shown
on this internal dashboard enabling decision-makers to develop
business strategies that were
aligned with local restrictions.
112
Figure 6.4 Bass Pro COVID-19 Retail Dashboard showing status
of store operations in the United States during the COVID-19
pandemic
(Source: WhereNext, Sankary, 2020)

For example, in Anchorage, Bass Pro’s two stores were
designated at ship-only since the
retailer was deemed non-essential. A vast majority of Bass
Pro’s stores could remain open (140
out of 169) but had to implement 6-foot social distancing
guidelines. This meant that like many
other businesses, store employees and managers have had to
implement queueing strategies
outside of the stores to prevent too many people in a store at
any given time.
A unified source of truth, the dashboard also showed stores
impacted by closures due to
employees who has tested COVID-19 positive. Employees at
those locations had to be
immediately quarantined or switched to remote work. Most
importantly, the dashboard
enabled senior leaders in different departments to collaborate
closely and be on the same
page. Additionally, it transformed the conversation from closed
stores to those locations that
remained open (a vast majority). This prevented the onset of
unnecessary panic and focused
decision-makers on amending retail strategies consistent with
local health guidelines. Last but

not least, as COVID-19 cases spread rapidly, the case count
information provided location
intelligence on how to allocate one million masks donated by
the company's founder to
healthcare workers on the frontlines of the crisis.
While descriptive in nature, Bass Pro’s business continuity
dashboard provided valuable
location intelligence-based situational awareness guidance
during a volatile period of
uncertainty. In addition, using data and location intelligence,
philanthropic efforts were guided
provided a humanitarian angle to the deployment of the
company's COVID-19 dashboard.
Business Recovery: Real Time Monitoring
Real-time monitoring of disruptions can aid in rapid recovery.
In the aftermath of
Hurricane Florence, CSX, a large railroad company with more
than 21,000 miles of lines
113

extending through 23 states, Washington DC, and 2 Canadian
provinces, needed to rapidly and
accurately identify compromised lines and other infrastruct ure.
With an army of drones, CSX
conducted reconnaissance in flooded areas with no electricity
and poor cell coverage. The firm
was able to identify washouts (figure 6.5) that were displayed in
a central GIS-based war room
in real time. This information enabled senior leaders to work in
concert with field crews who
had eyes on the ground to prioritize deployment of resources to
fix compromised infrastructure
and proactively shut down compromised assets to protect
workers and communities from
harm. This, in turn, allowed operations to begin in a safe and
timely manner, ensuring that
critical supply chains in North Carolina, severely affected by
the hurricane, could be restored
expeditiously.
Figure 6.5 CSX culvert washout in North Carolina after
Hurricane Florence, September 2018
(Source: Slide 9 of PDF presentation, available at

https://www.esri.com/~/media/Files/Pdfs/events/Rail%20Summi
t/2018_Rail/Erik_CSX)
Requesting assistance to locate the Esri Conference at which
this presentation was given.
Risk Resilience: Predictive modeling
Trees are good for the environment. For an electric utility,
though, tree cover in forested
areas poses a significant business risk. Downed powerlines are
known to cause forest fires that
may ultimately spread to nearby communities, causing untold
damage and suffering through
loss of property, livelihood, and even life.
Mid-South Synergy’s Electric Cooperative’s grid provides
service to 23,000 customers
across six Texas counties, Grimes, Montgomery, Madison,
Walker, Brazos and Waller, located
north-northwest of Houston. Part of Mid-South’s service area
overlaps with Sam Houston
114

National Forest. Historically 30 percent of Mid-South’s power
outages are due to fallen trees
and branches and vegetation encroachment. Exacerbating the
risk, many trees had been killed
in its service area by a severe drought. Despite right-of-way
maintenance, dead trees outside
the company’s service area increased the risk of power outages
as well as fires in the National
Forest. Mitigating this risk to improve the reliability of service
and reduce the likelihood of
catastrophic forest fires prompted the company to leverage GIS
technology.
Using soil data from the US Geological Survey and tree
coverage from the US Forest
Service, Mid-South located tall pine trees in dry soil types,
which posed the greatest hazard to
its power lines. Using predictive modeling in Esri’s ArcGIS, the
company generated the
probability of dead trees falling on the utility’s electric wires
across the service area. The entire
service area was subsequently categorized by risk level using a
weighted overlay model (figure

6.6), which helped Mid-South’s field crews prioritize areas that
posed the greatest hazard.
Trees in those areas were then removed and encroaching
vegetation cleared.
Figure 6.6 Weighted Overlay Analysis by Mid-South Co-op for
showing Risk Levels posed by Dead Trees, Sam Houston
National Forest TX
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Within the first year, tree-related outages dropped by over 60
percent while customer
complaints about trees dropped by over 90 percent. Armed with
predictive location
intelligence, the company could proactively schedule the
removal of dead trees from parts of
its service areas before they posed significant threats to service.
This boosted the utility’s
system reliability, secured its grid, improved customer service,
and provided location data and

analytics-based guidance for risk mitigation (Esri 2020).
Innovations in Unmanned Aerial Systems (UASs) now enable
utilities such as Mid-South
Synergy to collect high-resolution imagery, for example, of tree
cover, and then use machine-
learning-based pattern recognition to identify threats to their
infrastructure. This automated
processing and pattern recognition from imagery accelerates the
generation of accurate
location intelligence from geospatial big data. Such advances
illustrate the promise and
potential of predictive modeling of risk in the private sector.
Business Risk Management: Predictive Big Data Modeling
Geo-artificial intelligence (Geo-AI) methods are at the forefront
of predictive modeling
of business risk. With the prevalence of geospatial big data, the
role of predictive analytics for
risk management and mitigation are expanding. A large amount
of big data is unstructured—for
instance, spatial imagery captured by drones or curated from
satellite imagery. Allied with
powerful visualization, data mining enabled by machine

learning and pattern recognition can
help uncover patterns and relationships to accelerate decision-
making during emergencies such
as natural disasters. For example, a major full-service insurer
uses thousands of aerial photos of
residential properties damaged by forest fires to train machine
learning algorithms to detect
the extent of property losses. When the Malibu area of Southern
California was devastated by
the deadly Woolsey Fire in November 2018, geo-AI models
applied to before and after imagery
of properties damaged by the fire were able to detect with close
to 100% accuracy those that
were a total loss. This helped the insurer proactively reach out
to affected customers, ensure
their safety, and expeditiously process their claims.
Concluding Case Study: Geospatial Innovation at Travelers
Insurance
Travelers is a leading property and casualty insurer, with over
30,000 employees and
13,500 independent agents and brokers. It operates in four
countries including the United
States and Canada, with revenues totaling around $30 billion in

2019 (Travelers 2019). Travelers
provides coverage to individuals and businesses; its product
portfolio also includes bond and
specialty insurance such as contract and commercial surety. The
firm counts Allstate and
Nationwide among its main competitors.
Over the years, Travelers’ use of GIS and location intelligence
across the enterprise for
competitive advantage has encompassed many organizational
functions, making it a spatially
mature firm. Enterprise GIS at Travelers is a platform for
collaborative engagement that is
guided by three main principles:
1. Pay what you owe. The business objective is to accurately
price risk for a location in
order to underwrite appropriate location-specific insurance
policies.
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2. Improve customer experience. The business objective is to

provide speedy claims
resolution and timely assistance to customers whose lives may
have been greatly
affected by an emergency.
3. Increase efficiency and productivity. The business objective
is to match resources such
as adjustors to the realities on the ground so that claims can be
appraised quickly, with
shorter processing times.
Pay what you owe
As an insurer, one of Travelers’ main business objectives is to
accurately price risk for
any location. To do so, the company’s research and
development team examines vast
repositories of data that includes addresses, property taxes, and
weather history. Weather
history consists of multiple peril layers such as hurricane,
tornado, hail, wildfire, and
earthquake. Other factors include weather/climate variability,
population density, population
growth (in areas with weaker enforcement of building codes),
urban expansion, and increase in

the average size of a house.
Travelers also examines up to 120 location-specific risk factors,
often at the street level.
These factors, along with other geographic data, are modeled to
predict the risk level for that
location. This allows Travelers' R&D team to determine if
existing insurance products are
suitable for a market or if new policy products need to be
developed. Also, based on risk, the
extent of coverage can be determined, then reconciled with
regulatory requirements. This
drives insurance premiums and deductibles, ensuring optimal
polices for customers and better
returns for Travelers.
Travelers’ use of location intelligence for hyper-local modeling
of risk has several related
benefits. In addition to “rate-making,” Travelers is able to make
decisions on reinsurance
purchase. Depending on the risk profile of a location or
business, Travelers decides whether to
purchase insurance for its own insurance policies. Location
intelligence also provides inputs to
the company’s annual financial planning.

Improve customer experience using innovative predictive
models
Predictive modeling of risk has been a hallmark of Traveler’s
focus on improving its
customers’ experience, often at a traumatic time in their lives.
For this reason, Travelers has
made significant investments in its technology platforms, talent
and data to integrate
geospatial capabilities and location intelligence into all areas of
the insurance life cycle. For
example, the insurer employs 650 certified drone pilots, who
logged over 53,000 flights in the
lower 48 states by early 2020 to build a robust portfolio of
high-resolution imagery. Travelers
National Catastrophe Command Center teams also aggregate
millions of data points from
weather services, satellite imagery, geospatial and location
information
117

In addition, Travelers partnered with the National Insurance
Crime Bureau (NICB) and
the Geospatial Intelligence Center (GIC) to capture “blue and
grey sky” (before and after)
imagery of events. The imagery is critical during the response
and recovery stages of risk
management. For instance, after the Kincade fire that ravaged
California’s Napa Valley in
October 2019, Travelers’ claims professionals began manually
to review images of the
devastation within days to assess the damage at each insured
location. By comparing before
and after images (figures 6.7 and 6.8), properties that were total
losses were identified
manually before many of the affected customers could re-enter
the area.
Figure 6.7 Neighborhood in City of Santa Rosa, Sonoma County
CA, Before Kincade Fire, 2019
(Source: City of Santa Rosa, n.d.)
Figure 6.8 Same Neighborhood in City of Santa Rosa, Sonoma
County CA, After Kincade Fire, 2019

118
(Source: City of Santa Rosa, n.d.)
Teams of insurance experts, software engineers, and data
scientists work in tandem
with the enterprise GIS team to automate pattern recognition
from imagery for faster and more
accurate incident response and recovery. By combining
artificial intelligence with location
analytics, these teams built the company’s innovative Wildfire
Loss Detector, a PyTorch-based
deep learning model that analyzes tens of thousands of images
from damaged and undamaged
homes to evaluate the space around a property and determine its
likelihood of damage during
a wildfire (Travelers 2019). Using its Wildfire Loss Detection
model, the company anticipates
advancing payments to its customers prior to physical
inspection for over 90% of its claims.
Overall, AI-based innovations are being used for a variety of
purposes at Travelers, including
catastrophe modeling.

Increase efficiency and productivity
Location intelligence also plays an important role in deploying
assets on the ground in
the aftermath of an emergency. With location analytics, the
company has now fine-tuned its
staffing approach during the recovery phase of an event,
depending on location and the type of
emergency. This efficient allocation of critical resources
(adjustors and claims handlers) helps
Travelers handle and pay out claims expeditiously and
accurately.
Finally, location intelligence can be key to detecting fraud and
reducing costs stemming
from fraudulent claims. All fraud happens somewhere; it has a
spatial dimension. Many
insurance companies use mapping and statistical modeling to
detect spatial patterns of claims,
especially those that are much higher than average for particular
types of repairs. In this way,
insurers can isolate high-value claims and trace them to
particular service providers, e.g. auto
repair shops, by determining service outlets to which customers

have traveled unusually long
distances.
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Chapter 7
Enhancing Corporate Social Responsibility
Introduction
In making important decisions, organizations must consider how
such decisions will
affect shareholders, management, employees, customers, the
communities in which they
operate, and the overall planet. Finding optimal solutions that
are in the best interests of all the
varied stakeholders is not always easy, but it is an important
responsibility of business. This
chapter examines how location intelligence can guide and shape
ways in which a contemporary
business can balance its financial goals and competitive
pressures with environmental, social,

and human objectives.
Corporate Social Responsibility (CSR) calls for a company to
be socially accountable in
ways that go beyond making a profit. The company takes a
broader view of its goals, thinking
not only of its stockholders, but also of the benefits to its
employees, customers, community,
the environment, and society as a whole. As mentioned in
chapter 1, the 2019 Business
Roundtable's “Statement of Purpose of a Corporation” made a
major shift by declaring that a
company serves the needs of all its stakeholders (Business
Roundtable, 2019). The statement
includes commitments to development and to compensating
employees fairly, fostering
“diversity and inclusion, dignity and respect,” interacting fairly
with suppliers, enhancing the
communities a business is involved with, supporting sustainable
practices, and generating
“long-term value” for shareholders (Business Roundtable 2019).
Environment, Society and Governance
It has been observed that CSR articulates the range of business
societal intent, while

environmental, social, governance (ESG) represents the
actionable practices and benchmarks
for intended practices, and the alignment of their business
strategy, initiatives, investments,
and partnerships. The broadest and most widely accepted ESG
framework is the United
Nations Sustainable Development Goals (SDGs), adopted i n
2015 with targets in 17 areas set for
2030 (Figure 7.1). As noted in the UN statement:
“On behalf of the peoples we serve, we have adopted a historic
decision on a
comprehensive, far-reaching, and people-centered set of
universal and transformative goals
and targets. We commit ourselves to working tirelessly for the
full implementation of this
Agenda by 2030. …We are committed to achieving sustainable
development in its three
dimensions – economic, social, and environmental – in a
balanced and integrated manner. “
Countries and companies are now taking aggressive steps to
advance and their ESG
agenda and utilizing locational analytics and related GIS

systems and data to do so. The
successful implementation of United Nations Sustainable
Development Goals (SDGs) Data Hubs
by more than 15 countries over the last four years has
established a consistent, scalable pattern
for reporting on and monitoring the progress of the SDGs (Esri
2021).
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Figure 7.1: United Nations Sustainable Development Goals

(Source: United Nations, 2022)
Business Implementation of ESG
As noted in the Chapter 1, a substantial percentage of global
businesses report on ESG
achievements. Location plays an important role in integrating
this information. Using GIS
mapping and location analytics, a company can—
scalable way, and share it
across various business platforms.
-enrich such data with location-specific indicators of
interest to the firm, for
example, indicators of health and wellness, or racial/ethnic
diversity. Other indicators
might include psychographic attributes of customers such as
attitudes towards the
environment, social causes, and activism.
on-specific GIS maps, reports, and dashboards
that inform business
strategy and ESG practices, monitor progress towards specific

goals, and measure their
impact.
organization, who are likely to
be affected by the firm’s ESG actions.
socioeconomic composition of
communities in which the company operates. This, in turn, can
inform community
engagement projects.
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Sustainable Supply Chains
One area of ESG focus is supply chain transparency. This
requires companies to know
what is happening in their supply chains and to communicate
that both internally and
externally to employees, customers, and other stakeholders. The

reputational risk and cost of
engaging with suppliers who unethically source raw materials or
manufacturing partners who
do not hire locally, pay fair wages, or engage in child labor can
be immense. To this end, using
mapping and geo-enrichment, many companies are ensuring
supply chain traceability and
providing full disclosure about internal operations, direct
suppliers, indirect suppliers, and
origins of raw materials to various stakeholders.
Early adopters of supply chain mapping are companies such as
Nike that maps its
manufacturing plants and offers insight about individual
factories (Bateman and Bonanni,
2019). UK retail giant Marks and Spencer provides interactive
mapping (Bateman and Bonanni,
2019) of its more than 1,300 factories worldwide in 44 countries
employing over 900,000
people. As shown in Figure 7.2, these factories produce Marks
and Spencers' food and
beverages as well as clothing, accessories, beauty products,
footwear, giftware, and items for
the home.

Figure 7.2 Marks and Spencer Global Supplier Locations for
Food, Clothing, and Home products
(Source: Marks and Spencer,2022)
122
Figure 7.3 Marks and Spencer Wool Supplier Locations in New
Zealand
(Source: Marks and Spencer,2022)
Users can query the interactive map to determine the exact
locations of plants from
which certain raw materials are sourced. For example, Figure
7.3 shows farm locations in New
Zealand from which Marks and Spencer sources wool for
clothing apparel and related other
household products, along with the number of employees and
animals at a particular farm.
VF Corporation, whose brands include Timberland, The North

Face, Wrangler, Dickies,
and Vans has taken one step further in leveraging location
analytics to improve product
traceability and supply chain transparency (VF Corporation,
2018). After creating an exhaustive
database of Tier 1, 2, and 3 suppliers of materials (such as
fabric, leather, yarn, foam, laces,
trim, etc.), traders, textile mills, factories, and distribution
centers, VF Corp. has created
traceability maps of its brand name products that communicate
to consumers exactly where
raw materials for a particular product was sourced from, where
it was manufactured,
assembled, and shipped for distribution, and how components of
the product flow between
different facilities in the supply chain.
For example, Figure 7.4 shows Timberland's Women's
Waterproof Boots that are traced
back to 20 facilities across produced in 7 countries over 4
continents. Traceability maps also
include information on each facility's workforce diversity, as
well as certifications on (a)
sustainable materials use, (b) environmental and chemical
management, (c) health, safety, and

social responsibility, along with worker well-being, community
development, and
environmental sustainability programs. These product-by-
product supply chain traceability
maps take transparency and disclosure to a high level, inviting
all stakeholders including
consumers to be more informed about where and how the
products they buy are made.
123
Figure 7.4 Traceability map of Timberlands Women's Premium
Waterproof Boots, showing production and distribution
facilities (factories, textile mills, material suppliers, and
distribution centers) worldwide
(Source: VF Corp.,2020)
Preserving Biodiversity
Companies can play an important role in preserving
biodiversity. An example is Natura,
the largest manufacturer and marketer of cosmetics, household,

and personal care products in
South America. As part of its commitment to sustainability,
Natura seeks to conserve
biodiversity in the Amazon region, where its agroforestry
farming and employment strategies
aim to build community wealth. Using a spatial approach,
Natura has fostered interactions with
rural communities and developed sustainable value chains that
generate superior returns for
the company (Cheng, 2021).
In the early 2000s, Natura launched its Ekos line of beauty
products comprised of bath
products, premium fragrances and cosmetics, hair care, and
skincare products, in addition to
products for infants and children. Raw materials for these
products included Brazil nut, passion
fruit, andiroba plant-based oils, murumuru butter, cacao (from
which cocoa butter is sourced),
and other biodiverse inputs native to the Amazon rainforest in
Brazil. By the 2010s, with
burgeoning demand for such products, one of the problems
encountered by Natura was how to
find potential suppliers in a region plagued by a lack of
logistics. To do so, the company needed

to compile production and harvest data, including the locations
of thousands of participating
farms. Retention of suppliers was another challenge. But the
company’s policy was to maintain
open relationships with suppliers and constant interactions with
the community (Boehe,
Pongeluppe, and Lazzarini, 2014).
To achieve these objectives, the company built a geospatial
platform. Supply chain data
collected from the field was combined with internal business
data, analyzed, and then
published in the form of interactive web maps and apps. Using
the company’s geospatial
124
platform, farming cooperatives, consumers, and shareholders
could design different views of
data, specific to their workflows, and gather location
intelligence to make decisions about
sourcing, pricing, and distribution. The company’s platform
also improved traceability and

transparency of its investments, production, and supply chain
infrastructure. With a greater
ability to view the entire supply chain, Natura has maintained
its commitments to biodiversity
and environmental stewardship while generating sustainable
competitive advantages for the
company (Esri, 2015). By using a "quadruple bottom line"
approach that balances financial,
environmental, social, and human objectives, Natura has
continued to diversify its product
offerings using an expanded array of supplier communities and
bio-ingredients while
simultaneously protecting the Amazon and committing to the
ethical sourcing of biodiverse
ingredients (Natura, 2020).
Climate Resiliency
A recent study has shown that the world economy could shrink
by 10% if the 2050 net-
zero emissions and Paris Agreement targets on climate change
are not met (Swiss Re, 2021).
Around the world, business leaders are enacting strategies and
tactics to address climate
change. As climate crises disrupt business operations and

increasingly pose threats to business
continuity, companies can monitor their environmental and
carbon footprints over space and
time by using geo-visualization, dashboarding, and predictive
modeling approaches.
AT&T serves as an example of a corporation taking an active
role in understanding the
impacts of climate change and taking actions to impact global
warming as well as to assist
businesses in mitigating climate impacts. Through their Climate
Resiliency initiative, the
company (in collaboration with Argonne National Laboratories)
is building a Climate Change
Analysis Tool. Using data analysis, predictive modeling, and
visualization, this tool enables AT&T
to react to climate changes by making the adaptations necessary
to help increase safety,
service, and connectivity for its employees, customers, and
communities (AT&T, 2021). An
example of this initiative is assessing the potential impact of
climate-induced flooding on their
infrastructure (see figure 7.5) and taking appropriate mitigation
actions.

AT&T plans to make the tool widely available to other
businesses and the public, citing a
recent survey which found that the majority of US businesses
(59%) view climate change as a
priority, yet less than a third (29%) have assessed the risks of
climate change to their business
(AT&T, 2019).
COVID-19 Pandemic Dashboard
Dashboards provide visual displays of critical business
information arranged on a single
screen to provide a consolidated, unified view of a business or
phenomenon. (Sharda, Delen,
and Turban 2016). The COVID-19 pandemic has accelerated the
need for businesses,
organizations, and communities to visualize fast-moving and
rapidly changing business patterns
and trends, often in real time. Due to the rapidly increasing
volume of data from disparate
sources, the demand for scalable, efficient, locationally
sophisticated visual analytics is at an all-
time high. The importance of accurately depicting on-ground
realities and “telling the story” to
different stakeholders, from senior leaders to frontline

employees, has never been higher. This
125
unprecedented demand for data visualization and predictive
modeling of business risks in real
time has catalyzed the use of sophisticated dashboards, with
mapping, for COVID-19 and other
government and business applications.
Figure 7.5 Visualization of AT&T Fiber (black lines) and Cell
Sites (green dots) in Savannah, a coastal Georgia city, at risk of
flooding, with darker colors indicating high flood levels
(Source: AT&T, 2019)
Johns Hopkins University (JHU)’s COVID-19 dashboard
(Figure 7.6) received worldwide
attention during the pandemic because it seamlessly combined
data from dozens of sources to
provide spatial and temporal depictions of critical COVID-19
trends in real-time. Viewed up to a

trillion times or more, the dashboard fuses COVID-19 data from
hundreds of sources such as
the World Health Organization (WHO), European Centre for
Disease Prevention and Control
(ECDC), US Center for Disease Control and Prevention (CDC),
and various country, state,
municipal, local governments and health departments.
To identify new cases, JHU researchers have monitored various
Twitter feeds, online
news services, and direct communication sent through the
dashboard. COVID-19 cases, incident
rates, fatality rates, and other metrics of interest to
governments, health authorities,
businesses, news organizations, and the general public were
reported at various geographic
resolutions. Initially, for some countries, such as the United
States, Australia, and Canada, data
were reported at the city level, and at the country level
otherwise. At present, US COVID-19
data is reported in the dashboard at the county level. Data were
updated multiple times each
126

day to keep the dashboard up-to-date and meet expectations as
an authoritative source of
COVID-19 data and its visualization at a time when the spread
of the disease became rampant
in many parts of the world (Dong, Du, & Gardner, 2020).
Figure 7.6 Johns Hopkins University's COVID-19 dashboard
showing cumulative cases worldwide in December 2020
(Source: Johns Hopkins University ,2020)
(PLEASE PRINT IN LANDSCAPE MODE)
It has also informed modeling efforts by experts in
governments, public and private
sector agencies, and academia to generate accurate
spatiotemporal forecasts of transmission
and spread of the disease. These models have informed the
formulation of public health policy
of governments and health organizations worldwide to prevent
further escalation and spread of
the virus. As such, JHU's COVID-19 dashboard is a perfect
illustration of the power of

dashboards for data description, fusion, and visualization to
inform numerous stakeholders.
In the private sector, organizations have embedded smart
mapping capability within
COVID-19 business dashboards to monitor facility status (for
example, what factories are open
or closed, what capacity where warehouses are operating),
identify business segments
(customers, trade areas, markets) most vulnerable to risk,
develop contingency plans, manage
and care for a distributed, remote workforce, improve supply
chain visibility, and craft
strategies to safely reopen businesses and facilities where
appropriate. Such dashboards also
enabled businesses to untangle myriad local, state, and national
regulations and guidelines that
were frequently at odds with one another. Examples include
Wal-Mart’s Store Status
dashboard and Bass Pro Shops’ supply chain risk mitigation and
business continuity dashboard
(Sankary 2020).
Predictive Modeling of Pandemic
While John Hopkin’s Dashboard provided an update to

indicators of the pandemic’s
outbreak, another predictive analysis was needed to assist in
targeting resources. Such a model
127
was developed and implemented by Direct Relief. Direct Relief
is a non-profit humanitarian aid
organization operating in the United States and more than 80
nations worldwide, providing
critical relief to improve the health and lives of the most
vulnerable populations affected by
poverty and crisis (Direct Relief, 2020). The company's mission
is to "improve the health and
lives of people affected by poverty or emergencies – without
regard to politics, religion, or
ability to pay." (Direct Relief, 2020)
To achieve its mission Direct Relief needs precise geographic
information and accurate
spatial models to predict the needs for humanitarian assistance
when normal information

channels have been disrupted or destroyed. The range of relief
events include catastrophic
floods, storms, fires, and earthquakes, and as pandemic diseases
such as COVID-19, Ebola, and
Zika as well as persistent infections like HIV, malaria, and
tuberculosis threaten millions
annually (Schroeder, 2017). Such information helps the
organization identify specific needs on
the ground in an impacted area, coordinate its response with
dozens of other organizations,
and then deliver targeted relief (supplies, equipment, pers onnel)
in a timely manner.
During the COVID-19 pandemic, providing protective gear and
critical care medications
all over the world to as many healthcare workers as possible and
as quickly as feasible was a
crucial part of Direct Relief’s operations. Timely shipping of
personal protective equipment
(PPE)—millions of N95 and surgical masks, gloves, face
shields, and tens of thousands of
protective suits—in coordination with public health authorities,
nonprofits, and businesses
posed an immense logistical and supply chain challenge.

To address the challenge and meet critical needs at the point of
care in the US, Direct
Relief needed to identify hotspots of COVID-19 infections and
hospitalizations. To do this, Direct
Relief used Facebook-provided data, data from other third
parties, and AI to predict, visualize,
and analyze the spread of COVID-19 in US counties (Smith
2020). Specifically, Direct Relief
employed a neural-network-based AI model developed by
Facebook’s AI Research (FAIR) team.
This FAIR model combines reliable first- and third-party data
on a wide variety of important
factors such as confirmed cases, prevalence of COVID-like
symptoms from self-reported
surveys, human movement trends and changes across different
categories of places, doctor
visits, COVID testing, and local weather patterns to forecast the
spread of COVID-19 in the US
(Le, Ibrahim, Sagun, Lacroix, and Nickel 2020).
Using spatial cluster and outlier analysis based on the outcomes
of the FAIR model,
Direct Relief identified emerging, receding, and persistent
hotspots, coldspots, and outliers of
COVID-19 spread. For instance, figure 7.7 illustrates outcomes

of cluster and outlier analysis to
predict that Los Angeles, San Bernardino, and Riverside
counties in California would become
hotspots of COVID-19 spread between December 20, 2020, and
January 9, 2021, while
Maricopa County, Arizona, Miami-Dade County, Florida, and
Cook County, Illinois, were
predicted to be outliers (counties with high prevalence of
COVID-19 surrounded by other low-
prevalence counties).
128
Figure 7.7 Predicted COVID-19 Case Spread from Dec 20, 2020
to Jan 9, 2021 (based on FAIR Model) in U.S. Counties
(Source: Direct Relief ,2020)
In addition to the FAIR model, by analyzing human mobility
patterns, Direct Relief was
able to predict surges and regional acceleration of COVID-19 in
rural northern Ohio and western
Pennsylvania, and in states such as Wisconsin (figure 7.8). The

organization predicted
accurately that hospitalization rates were likely to lag periods
of elevated mobility by about 12
days, while mortality rates would lag by another 10 days (Smith
2020).
Figure 7.8 Change in Human Movement during the COVID-19
pandemic in Wisconsin counties between Oct - Dec 2020
(Source: COVID-19 Mobility Data Network,2020)
Armed with such predictive spatiotemporal insights into disease
spread, Direct Relief
overlaid its shipment facilities on its dashboard of COVID-19
spread to identify their proximity
to hotspots. This, in turn, enabled the organization to position
critical care resources and
129
supplies, track deliveries, and prioritize its financial support to
healthcare facilities responding
to the pandemic. Such predictive location intelligence can also

inform and accurately time the
responses of public health officials, help them develop policies,
and provide recommendations
to the public to slow the spread of the virus. Overall, using
location modeling and intelligence,
the company is able to fulfill its mission of serving the most
vulnerable populations with no
expectation for payment or profit.
Diversity, Equity, and Inclusion (DEI)
Issues of racial inequities and injustice have been growing in
societal and business
discourse in recent years and came to the forefront in 2020. As
corporations have navigated
social and racial unrest, their role in addressing socioeconomic
inequities has increasingly come
under the microscope. There has never been a more important
time for businesses to
understand the importance of location and local geographies in
addressing these deep-seated
challenges.
Location intelligence can inform a company's efforts to engage
in racial justice efforts in

their immediate communities by providing a geographically
nuanced, data-enriched view of
community conditions (for example, access to affordable
housing, healthcare, education,
transportation, banking, and financial services, parks and open
spaces, to name a few), and
identifying gaps that stem from causes such as racial inequities ,
discrimination, and lack of
access to power and resources.
Spatial statistical modeling can yield powerful location-specific
insights about
correlations between community conditions and barriers to
equality (for example,
race/ethnicity-based discrepancies in reliable broadband access
and usage spawns economic,
educational, housing, and health inequalities, thus deepening
the understanding of root causes
of racial injustices. This can inform a company's decisions to
prioritize their racial justice efforts,
customize location-specific resources in alignment with
community conditions, identify
partners in the community, provide a platform for collaboration,
monitor the progress of
initiatives, and communicate their impacts using tools that are

enriched by location insights and
intelligence.
For example, the “Business Case for Racial Equity” study
estimated that $135 billion
could be gained per year by reducing health disparities (Turner,
2018). Healthier workers have
fewer sick days, are more productive on the job, and have lower
medical care costs. They
estimate disparities in health in the U.S. today represent $93
billion in excess medical care costs
and $42 billion in untapped productivity. This is in addition to
the human tragedy of 3.5 million
lost life years annually associated with these premature deaths
(Turner, 2018). Innovative
companies ProMedica, Kaiser Permanente, Cigna, and
UnitedHealth Group have created
geographically focused shared value approaches that address
racial health inequality in a
manner that improves health and reduces costs (deSouza and
Iyer, 2019). Unfortunately, the
COVID-19 pandemic has made the challenge greater, as it has
had a disproportionate impact on
underserved communities (United Health Foundation, 2021).

130
Location intelligence can also provide companies with a
valuable window to create a
diverse, more equitable workplace. Location-infused dashboards
can provide insights into the
workforce diversity of an organization operating in multiple
locations. This can be benchmarked
against industry standards to identify gaps based on
demographic, socioeconomic, and diversity
metrics such as duration of tenure and prior experience. Based
on these gaps, organizations can
target specific employees for transfer from one location to
another and tailor attractive
compensation packages based on locational analysis of factors
such as the cost of living.
For example, San Diego has undertaken an inclusive economy
initiative. The goal is to
contribute toward the regional goal of 20,000 skilled workers
(degree or credential holders) in
San Diego County by 2030, and to do it through a more
“Inclusive economy”. Part of the

initiative is the use of location analytics as we will look inward
to address regional talent
shortages and strengthen the relationship between to under
barriers to such as education, and
access (figure 7.9.)
Figure 7.9 Disparities in Access to Jobs by Race and Ethnicity
within the context of an Inclusive Economy in San Diego CA
(Source: San Diego Regional Economic Development
Corporation, n.d.)
Location intelligence can also shape HR recruitment strategies
by providing insights into
the diversity of graduates in immediate communities
surrounding an organization. Companies
can direct resources accordingly to participate in job fairs and
launch geotargeted advertising of
open positions to create a diverse pool of prospective
applicants. Armed with location
intelligence on the composition of students in institutions that
serve underrepresented
131

populations, including historically Black colleges and
universities (HBCUs), tribal colleges and
universities (TCUs), and women’s colleges, organizations can
build a pipeline of new job
candidates.
Community Development
Finally, spatial analysis of industry clusters can produce
location intelligence on regional
concentrations of employers and employees for a particular
industry. Regions comprised of
diverse communities such as multinational, multicultural ethnic
melting pots can also be
geotargeted for recruitment of employees who are likely to
contribute to creating a diverse and
more equitable workplace. One example of this is the creation
of Opportunity Zones as part of
the 2017 Tax Cuts and Jobs Act (TCJA). A total of 8,764
Opportunity Zones have been
designated in the United States (figure 7.10), many of which

have experienced a lack of
investment for decades. The Opportunity Zones initiative is a
tax policy incentive to spur private
and public investment in America’s underserved communities.
The aim of the program to
encourage private investment in communities of need. The
Council of Economic Advisors
(2020) estimates that by the end of 2019, Qualified Opportunity
Funds had raised $75 billion in
private capital. Although some of this capital may have
occurred without the incentive, the CEA
estimates that $52 billion—or 70 percent—of the $75 billion is
new investment. These
activities are tracked by multiple sources, including a StoryMap
(Figure 7.11) by Economic
Innovation Group (2021).
132
Figure 7.10 Over 8,700 Census Tracts Designated as
Opportunity Zones in the United States
(Source: HUD, n.d.)

Figure 7.11 Opportunity Zones Activity Portal, showcasing
economic activities and development in Opportunity Zones in
the
United States
(Source: Economic Innovation Group, n.d.)
One specific example of this type of shared value is in Detroit.
JPMorgan Chase is a
leading bank worldwide, with 2019 revenues of $110 billion,
257,000 employees, and a large
suite of products and services including corporate banking, risk
management, market-making,
brokerage, investment banking, and retail financial services.
JPMorgan was a signatory of the
Business Roundtable statements on CSR and has embedded CSR
as part of its culture. One of its
exemplary CSR projects has been a long-term program to help
small businesses thrive in the
city of Detroit, which had been on a downward spiral for
several decades, exacerbated by the
great recession of 2007–2009. As a result, many downtown
neighborhoods and major streets
were deserted and in disrepair, with small businesses bankrupt

or enduring heavy losses.
JPMorgan decided to foster a campaign named “Invested in
Detroit” across these
neighborhoods to seek to rebuild the city and its economy
(Heimer 2017). It would do this
through a program of credit for small businesses combined with
bank-initiated training of
affected managers and employees, and a highly-skilled analytics
team rotated to Detroit from
the bank’s array of prosperous divisions and offices. The bank
invested $150 million in this
project, partnering with local redevelopment enterprises to
identify businesses with potential
that were not able to meet loan criteria.
The bank discovered it could help these businesses most
effectively by using Community
Development Financial Institutions (CDFI) loans, which are
keyed to low-income areas to help
133

disadvantaged small enterprises and nonprofits. JPMorgan’s
analytical team assisted in
locationally analyzing a plethora of data points by
neighborhood and pinpointing micro-districts
of 10 to 15 blocks each for the initial credit and job-training
push. The bank has succeeded
already with loans to three initial micro-districts and will scale
this up to a dozen more while
rolling out this CSR approach to depressed areas of other
American cities. JPMorgan is also
realizing eventual financial benefit since in Detroit it holds $20
billion in deposits. Hence, the
"Invested in Detroit" project can stimulate long-term business
growth and conversion of many
new and renewed businesses into bankable entities adding to
JPMorgan's market share of
deposits and loans (Heimer, 2017).
Corporate initiatives based on shared value, such as JPMorgan's
"Invested in Detroit"
allow businesses to contribute to the greater good. This can
ultimately enhance their
reputation and standing as ethical, responsible, and trustworthy
enterprises, which in turn can
help retain customers, improve employee morale, lower

turnover, and instill a purpose-driven
corporate culture.
Closing Case Study: Nespresso
Nespresso is an operating unit of Nestlé, headquartered in
Lausanne, Switzerland.
Nespresso produces coffee pods in aluminum-coated packets,
which can be used in Nespresso
espresso machines or equivalents. Raw, high-quality coffee
from developing nations, mainly in
Africa and Latin America, is shipped to three factories in
Switzerland, where the coffee is
ground, encapsulated in aluminum pods, and sold worldwide.
The Nespresso single-serve
system was patented in 1976 and today is sold in over 81
nations (Nespresso, 2022).
Nespresso is committed to the UN 2030 goals, tracking their
performance on the
relevant 10 of the 17 goals in various initiatives
(Chiappinelli,2018). In 2003, Nespresso formed
the AAA Sustainable Quality program with the assistance of the
Rainforest Alliance. The AAA
Program guides and equips farmers with the technical

knowledge and financial resources
necessary to pursue sustainable practices. Nespresso then pays
the farmers a premium market
price for coffee that meets the AAA Program quality standards
(Rainforest Alliance, 2021).
The AAA Program started with 500 farmers in 2003 and now
reaches more than 122,000
farmers in 15 countries representing a total annual investment
of over US $43 million per year
(Nespresso, 2021). The company sources 93% of the coffee
through that program and 95% of
our global coffee purchases for 2019 met the Fairtrade
Minimum Price (Nespresso, 2021). The
AAA program is part of Nespresso’s “Positive Cup Framework,”
This framework focuses on long-
term sustainable coffee supplies, analytics to support farmers,
transparent communication to
customers, and responsible practices in communities. These
priorities are supported AAA
platform, which includes F.A.R.M.S (Farm Advanced
Relationship Management System) (De
Pietro,2019). At the farm level, the geospatial platform can be
used for a variety of analyses,
such as biodiversity protection (figure 7.12). At the global

level, it can track the achievement of
AAA sustainable goals (figure 7.13).
134
Figure 7.12 Nespresso F.A.R.M Biodiversity map displays
analysis of landslide potential and tracks the planting of trees to
protect biodiversity
(Source: Nespresso)
Figure 7.13 Nespresso's Positive Cup Dashboard displays the
F.A.R.M.S GIS visual database that tracks the performance of
all
75,000 farms along with a number of sustainability measures
(Source: Nespresso)
This long-term multi-decade commitment to sustainability has
led Nespresso to be
lauded for its commitment to sustainability and commitment to
enhancing local suppliers and
communities. Porter and Kramer recognized this when they

introduced the concept of shared
value in 2011 (Porter & Kramer, 2011). In the article, they
called out Nespresso for their
135
community building impact, noting: “Embedded in the Nestlé
example is a far broader insight,
which is the advantage of buying from capable local
suppliers….Buying local includes not only
local companies but also local units of national or international
companies. When firms buy
locally, their suppliers can get stronger, increase their profits,
hire more people, and pay better
wages—all of which will benefit other businesses in the
community. Shared value is created."
(Porter & Kramer, 2011, p.10).
Nespresso, as well as the other examples in this chapter confirm
the interrelatedness of
business and the communities they operate in, and the mutual
business and society gains that
can be possible. Geospatial platforms enable location analytics

that can reveal these patterns,
trends, and opportunities.
136
LOCATION ANALYTICS
PART III
137
Chapter 8
Business Management and Leadership
Introduction
This chapter turns to examine the human and behavioral side of
spatial business.
Mounting great geospatial efforts and design is not likely to

succeed without the human factors
of management and leadership. In this context, leadership
involves skilled management,
championing spatial initiatives, continuing training and
education of employees, and
understanding the human elements that strengthen locational
transformation of the
organization. It encompasses corporate social responsibility, for
which a company considers, in
addition to its profits, its full effect on society, including
environmental, social and economic
impacts. The chapter also includes the management concern for
location privacy.
Consider an Asian cruise shipping firm that is seeking to
develop locational intelligence
because its senior vice president of marketing “gets it” about
how locational information can be
applied competitively, leading to more profits. The director of
global marketing, who is
beginning to learn about spatial and is also enthusiastic, is
working with an analytics expert who
is tasked with pushing forward a powerful spatial marketing and
customer system to offices
companywide.

Among the firm’s challenges are how to evaluate and tw eak a
starting enterprise-GIS
built by an outsourcer, how to train 25 middle managers and
skilled business analysts in seven
major business units, how to motivate managers who are trained
to move forward in their units
with creative use of the new system, and how to gauge progress
and measure locational value.
A looming challenge is how to break down organizational walls
and widen the location analytics
initiative to other units such as navigation and supply chain.
Also, since building long-term
customers is crucial, the leaders and managers of the firm must
protect the location privacy of
their records.
All the chapter issues are captured in this example: spatial
maturity, workforce
development, middle and senior management, digital and spatial
transformation, ethics and
privacy, and, and the pulls and tugs of executive championing
of location analytics versus
competing internal initiatives.

Spatial Maturity Stages
Figure 8.1 shows an organization's stages of spatial maturity,
based on a stage model for
analytics in general proposed by Davenport and Harris (2017).
Davenport and Harris (2017),
who posited the analytics stages upon which Figure 8.1 is
based, also describe a progression
through analytics stages, which may be adapted to apply to
location intelligence and analytics.
138
In stage 1, there is limited locational data, which often lacks
quality controls. There are
limited workforce skills in location analytics and few if any
metrics measuring spatial value and
productivity. The trigger that moves to stage 2 is often effort by
the original supervisor or low-
level sponsor to broaden the support base and try to
communicate with senior leaders. Since
GIS and location intelligence are relatively new to some
business leaders, an important step in

the process is to engage them, educate them if necessary, and
bring them along in the journey.
In stage 2, sponsorship of spatial initiatives comes from a local
departmental or
divisional manager (Davenport and Harris 2017; SBI 2018). The
stage is typified by testing
spatial applications locally and assessing net benefits. The
initiative may then be taken up by
other departments and their sponsors.
139
Figure 8.1 The five Spatial Maturity stages represent moving
organizationally from analytically impaired, to localized
analytics, analytical aspirations, analytical company and
analytical competitor
(Source: Davenport and Harris, 2017)
Whether location analytics stays long-term in this stage or
advances to stage 3 depends
on gaining the initial support from senior management for a

companywide effort. An example is
a leading international commercial real estate firm, in which a
highly skilled technical manager
succeeded in gaining traction for a spatial intelligence initiative
in US business units but had
mixed success in persuading overseas units to start local
projects, in some cases encountering
resistance to a new technology. In this instance, spatial maturity
had been temporarily stalled
at the local stage overseas.
Stage 3 involves sponsorship by a senior executive and the
formation of antecedent
structures to launch an enterprise spatial system. Usually, a
location analytics project is
improved to the point of garnering visible attention
companywide. Another crucial
development is “defining a set of achievable performance
metrics and putting the processes in
place to monitor progress” (Davenport and Harris 2017). At the
end of a successful stage 3, the
C-suite will begin to recognize the importance of GIS, and
sufficient capabilities will be in place
to implement enterprise-wide.

In stage 4, the senior executive team decides that location
analytics will be implemented
across the entire company. A centralized and highly skilled GIS
team is assembled, bringing in
talented location intelligence employees who may have been
working in business units, and the
team's relationship to the corporate IT group is resolved. A
centralized companywide spatial
database is also established.
When an enterprise GIS is installed with strong performance
and benefits, the C-suite
begins to see it as a competitive force. Stage 5 begins with this
recognition from senior
management, which then adds resources to position the system
competitively. At UPS, for
example, top management realized that the ORION enterprise
routing system was world class
and could create significant cost savings and efficiencies. Once
a spatial system is enabling a
firm to gain on competitors, the challenge becomes to maintain
that lead with further
improvements such as novel analytics and strategic
applications, upgrading of infrastructure,
and embedding useful applications of emerging technologies.

This progression across stages is helped by facilitators
(Davenport and Harris 2017, SBI,
2018). The two most important facilitators are a perception of
the value of location-based
insights for business and the availability of world-class spatial
technology. Practically, these
factors were present for the Walgreens case and will be seen in
this chapter’s BP case. The next
two factors are C-suite support and clear business strategy. In
the case of UPS, covered in
chapter 9, location analytics languished as a project for many
years with little recognition from
the C-suite, until a regional test finally excited the top leaders,
resulting in rapid progress in
maturity stages. The last factor of articulation of ROI in GIS
has less influence and would likely
add to progression through later stages. This is seen in results
from a survey of 200 businesses
(SBI 2018), in which respondents who were asked to indicate
the factors that differentiated
140

stages of spatial maturity. (see Table 8.1). Rather than ROI, the
most important maturity-
facilitation factors were availability of best-in-class location
intelligence technology and value of
location-based insights to the business and customers.
Success in progressing through the stages benefits by efforts by
the GIS team in
engaging leaders and managers outside of the location analytics
area and collaborating with
them in the progression. Among other things, this ensures a
sustainable support base for
location analytics.
Table 8.1 Perceived Net Facilitating Factors of Differentiation
of Spatial Maturity
Facilitator or Inhibitor of Differentia tion of Spatial Maturity
Net Percentage
Facilitating (Facilitator
minus Inhibitor)
Value of location-based business and customer insights 45%

Availability of best-in-class GIS and location intelligence
technology 45%
C-suite sponsorship and support 30%
Clear and coherent business strategy 29%
Clear articulation of ROI of GIS and location intelligence
initiatives 17%
(Source: SBI, 2018)
Management Pathways
Since the location analytics team is rarely located in the c-suite,
which underscores the
importance of the strong technical managers. As mentioned
earlier, such managers can be
crucial to obtaining and retaining a sponsor or champion,
branching out to other middle
managers, leading in improving the quality of data and
spearheading technology and software
improvements. In addition, a manager has responsibilities in
location analytics department

planning, day-to-day management of personnel, hiring, and
coordinating with the IT
department, which tends to be considerably larger than the GIS
group.
As appreciation for location analytics improves a direct
communication tie-in will need
to be made with senior management (Tomlinson, 2013).
Certain steps recommended for effective Location Analytics
Management (Somers,
1998; Tomlinson, 2013) are the following:
implementing projects in a
particular firm. The common system development steps for a
spatial project include
planning, analysis including requirements specification, design
and build,
implementation and maintenance. However, GIS management
needs to be flexible to
141

such factors as scope of project, speed required, human resource
capacity to complete
the project, and extent of control imposed on the project team.
One recommended
step for location analytics projects is, early on, to hold a
technology seminar, which is a
meeting of nearly all the major stakeholders, that has focus on
training for the project,
awareness of its goals and challenges, and gaining
understanding and consensus on the
development and oversight roles involved throughout the
project (Tomlinson, 2013).
need to work on and gain
consensus on a plan for the location analytics. The plan should
have a vision of what a
desired long-term outcome is of location analytics in the
organization (Tomlinson, 2013,
Somers, 1998). The plan can serve as a unified series of steps
building up to important
goals. It should be subject to modification as the planning
period unfolds, and it needs
to be aligned with plans for IT and for the business as a whole.

technology projects, location
analytics projects need to have users involved in desi gning and
building systems, as well
as evaluating systems that are in active use (Pick 2008,
Tomlinson, 2013). Location
analytics is a resource that has multiple potential users and its
outputs are encouraged
to be shared, as much as proprietary or security constraints
allow. Users can discover
beneficial applications that were not planned for, so need to be
added.
managers need to communicate
with multiple stakeholders that include technical team members,
users, vendors, the IT
manager or CIO, and often including senior management.
Communications must be
proactive, timely and appropriate to the level and interests of
the other party or parties
(Somers, 1998). For instance, a short phone call with the VP of
marketing would differ
from an intensive video-conference review of performance with

3 members of the GIS
team. Each exchange is tailored to the time, knowledge base,
and objective of the
communication exchange.
To sum it up, as stated by Roger Tomlinson (2013), a “GIS
manager must not only have a
firm grasp of GIS technology and capabilities, but also be a
meticulous organizer, a strong
leader, and an effective communicator.”
Case Example: CoServ
As an example, consider the role of GIS middle management
at CoServ, a Texas utility
cooperative. CoServ is an electric and natural gas distribution
company founded in 1937,
serving over 250,000 electric meters in six counties north of
Dallas, Texas (CoServ, 2020). The
firm also offers solar renewable energy. The company started in
a region of rural farmland
where a group of residents formed the nonprofit cooperative to
provide them with energy.
Today, to the north and northeast of Dallas, former farmland
has increasingly been converted

into corporate headquarters and other facilities, but CoServ’s
western area still functions, for
now, as a rural cooperative.
142
CoServ originally started its location analytics unit to upgrade
small orange map books
of its electrical systems, which were manually copied for use in
the field. Today CoServ deploys
an enterprise system, web mapping for business units, and full
access to the system by field
personnel on their mobile devices. It also provides corporate -
level enterprise support to
business-unit GIS teams in electric utilities, gas utilities, and
engineering.
The GIS manager of over a decade has developed workforce, set
project goals,
established working relationships with the business divisions,
collaborated on a workable
structure for an integrated IT/GIS department, established
strong relationships with vendors

and outsourcers, and developed visibility for GIS across the
company as well as in the senior
leadership.
The IT/GIS group works together well with the understanding
that the IT department is
in charge of configuring systems, servers, databases, and
networks, but that the GIS team
populates the databases with data, installs the GIS software and
portal, and runs the
administrative accounts. The GIS middle manager has also
worked out a productive relationship
with the spatial teams in engineering, electric, and gas.
Engineering, for example, has
specialized utility design and operations software, for which the
GIS department serves only as
a consultant if needed. Likewise, gas and electric GIS groups
use SCADA software to monitor
transmission and pipeline flows, which central GIS installs and
supports as needed.
The largest and most challenging project has been developing
the enterprise location
intelligence. The GIS manager and team realized this would
take many months of effort, not

just technical effort but also coordinating end users, scoping the
steps of the project, going
through iterations of testing, changing time-worn processes, and
training users. The system
succeeded with electric utilities and well along the process with
gas. The web-map portal built
on top of the enterprise base has been popular with end users
since they can customize spatial
applications within hours or days, rather than waiting weeks.
Overall, the CoServ story exemplifies many of the key
responsibilities of location
analytics management: developing an approach that works in a
particular firm -- long-range
planning, coordinating the GIS team with users, and effective
communication.
Applying management principles to spatial transformation
Digital transformation is a process of applying digital
technology to creatively and
fundamentally change a business, including its existing culture,
organization, and business
processes (Tabrizi et al. 2019). Since GIS and location
intelligence are becoming increasingly

digital, organizations are concurrently undergoing spatial
transformation, which we define as
the part of digital transformation that involves locational
processes and cross-organizational
and cultural changes. If a business is reimagined to have its
geospatial information and
processes in digital form, based on such features as web maps,
portals, cloud computing,
broadband internet, 3D or 4D visualization both inside and
externally facing, and if this changes
the way business is conducted, then spatial transformation is
underway. As part of spatial
transformation, people also change in their job roles, skills,
productivity, and decision-making.
143
A business that is spatially transformed is usually in the 4th or
5th maturity stage, so
that spatial applications have permeated the organization and
may already be a competitive
force. For instance, the BP case at the end of the chapter
exemplifies location transformation in

the 4th maturity stage.
A practical view of spatial transformation posits a series of
steps (McGrath and
McManus 2020, Harvard Business Analytic Services 2020):
ration experience (McGrath and
McManus 2020), i.e., indicate
which locational elements or tools will be digitized, beyond the
state they are in now.
An example would be changing from a disjointed set of maps
showing each step in a
supply chain to having an integrated digital display of the entire
supply chain from raw
material to customer.
operating experience. Although
technically trained people are required, there is equal need for
investing in people with
soft skills who are creative, adaptable, and flexible
(Frankiewicz and Chamorro-Premuzic
2020).

-based problems and use metrics.
For instance, in the State of
Connecticut, truck routing displays were transformed from 2D
to 3D to solve the
problem of trucks being too large for safe passage on routes
with bridges, overpasses,
and tunnels. 3D mapping enabled precise measurement of the
maximum allowable
dimensions for truck transit.
to maintain focus on
having extensive, high-quality
data upon which to base the spatial transformation.
the user to arrive at or create
solutions that can reside on top of a stable platform. An
ecosystem implication refers to
the interfacing of a robust enterprise spatial platform with the
platforms of other
collaborating businesses or organizations. For example, the
Arizona Republic newspaper
derived competitive advantage by applying a GIS system to
select geographic areas

suitable to a particular advertiser and collaborating with
advertiser systems that
supported decisions on what goods and services to advertise
(Pick 2008).
transformation changes the business
profoundly, to the extent that senior management becomes the
driver (Frankiewicz and
Chamorro-Premuzic 2020).
Many companies seek digital transformation, but fewer succeed.
In a 2020 poll of 700
executives, 95% indicated that digital transformation had grown
in importance over the past
two years, and 70% pointed to digital transformation as
significant, yet only 20% evaluated
their own firm’s digital transformation efforts as effective
(Harvard Business Analytic Services
2020). These findings point to the difficulty of succeeding in
digital transformation but also to
144

the competitive opportunity for the firm that does succeed. The
same study emphasized that
cultural change may be a barrier to overcome. In the instance of
spatial transformation, culture
that is set in its use of legacy approaches to GIS can be
transformed by top management setting
clear business goals, communicating those goals, and using
indicators to check performance in
reaching them (Harvard Business Analytic Services 2020).
Leadership and championing
Leadership is crucial in spatial business. A leader provides
vision to an organization,
communicates the vision to the people he oversees, motivates
and inspires them to work
toward the vision, and enables this effort among the relevant
people in the organization. The
leader or champion of GIS and location intelligence in a
business has even more challenge
because spatial and GIS are frequently unfamiliar concepts that
require persistence and
patience to “educate” personnel and stakeholders about what
GIS is and why it is important for

business. This added challenge is evident in some cases, such
as the long delay at UPS for
spatial to be recognized by upper management or by the
continual challenge and only mixed
success at one of the leading global commercial real estate
companies in educating and
persuading country business units outside the U.S. about GIS
and why it can be significant.
Among the qualities of spatial leadership that are recomme nded
are the following:
behavior and beliefs, on
which others in the organization or unit can model their
behavior.
think up and present a vision
of the business. It is essential that the vision be shared
throughout the organization or
portions of it overseen by the leader. This can best be achieved
by inspiring
subordinates to “buy off” on the vision, rather than by coercion.

others to do most of the
work, so needs to support their efforts. This effort is often done
in teams. The leader
builds trust in subordinates and teams and he needs to continue
to stay engaged with
the work as it is carried out, sometimes over considerable time.
2017). The effective leader
must reach out to seek to establish friendship with subordinates
and stakeholders. She
should offer guidance and genuinely be concerned about the
people who are doing the
work and carrying the projects forward.
Specific approaches recommended for leaders of geospatial and
location intelligence in
business (Kantor 2018; SBI 2018) include setting priorities,
which lead in turn to strategies.
This can be seen in Figure 8.2, which defines how to get to
vision by setting priorities,
leading to strategies, and turn strategies into implementation
(SBI 2018).

145
Figure 8.2 An illustration summarizes the components of the
three major steps in achieving location strategy of establishing
priorities, determining strategy, and implementing the strategy
(Source: SBI, 2018)
As an example of setting priorities that lead to strategy, the
location analytics leader
may set as a top priority the development and implementation of
location analytics with big
data to provide for improved logistics throughout North
America. Further priority areas that the
spatial leader might address are the following (Kantor 2018):
social media sentiment
analysis to reveal changes in attitudes across geographies.

dge of the customer through
locational tagging of customer
ordering patterns in time and space using mobile apps.
Other dimensions of spatial leadership are corporate social
responsibility and the
awareness of the privacy and ethical implications of location
intelligence.
Examples of ethical spatial leadership occurred in the 2020
COVID-19 pandemic, during
which leaders of several software vendor companies put profit
motives aside and provided free
spatial software and services to help nonprofit organizations,
academia, research institutions,
businesses, and governments monitor the geographical spread of
the virus, optimize the
delivery of needed supplies, and track down the contacts of
infectious individuals through geo-
referenced social media apps while preserving the privacy of
those individuals.
146

Privacy and Ethics in Spatial Business
Notwithstanding the tide of expanding benefits, efficiencies,
and innovation from spatial
business applications, location intelligence also presents
companies and their spatial leaders
with ethical dilemmas. Location has its own set of ethical issues
associated with it. Often a
decision has to be made that must balance risks between
locational value at one end of the
scale and harm at the other end—unintended or intentional harm
to customers, employees, or
the general public.
Here, we raise privacy, one of these ethical concerns, to
highlight the breadth of
challenges, and suggest several ways to consider approaching
them. Location privacy is defined
as control over the locations of people and their associated
personal information and over the
primary and secondary uses of this information. This ethical
issue has grown to widespread

prominence through the proliferation of methods to determine
the location of people and
assets, and the increasing accuracy of these methods.
The escalating use of cell phones worldwide means that the
locations of billions of
people can be tracked over long periods without their
knowledge. At the same time, satellite
imagery of the planet is available from dozens of providers at
varying scales, resolutions,
spectral wavelength, and frequencies of collection. Imagery can
be scanned rapidly by machine
learning to identify features and at very high resolution to
recognize vehicles, people who are
outdoors, and other identifiers. People's locations can also be
collected from fixed video
cameras and wearables.
A location-tracking industry has sprung up that is unregulated
in the US and sells
detailed tracking information about people and assets to other
companies and organizations.
The ethical issues of location privacy are relevant to purveyors
of geo-referenced data, but also
to executive decision-makers in the location-tracking industry

and to companies that purchase
and make use of the location information.
An example of the ethical issues for personal privacy can be
seen in a database from a
location information firm that was provided anonymously to a
research team at the New York
Times (Thompson and Warzel 2019). The database, with 50
billion location pings from cell
phones of over 12 million people in the US, reveals the ordinary
daily-life mobility patterns of
individuals—but also unusual patterns, including visits to drug
rehabilitation facilities, doctor
offices, or more worrisome places. The individuals have
consented to reveal their location by
clicking “yes” to the legal notices that pop up with many
applications. Some software apps
include little pieces of code called SDKs that provide normal
location information for the app,
but also can be collected by location information firms
(Thompson and Warzel 2019).
Given the growing threat of intentional or unintentional misuses
of personal data and
many other situations where location information and analytics

have the potential for harm,
what can the individual do to protect herself? What can the
company and its leaders do to
protect workers and customers and ensure that spatial business
adheres to ethical standards?
147
Measures that can help to reduce this threat include the privacy
policies of businesses
and government regulation, as well as tools for anonymizing
information and data obfuscation
(Duckham 2013). Companies can set high standards that reject
misuse of personal information,
including not allowing purchase of unconstrained location files,
and can require GIS, IT, and
business managers to weigh the ethical balance in any decision
presenting potential for
locational harm. In addition, regulation needs to come from
federal and state legislation, which
so far in the US has not sufficiently regulated private-sector
data controls outside of some
industries such as healthcare and banking. The US Constitution

does support some aspects of
locational privacy rights through the Fourth Amendment,
although more legal tests are needed
to clarify these rights (Litt and Brill 2018). In Europe, the
General Data Protection Act of the
European Union has constrained use of personal information
without permission but does not
include any substantive regulation of location privacy. On the
individual level, anonymizing and
obfuscating data can be effective, up to a point, but is often
difficult for the individual to
control, especially with large spatial datasets.
Developing Spatial Business Workforce
This chapter concludes with a critical management and
leadership concern, which is
how to recruit and develop and a location intelligence
workforce, in a market that has limited
supply and growing demand. The concern extends beyond
internal training within businesses
and must consider the opportunities and constraints in society
on how geospatial workforce is
being educated and trained.

Since skilled team members and managers at different levels are
crucial to supporting
business use of location analytics across the spectrum of stages,
spatial workforce development
is essential. This topic has received attention from varied
stakeholders including universities,
governments, professional groups, and businesses. Several key
questions arise, among them:
What mix of geospatial knowledge and skills is needed for the
challenges of working in
industry? How can universities best prepare future workers in
spatial business? What is the
market in the US for geospatial in business?
In terms of knowledge and skills needed, a general reference
point is the Geospatial
Technology Competency Model (GTCM), that was developed
through a collaboration between
the U.S. Department of Labor Employment and Training
Administration and GeoTech Center
(DiBiase et al., 2010, GeoTechCenter.org, 2020) (See Figure
8.3).
148

Figure 8.3 A pyramidal diagram of the Geospatial Technology
Competency Model includes personal effectiveness at the base;
academic, workforce, and industry competencies in the middle;
and management competencies and specific occupational
requirements at the top
(Source: U.S. Department of Labor, 2020)
In this model, multidisciplinary academic competencies and
workforce competencies
form a foundation for industry competencies. The GTCM
includes broad human competencies,
such as interpersonal skills, combined with knowledge of
geography, science, engineering,
math, computing, critical thinking, and communications. The
workplace competencies include
business fundamentals, problem solving, planning and
organizing, and teamwork. The upper
half of the pyramid emphasizes management competencies, as
well as technical competencies,
management competencies, and occupation-specific
requirements.

The level of knowledge and skills depends on the overall
responsibilities of the position.
Table 8.2 outlines these spatial business GTCM related
competencies at the entry level (e.g.,
analyst), mid-level (e.g., manager), and senior level (e.g.,
director). As such it also serves as a
model pathway for a spatial business career. To that end, a
sample of existing position titles are
provided across a range of industry sectors.
149
Table 8.2 Spatial Business Competencies
Spatial Business
Competencies
Spatial Business Career Pathways

Entry: Analyst Mid: Manager Senior: Director
Academic Required undergraduate
degree, preferably with
business and GIS
elements.
Certificates can
substitute for business
and GIS elements.
Preferred advanced
degree (at Master’s level)
with business and GIS
elements.
Certificates can substitute
for location analytics in
degree.
Required advanced
degree (at Master’s level)
with business and GIS
elements

Certificates can substitute
for location analytics in
degree.
Workplace Preferred 1-3 years of
experience in workplace
environment involving
GI.S
Required 3-5 years of
GIS.
Required 5-10 years of
GIS.
Industry Wide -
Sector
Industry sector
experience preferred but

not required.
Ability to manage
technical and business
aspects of selected
industry.
Ability to lead location
analytics enterprise to
achieve business goals
and strategies.
Management Management experience
not required.
Required 1-3 years
supervisory experience.
Required 3-5 years
management experience.
Sample Current
Job Titles
Senior GIS Analyst

(Silicon Valley wireless
services start up).
Business Systems (GIS)
Analyst (regional utility
company).
Geospatial Data Analyst
(national security
company.)
Senior Product Manager,
Location Intelligence
(global business data
consulting firm).
Geospatial Strategy and
Analytics Manager
(regional e-commerce
company).
Manager of GIS
Analytics and Insights
(large regional grocery
company).

Executive Director,
Enterprise Location
Intelligence (global
pharmacy company).
Director Geospatial Data
Science (global life
insurance company)
Associate Director,
Geospatial Services
(national non-profit)
Universities play an important role in preparing a spatial
business workforce at each
level of knowledge, skills and responsibilities noted above.
What is needed for this model to
work well in practice is coordination between academic
preparation, workplace training, and
150

mentoring from experienced geospatial professionals. Ideally,
there would be a spectrum of
programs that integrate business, geography, GIS, location
analytics into a unified curriculum,
with practicums of students networking with industry people in
communities of practice.
A starting point concerns the status of university spatial -related
programs and whether
they recognize and include suitable preparation for business
careers (Marble 2006, Dibiase et
al., 2010; Tate and Jarvis, 2017). To date, education that
integrates critical areas of business and
geography/GIS are relatively rare.
For example, Geography departments have been viewed as the
most important source
of geospatial workers and with good reason, since GIS’s
original base disciplines in the 1960s
and 1970s were geography and cartography (Solem, 2017).
However, the discipline of
geography is becoming more multidisciplinary (Tate and Jarvis
2017). Correspondingly, while
business schools have grown Business Analytics components in
the curriculum, many have

lagged in integrating location analytics into their curriculum
(Sarkar et al., 2020).
While more integration will undoubtably occur as the needs for
these knowledge and
skill continue to grow, professional certificates can also serve
as useful complement to
traditional academic training. For business degrees, this can be
a certificate in Location
Analytics. For GIS degrees, this can be a certificate in a
business area such as marketing and
supply chain management. Another suggestion for broadening
GeoSpatial education (Solem,
2017) is to encourage education complementing standard GIS
technical courses with courses on
holistic topics of ethics, human welfare, citizenship, social
relations, and well-being (Solem,
2017). The goal is “to promote individual autonomy and
freedom through imagination.” This
approach can provide broader viewpoints of societal dimensions
that could be addressed
through location analytics.
Overall, the value of spatial business academic and professional
training is high.

Although the US federal government does not have separate job
categories for Geospatial
managers, analysts, or geospatial specialists, 2020 US Bureau of
Labor Statistics data for related
categories such as cartographers, geoscientists, statisticians,
and software developers show
high rates of growth, pointing to demand-driven job markets for
spatially-educated people
entering the workforce in the 2020s.
Communities of Practice
Several studies of Geospatial education in geography have
called for broadening
Geospatial education considerably beyond traditional academic
topics by introducing
communities of practice and broad capabilities of thinking
about life (Tate and Jarvis, 2017;
Solem, 2017). Communities of practice are proposed as a
bridge between academia and
industry. As seen in Figure 8.4, the GIS community of practice
includes current and newly
graduated GIS students along with two types of experienced
professionals, those with a
graduate degree in GIS and those without a formal degree (Tate

and Jarvis, 2017). The
community is regarded as complementing formal courses. It is
suitable to virtual form. It
provides an informal way to bridge the barrier between active
business workforce and
151
students, with information exchange going both ways, since the
business practitioners confront
real projects without disciplinary boundaries and recent and
current students bring insights in
technologies, platforms, concepts that increase lifelong learning
and awareness even among
business professionals.
Figure 8.4 A graphic illustrates the GIS community of practice
in the middle, which consists of and unites current students,
recent student alumni, business GIS professionals with GIS
degrees, and other GIS professional schooled on the job
(Modified from Tate and Jarvis, 2017)

An example of job preparation and transition to industry is the
career progression of
Beth Rogers, shown in figure 8.5, who went from an
undergraduate education in biology to a
MS in geoscience and then to a first business job as an
intermediate software developer at Fruit
of the Loom, a major underwear and clothing company that is
part of Berkshire Hathaway
(Kantor 2020). She joined the firm’s analytics group, which has
access to a vast database of
company clothing data. Although the environment and problems
were very different from her
graduate school study of the geography of fish, she appl ied her
spatial background to develop
algorithms that saved shipping costs in supply chains and
relocated products to alternative
distribution centers. On the job, she received mentoring from
the CIO and others and was so
successful that Beth was advanced in several steps to senior
manager of data science (Kantor
2020). This story illustrates how a solid and broad science
education can be converted, with
mentoring, into an intensive and rapid-moving sequence of
spatial business positions.

152
Figure 8.5 Beth Rogers, a senior manager of data science at
Fruit of the Loom is pictured in a photo
Concluding Case Study: BP
BP (formerly British Petroleum) is the world’s seventh largest
petroleum company, with
2017 revenues of $223 billion and 70,000 employees. In the
mid-2010s, a decision was made by
Senior BP management to overcome mounting IT glitches with
its legacy system by initiating
companywide digital transformation (Jacobs 2019; Venables
2019). A major transition from
maturity stage 3 to stage 4 was affected by rolling out an
enterprise architecture, which the
Geospatial Lead and team named “One Map.”
The One Map enterprise platform replaced the existing
decentralized silos with an

integrated system that includes big data across the enterprise,
IoT, access for any authorized BP
employee, inclusion of all static, real-time, and historic data,
and storage in regional clouds for
more rapid response. The platform includes systems of record
for field assets, as well as spatial
analysis and location analytics tools (Boulmay 2020).
Infrastructure is regional, consisting of in-
country server installations, each with enterprise servers, portal,
and full geospatial software.
With this modern platform, any user anywhere worldwide, with
permission, can access the
complete set of BP map layers and information. Monthly, there
are over 2,100 active spatial
portal users and over 4 million map service requests. For
backbone applications, automation of
widely repeated workflows is emphasized, which simplifies use
and maintenance (Boulmay
2018).
153
BP’s small Geospatial team was originally tasked a decade

earlier to support four siloed
US BP business units spread over four states. BP made the
decision to leave critical specialist
systems inherited from the silo period in place, but now they
also are linked to the enterprise
backbone, which supports a common enterprise-wide centralized
database that contains over
40,000 datasets (Boulmay 2019). This allows robust storage of
data accessible across the
company’s matrix of regions, portable devices, and functional
teams. Through collaborative
conversations and organizational networking, the GIS manager
ascertained that specialized
business units preferred to develop their own dashboards and
visualizations, which put those
analytics applications in the hands of the specialists who
understood pipeline design or raw
material supply chain routing. Specialist analytics were handed
over to special teams known as
“citizen developers” (PESA News 2020).
The Geospatial Lead exemplified spatial leadership in other
ways as well. He rolled out
continual training and emphasized marketing and justification
of GIS at maturity stages along

the way. He also realized that, after building standardized
software, infrastructure, and data, he
needed to lead by shifting attention to the data services that
were being used by the most user
departments and emphasizing the high-use applications. An
example was leading in supporting
the mapping and spatial analysis required for the COVID-19
pandemic. The standardized central
data could be analyzed in new ways, and quickly, allowing
valuable reports to be produced
within days.
The lessons from the case are reflected in the facilitating factors
of spatial maturity, as
seen earlier in Figure 8.1. All along the way, it was important
to educate internal "customers"
i.e. managers of peer groups and, later on, higher level
executives in the value of location
intelligence. The geospatial platform was carefully chosen as
best in class. As it grew and
matured, the spatial unit had a consistent strategy of providing a
platform while letting the
users develop applications. The C-suite became supportive and
directly involved as part of the
location-intelligence enterprise roll out. Specific lessons at BP

are summarized in Table 8.3.
Table 8.3 Digital Transformation at BP – 8 Lessons Learned
Lesson 1. Embrace the enterprise platform
Decide which part of the organization should take ownershi p of
geospatial capabilities. Do not try
to do everything.
Lesson 2. Find the right home. For BP, the original home was in
the subsurface business under the
Upstream segment of the firm.
Lesson 3. Set your data free
Make data across the entire organization as freely available as
possible, subject to security and
privacy constraints.
Lesson 4. Let business users extend the platform
Provide tools that are as user friendly as possible, delegating to
business users the extension and
upgrading of applications in their areas.

154
Lesson 5. Tailor user support to the new paradigm
Foster collaboration between the location analytics team, IT,
and the business units, which leads
to support.
Lesson 6. Automate
If technically feasible, automate a workflow early if it is likely
it will be repetitive.
Lesson 7. Mind your marketing
Market concertedly the locational transformation internally and
externally.
Lesson 8. Measure success
Evaluate the locational transformation at steps along the way.
Afterwards review of the steps can

provide invaluable feedback and lead to necessary tweaks and
improvements.
Lesson 9. Build and maintain teamwork and appreciation for the
players.
Give attention to support and appreciation of the team members.
Lesson 10. Communicate clearly and widely and repeat the
message as needed
Keep a focus on communicating activities and accomplishments.
(Source: modified from Boulmay 2018)
As this system rolled out, the small team shifted from reporting
to a small-scale business
unit, to reporting to corporate IT, and later to the leaders in core
business divisions served in a
major way by the system, since they had the deep business
knowledge of value and workflows
(Boulmay 2018). Subsequently, senior management came to
understand that geospatial
transformation was a key part of the corporation’s digital
transformation, which increased their

interest and support. In the latest reorganization of the company
with the arrival of a new CEO
in early 2020, the Geospatial team joined a new high-level
group, innovation and engineering, a
locus for the people leading BP’s corporate digital
transformation, a key goal of the CEO.
155
CHAPTER 9
Strategies and Competitiveness
Introduction
Geospatial strategy begins with justification of the importance
of location analytics
weighed against multiple other uses of organizational resources.
Decision-makers need to ask
how strategizing will pay off, and how, where, and why location
value can be realized. Then, if
strategic planning is to occur, what are the formal steps? How
can policy be turned into action

and how can it be kept current?
For information systems, a well-known success factor is to
achieve alignment of IT
strategy with business strategy (Peppard and Ward, 2016). This
means that parts of IT strategy
complement and strengthen the corresponding parts of the
business strategy. For instance, if
the business strategy seeks to offer spatial decision-making to
an organization’s field workers,
the IT strategy seeks to provide decision software on a cloud-
based delivery platform that
connects the mobile devices of the workforce worldwide. The
importance of alignment likewise
applies to Location Analytics strategy (Lewin, 2021).
Geospatial strategic planning has both external and internal
elements. The external
element focuses on how location analytics can be used to
strengthen the firm’s competitive
position, or modify forces affecting competition, such as
customer relationships or new
products. Internal planning emphasizes improving the firm’s
own GIS infrastructure and
processes. The internal element focuses on the alignment with

business needs, technological
capacity, and human resource requirements to achieve desired
location and business value.
Several themes have arisen in this book that provide strategic
themes for organizations
to consider. These include:
eeds
Growth

Management to Capture Vision and Deliver Impacts
156
This chapter considers these themes within the context of
strategic planning and the
next chapter elaborates on their implication for practice.
Geospatial Strategic Planning
Strategic planning is standard practice for middle-sized and
larger organizations,
whether a business, university, or governmental unit, and
strategic plans are often required by
the senior management or the board overseeing the organization
(Hitt et al. 2016, Hitt, 2017).
Likewise, IT planning has become commonplace in
medium/large organizations (Peppard and
Ward 2016). Formal Location Analytics planning is growing
and shares many concepts with IT
planning, but has some unique features examined in this chapter

(Pick 2008; Lodge 2016). Also,
there are many organizations where a informal geo spatial
strategy is developed even if not
performed as a formal “plan’.
Steps to Development of a Geospatial Strategy
1. Initiate the plan with self-assessment and identify the
business issue(s) or opportunity(s)
that location analytics is intended to address.
Getting started on a geospatial strategy is challenging,
especially is it takes resources
away from short-term projects and ongoing operations. For that
reason, the strategic effort
should start with the highest-level senior manager or executive
who is responsible for the
overall outcomes of the strategy. The self-assessment needs to
consider the capacities of the
firm’s human resources, IT infrastructure, finance, and
management, and how they compare to
the demands of the location analytics strategy (Peppard and
Ward 2016; Piccoli and Pigni
2022).

2. Identify the current Spatial Business Architecture
components of the company.
They include people, infrastructure, data, applications, users,
business unit emphases on
location intelligence, and governance. This step gives an
overall picture of the present state of
GIS throughout the enterprise and the current capabilities.
3. Determine what the value-add benefits of the geospatial
strategy are to the corporation.
Evaluate the value benefits for a new or enhanced location
analytics capacity indicate
how it helps the firm’s business objectives. Tangible benefits
are measurable, such as the
benefit of faster delivery times, greater value-added
productivity, and measurable increase in
customer satisfaction. Intangible benefits include such non-
monetized advantages as high-
quality executive decision-making, improvement in company
brand image, and improved
readiness to cope with supply chain interruption.
Costs must also be appraised, including hard costs such as

personnel, spatial software,
data, virtual infrastructure, servers, and facilities, as well as the
soft costs of insurance, security,
157
and consulting. The net location value is the difference of
location analytics value benefits
minus costs.
In considering value, look across the relevant business
functions, and well as appreciate
social, community and environmental dimensions that may be
appropriate. Such an approach
reflects corporate social responsibility aspect of the geospatial
strategy.
4. Assess markets that new or substitute products and services
can be released into.
The Porter (2008) competitive forces model can be applied to
assess the
competitiveness of spatially- enabled products being released

into a market. As outlined below,
new and substitute products and services can alter the existing
dynamics of competition. Other
competitive forces that can alter the market are changes in
relationships of a firm with buyers
and suppliers. The geospatial strategy needs to assess the
competitive impacts for spatially-
enabled innovations.
5. Determine the vision and mission location analytics as a
component of the company.
The geospatial vision is a picture of what the company’s
location intelligence will be in
the future. It will serve as a guide for the organization to reach
its imagined role for location
analytics in the future and how location intelligence will
operate. It will reflect a full realization
of value to a variety of customers. This vision will be
complementary to the firm’s business
vision.
The geospatial mission is a statement of the key long-term
broad purpose of location
analytics in the organization. For instance, the mission might

be to achieve world leadership in
applying location intelligence in evaluating insurance risks. Or
the mission might be to have the
most accurate geospatial prediction for siting new car
dealerships in Brazil. The mission is often
brief so that it is understandable and can have wide adoption
throughout an organization. It
serves to unite the firm’s stakeholders around a primary goal.
A leadership team should include both those with technical and
business expertise to
ensure the vision and mission capture both the business strategy
and role of location analytics
in achieving the business strategy. They should be included as
participants in the vibrant back-
and-forth of discussion, argument, and eventually consensus on
mission and vision. Sometimes
this participation has been reduced, but as seen in the review of
location maturity stages,
gaining a voice in upper management decisions is a key factor
to succeed in attaining stages 4
and 5. Geospatial strategy progression has floundered without
both business and technical
expertise being included in formulating vision and mission. For
example, in a global commercial

real estate company, the leader of its geospatial i nitiative was
excluded from corporate
strategy-setting and GIS became a “hard sell” around the
company, falling way short of its
potential.
6. Define the scope of infrastructure to achieve the strategic
objectives.
What technical steps need to be accomplished? These might
include building software
applications, outsourcing to a cloud platform, providing
intensive spatial training for new
158
managerial users, prototyping innovation in emerging spatial
technologies, or strengthening the
physical geospatial infrastructure (Lodge 2016). Scope can be
examined in terms of
departments and regions, or in terms of internal and external
stakeholders.

7. Assess risks.
The strategy being proposed should consider risks of financial
losses, lowered quality of
outcomes, underperforming personnel, poor management
decision-making, reputational
damage, and adverse events in the external environment
(Peppard and Ward 2016). For
example, in a global commercial real estate company, the leader
of its geospatial initiative was
excluded from corporate strategy-setting and GIS became a
“hard sell” around the company,
falling way short of its potential.
Some of the other risks include:
decisions is missing.
alignment with IT and business
strategies.
insufficient to support the GIS

improvements called for in the strategic plan.
lue is underestimated because intangible value is
not recognized.
with very short life cycles, so
the benefits are reduced by the need for constant redesign.
nge in unexpected ways,
requiring major revision of the
GIS strategy.
If carefully conceived, a geospatial strategy can serve as a
touchstone over several
years. Smaller activities and projects can be measured against
the plan in approving them for
funding. This approach has been shown to steadily move an
organization towards achieving its
strategic goals. However, the external environment and markets
may change more rapidly than
the pace of the strategic plan. An example is the COVID-19
pandemic, which impacted many
firms in their spatial deployments, depending on industry.
Consider, for instance, the map-

intensive sharing economy for on-demand ridesharing. In
ridesharing, the GIS benefits of
locational intelligence in Uber and Lyft cars were overshadowed
by a sharply reduced customer
base. On the other hand, the pharmaceutical industry benefitted
by urgent need to apply
predictive location analytics to optimize vaccine supply chains.
Building a robust and complete Geospatial Strategic Plan that is
updated regularly
serves as a reference point for all the location-value
contribution that a company should strive
for, yet the extent of strategic planning tends to vary by the
spatial maturity stage of the firm
(Peppard and Ward 2016).
159
When a geospatial strategy is undertaken as an effort within a
nascent department
(stage 2 from chapter 1), the spatial strategic focus can be
limited to finding the immediate
opportunities, obtaining sufficient technology and determining

the value-added of the
endeavor (e.g., Steps 1-3).
As spatial maturity progresses to stage 3, the focus of planning
typically shifts to
incorporate strategic goals. At stage 4 of enterprise platform,
most of the recommended
strategy planning steps are in effect. Since spatial awareness is
now firmly implanted
throughout the organization, coordination of location strategy
with the company’s business
strategies might now be initiated by members of the senior
executive team. Finally, in maturity
stage 5, the spatial strategy has matured to include research on
competitors, risk analysis, and
the appraisal of innovative spatial technologies.
An example of making the shift to more comprehensive
strategic planning is seen for
British Petroleum, discussed in chapter 4. BP jumped, in a
transformative single year, from
planning on a multi-departmental performance basis to
enterprise-wide planning keyed to
strengthened connections to senior management. Maturation of
spatial strategic planning

paralleled the jump to a firm-wide, enterprise locational
approach for competitive advantage.
Shifts of strategy as location intelligence has progressed in
maturity also is seen in Hess
Corporation (see Appendix).
Case Example: United Parcel Service (UPS)
UPS is the world’s largest package and delivery company, with
481,000 employees,
performing over 2.3 billion route optimizations annually and
serving over 2,000 facilities in 220
countries and territories (Westberg 2015; Perez 2017; CFRA,
2021). Its revenue in 2020 was
$84.6 billion (CFRA 2021). It represents a case of long-term
geospatial investments that have
come to be at the core of the corporation, but not without some
setbacks.
In 1991, the firm introduced the first Delivery Information
Acquisition Device (DIAD). At
the time, the DIAD was an innovative mobile device for the
UPS driver that provided updated
delivery information and allowed drivers to electronically
capture information throughout their

route. Today, the latest DIAD includes these features, plus
scanning of bar codes, tallying of
cash on delivery, a programmed personalized map route that can
be modified during the day
with corresponding route stops and timecard information, and
many other features.
UPS’s ORION delivery system is one of the world’s most
sophisticated and powerful
locational optimization systems (Westberg 2015, Chiappinelli,
2017). It minimizes the driver’s
daily route based on advanced optimization models, data from
planning systems, and
customized map data (Westberg 2015). By optimizing
throughout the day, the UPS delivery
truck might take surprising and unexpected paths. For example,
for the next delivery, the truck
might drive past four delivery points without stopping, on the
way to a more distant fifth point.
Though this seems puzzling, the counterintuitive reason is that
it would optimally save travel
time, gasoline, and money to proceed directly to the fifth point
and return later to the missed
160

points. This was a giant step forward from UPS’s previous
routing routines, which did not
optimize beyond the next delivery point.
The GIS strategic planning had a slow start. Jack Levis,
formerly UPS’s Senior Director of
Process Management, started at the firm in 1975 and took over a
project in 2002 to provide
spatial optimization for UPS delivery routing. He and his small
team produced extraordinary
innovation in mapping optimization for the UPS fleet but lacked
a strategic plan and
management support to operationalize the mapping software
until 2012. In that year, senior
UPS management finally gave him the go-ahead to prototype it
for one region. The results were
startling, showing high ROI and driver satisfaction. The ORION
software quickly became a UPS-
wide strategic initiative, and still remains at the core of UPS’s
integrated data infrastructure,
now with additional regulatory and service components (Perez
2017). The contemporary

ORION includes dashboards for control in the local delivery
office. In Figure 9.1 UPS workers are
viewing a map to plan daily ORION deliveries on a monitor,
along with a handheld DIAD.
The lesson of the decade-long, low-profile ORION testing and
the subsequent decade-
plus years of strategic use, highlights several points. An
important point is that, in retrospect,
the development period was too long, given the subsequent
competitive strength of ORION.
During the ten years of R&D, this project had not been included
in the strategic business plan or
IT plan of the company. Due to this misalignment, the R&D
innovation was somewhat siloed
until its practical importance was suddenly realized (Levis
2017). After 2012, ORION turned out
to be the force of direct competition, a “secret weapon”
developed internally; the system is
currently generating over $400 million in annual cost savings
and avoidance (Gray 2017).

161
Figure 9.1 Two UPS logistics and delivery employees are
looking at a map from the company’s mammoth ORION
delivery
system in contemplating routing, with one employee holding a
DIAD mobile device that can access ORION on-the-spot in
deliveries
(Source: Pittsburgh Post-Gazette, 2020)
UPS now places strategic emphasis on improving ORION

further, developing a real-time
and dynamic updated version with even more complete global
coverage. This real-time version
has a much shorter development cycle of two or three years
(Gray 2017). For the future, CIO
Juan Perez foresees the “dispatch [of] a fleet of autonomous
package cars each morning that
are guided by a real-time version of ORION” (Perez 2017).
Another service under R&D is NPT
(Network Planning Tools), which builds on ORION with a
mixture of artificial intelligence,
advanced analytics, and optimization, a set of tools that can
yield better efficiencies (Perez
2017). ORION and its successors are now part of UPS’s
strategic business plan, aligned,
prioritized by senior management, and highly competitive.
Location Analytics Strategy in Small Business
Location Analytics strategy development for a small business
follows the seven steps
given earlier in the chapter, but has some distinguishing
features that arise from the limited
resources available and shifts in the challenges of competition
and collaboration. They include

the following.
-assessment of spatial capabilities may be more
challenging due to a small-firm
senior management’s lesser awareness of location intelligence.
It may encourage more
proportionate use of outside spatial consultants.
is essential. For example,
a small blinds and draperies firm adopted web-based single-user
business mapping
software to compete effectively with a larger number and more
numerous and varied
range of competitors over a large geographic market.
Accordingly, the GIS strategic goal
to understand the expanded geographical patterns of
competition was aligned to the
business strategy.
the small business can more
quickly survey its people, resources, software, data, etc.
Typically, in a small firm,
resources are scarce and hence it may be they have to adjust the

strategy to make the
most use of internal and external talent.
regarded as too difficult and
time consuming for a small staff.
face asymmetrical
competitive forces from large, incumbent firms.
diluted in the “haste to get
to market” (Gans et al., 2018).
162
since firm is often
innovating into an unfamiliar markets and environments. For
instance, the small blinds
and draperies firm felt it lacked its own capabili ty to determine
its GIS risk, so turned to

US Small Business Administration, which in turn referred the
company to a nearby
university to help in determining risk.
On the plus side, innovation may benefit by the small firms’
flexibility.
In summary, the same seven steps in formulating GIS strategy
apply also for small
enterprises, but with less geospatial internal workforce
capability, time pressure from
leadership to move rapidly to implement, and heightened
competitive forces from much larger
players. Consequently, locational intelligence strategy may be
cast aside in the rush to get to
market.
Location Analytics Strategy in a Small Business: RapidSOS
RapidSOS is a small, growing private firm founded a decade
ago, that developed and
offers an innovative approach to enhance the readiness of
response to 911 distress calls, by
sourcing and organizing location intelligence and other enriched
digital information to

accompany the distress calls sent to 911 emergency centers,
which are relayed to first
responders. The business and societal importance of providing
fuller and more accurate
information to expedite emergency response has been
accentuated in the covid-19 pandemic
which at its peak contended with issues of an overload of
critical 911 calls.
The enriched information that RapidSOS sends, along with the
call, to emergency
communication centers (ECCs) includes the accurate location of
the caller, information on her
real-time health, vehicle crash indicators, the caller’s personal
profile, and building security
features at or nearby the emergency location (RapidSOS, 2021).
The enriched location
information can also be passed directly to government
emergency dispatch centers, known as
PSAPs (Public Safety Answering Points) (see Figure 9.2). A
first responder who is dispatched
from an ECC or PSAP with the enriched information is quicker
to arrive at the emergency, more
prepared, and more knowledgeable in addressing the emergency
situation. For example, in a

boating accident in Florida, a retiree fell overboard and was
tangled in lines in freezing water in
mortal danger. He reached for his cellphone in a waterproofed
pouch, and phoned 911. The
RapidSOS-enhanced information informed a helicopter of his
exact point location, including
considerable background information on him, that led to his
rescue and resuscitation
(RapidSOS, 2021).
163
Figure 9.2 The RapidSOS emergency management system is
illustrated in a diagram that shows sensors of emergencies that
register an emergency situation and feed information into the
firm’s 911 Clearinghouse of data that are processed and sent
to government 911 Emergency Centers, first responders, and
emergencies contacts
(Source: RapidSOS, in FinSMEs, 2018)
Location intelligence is at the cornerstone of RapidSOS’s

business model. The model is
based on smartphones which can determine location in multiple
ways, including by GPS,
triangulation with multiple cell towers, Wi-Fi connections, and
signatures of neighboring
signals. This yields very accurate location identification of
callers’ mobile phones, including
indoor location, and, in some circumstances, even emerging
ways to identify 3D location. The
latter has the potential to locate the emergency caller in a
certain floor and room in a
multistory building. Analytics are also crucial to the RapidSOS
model in being able, during the
time of an emergency, to data-mine large amounts of big data on
health, social media, and
business, and extract what is relevant for a particular emer gency
situation.
Although the US federal government seeks to enhance 911 calls
with more
contemporary enhancements, it is doing so gradually. The
NextGen 911 initiative, supported by
the National Telecommunications and Information
Administration (NTIA) and National Highway
Traffic Safety Administration (NHTSA), is on a slow track to

work with states to make more
contemporary information available to first responders (NTIA,
2021). However, this program
would not match the extent of data provision that RapidSOS is
seeking, nor does it include
providing portals to ECCs and PSAPs.
The spatial strategies of RapidGIS evolved from Phase 1, which
centered on innovation,
to Phase 2, in which the firm pivoted its spatial strategy to “go
to market,” while formalizing
collaboration with Esri, GeoComm, and several other spatially-
oriented businesses (RapidSOS,
2020; GeoComm, 2021). In Phase 1, RapidSOS developed its
emergency support platform,
which has geospatial data at its foundation. The heightened
accuracy enables precise locating
of a caller in 2 or 3 dimensions. That translates into first
responders getting to victims crucial
minutes earlier.
164

The firm also innovated in building strong, long-term
relationships with leading real-
time digital data providers such as Uber, Apple, and Google, in
order to utilize their extensive
real-time data to provide the first responder with enhanced
knowledge of the emergency
caller/victim, the emergency site’s built infrastructure, and a
socioeconomic profile of the
geographic area around the incident. RapidSOS additionally
sought out ties with hundreds of
PSAPs and ECCs, explaining what the firm could offer each of
them.
In Phase 2, to get fast acceptance by PSAPs, the firm offered its
portal product to PSAPs
for free. RapidSOS astutely realized that, since a PSAP would
be wary of the risk of holding its
own extensive digital information, a free offer of RapidSOS’s
geospatially-enabled portal could
overcome the concern.
In the rush to market with the free portal product, how could
RapidSOS generate
revenues and assure profit in the long term? The answer is that
RapidSOS mainly relies on

payments from its data vendors, including Uber, Apple and
others, which benefit in turn by
having their data in use in the 911 space. In addition, RapidSOS
garners revenue from
companies which pay to add their apps to its platform installed
in the emergency
communications and dispatch centers -- an example of an
ecosystem approach.
RapidSOS’s capabilities are evolving to offer analytic modeling
of the likelihood of
different types of emergency problems at the caller’s location.
For instance, an emergency call
arrives at an emergency call center at 8:05am from a smart
phone caller at an exact location
near a small Wyoming city, but the caller is incoherent and
unable to reveal what the
emergency problem is. Using the enriched locational data,
RapidSOS can generate utilize
predictive analytics to generate the most likely emergency
problem to be a heart attack, with
45% certainty. On the other hand, RapidSOS must overcome
concerns about data privacy and
meet the challenge of providing consistently accurate data to
over 20,000 emergency dispatch

locations nationwide. Data violations can occur when the
victim’s personal information
informs first responders, constituting a tradeoff between
emergency relief to the victim and his
privacy.
This case supports the steps for forging a GIS strategic plan by
a small, startup
enterprise, while also demonstrating the challenges of achieving
success. Of the GIS strategic
planning steps from earlier in the chapter, RapidSOS included
the vision and mission for GIS,
intended GIS solution, and most other steps. What was not
present in the startup’s GIS plan
was to determine the net benefits for the firm and to assess
risks. Phase 1 involved a
disruptive strategy (Gans et al., 2018), which is a typical option
for startups. Since there was
innovation and upsetting of value chains, the emphasis was on
rapid market growth without
sufficient time or capability to assess risks and determine net
benefits. Phase 2 focused on
collaboration with major players (Gans et al., 2018). It allowed
more clarity on net benefits.
Risk was reduced by the cooperative tripartite agreement with

GeoComm and Esri. Throughout
Phases 1 and 2, location intelligence remained at the heart of
corporate strategy and was
integrated from the start with corporate strategic planning. As
the company matures, we
foresee that the firm will also need strategically to raise the
quality of its data and to serve
many thousands of emergency centers with consistently high
reliability. Also, as emphasized in
165
chapter 7, we suggest the firm needs to include corporate social
responsibility in its strategic
plan, including assuring a standard of data privacy as well as
equity and inclusion in providing
services across cities and municipalities nationally.
For location intelligence business startups, two essential
strategic decisions are
encountered: (1) whether to compete or collaborate and (2)
whether to be defensive,
protecting products and technological advances, or to focus on

rapid growth, development, and
experimentation/risk-taking in the marketplace (Gans, Scott,
and Stern, 2018). In both
decisions, RapidSOS chose the latter option.
Geospatial Competitiveness Value-Added
The Geospatial strategy needs to correspond to the company’s
mission and vision. The
necessity for this is well known in studies of information
systems (Peppard and Ward, 2016;
Piccolo and Pigni, 2019;) and is noted for GIS (Pick, 2008,
Carnow, 2019; Lewin, 2021). A
business that has achieved consonance between its geospatial
strategy and the strategic
direction and mission of the firm has alignment, which has been
shown to improve
performance in the long term. Since the GIS leader is
increasingly being invited in as part of the
firm’s strategic planning process, strategic spatial alignment is
becoming the norm. The more
active the GIS manager can be in the planning effort the better.
The manager may need to
assert to senior management the importance of GIS by pointing
to its role in addressing and

mitigating pain points for the business, while indicating that the
aligned GIS strategy can
contribute to mission and result in tangible benefits for the
company (Dangermond, 2019).
Sustaining the alignment of geospatial strategy and business
strategy over time requires
continuing effort, since technologies are changing rapidly and
the outside environment is
altering. Maintaining this alignment are often referred to as co-
evolution (Peppard and Ward,
2016). It is important over time to continually adjust the
geospatial strategy with the business
strategy, in response to changes in one or both. An example of
co-evolution in chapter 2’s
Walgreens case study is the shift from an earlier strategy that
emphasized US regional
management decision-making utilizing GIS centered on regional
management decision making,
to an international strategy that encompasses the expansion of
Walgreens through the Boots
acquisition into a global firm, with an enlarged GIS strategy
that seeks to provide spatial value
to the Boots division.

Business value can be increased by prioritization of the
initiatives in a geospatial
strategic plan. In assessing priorities, the following
considerations apply – (a) to determine what
is the most important of the strategic initiatives based on
expected value of benefits, (b) what is
the enterprise’s capacity to undertake an initiative, based on the
resources present, and (c) can
an initiative succeed based on benefits and risks (Peppard and
Ward, 2016). In assessing
benefits and the management risks for GIS, it is useful to
categorize the certainty of benefits
compared to the risk of the challenges (Carnow, 2019), as
pictured Figure 9.3.
166
Figure 9.3 A two-by-two table shows high and low geospatial
risks and challenges crossed-categorized with low and high
benefits, leading to four risk approaches, namely avoid,
cautiously embrace, experiment and aggressively embrace
(Source: Carnow, 2019)

Using this matrix, the highest priority should go to initiatives
with high benefit and low,
management risk. These might be considered the “low hanging
fruit.” A very risky initiative
with high benefit should be regarded cautiously, while one with
low benefit and low risk should
be considered an experiment for testing. A high risk, low
benefit initiative is given low priority.
Prioritization is evident In the UPS case in this chapter, in
which the ORION system initiative for
years was judged as experimental, before being raised to the
level at which senior management
aggressively backed it.
Competitiveness
Location Analytics can serve to expand competitiveness in a
firm, leading to lower cost
of goods and services, to differentiating products and services,
and to entry into specialized
market niches (Porter 2008). For UPS, the powerful proprietary
ORION software optimized daily
routing of trucks, leading to significantly lower average cost of
deliveries at scale. In another

example, a small firm, GIS Consulting Inc. (anonymous name),
was able to establish a strong
niche in high-end location analytics software for sophisticated
federal government agencies
that deploy the applications in real-time, spatially dynamic
environments. For instance, it
designed a standardized spatial surveillance system for
government vehicles and personnel that
provided support for a US Presidential inauguration.
According to the “Porter 5-Forces Model” (Porter 2008), direct
competition is a primary
force a firm has to confront (see Figure 9.4). There are four
additional forces that can disrupt
and impact the competitive arena: new market entrants,
substitute products, firm-buyer
167
relationships, and firm-supplier relationships (Porter 2008). GIS
and location analytics can
influence all four of these forces, offering strategic advantage.

benefits from a company and
its direct competitors—for instance, autonomous self-navigating
vacuum cleaners,
which use spatial algorithms with input from locational sensors
to provide hands-free
cleaning, have disrupted the competitive vacuum market.
Figure 9.4 Michael Porter’s model, relevant to formulating
geospatial strategy, has the five competitive forces of direct
competition as its central force, impacted by a competing firm’s
forces of its supplier relationships and buyer relationships,

and by new market entrants and substitute products
(Source: Porter, 2008)
-based products or services can usurp the
benefits of an existing
product or service by providing competitive market benefits. In
the sharing economy,
Uber or Lyft service substitutes for traditional taxi service by
outfitting their cars with
interactive mapping that displays customer and vehicle
locations, as well as pick-up and
transport routes in real time.
he firm-
customer relationship in
various industries and markets. For instance, at a large
midwestern insurance company,
the speed of response to impacted customers after a destructive
event is improved
through a GIS model that dispatches a team immediately to the
large disaster site by
spatially modeling it, according to the amount and type of loss
by location. This rapidity
of response, in turn, boosts customer satisfaction and loyalty.

168
-supplier relationship,
depends on the positioning of
the supplier in the market, i.e. whether the supplier or the firm
holds costing clout over
the market. Government regulation can also be a somewhat
unpredictable variable that
affects supply. An example is the supply of coffee plants from
hundreds of farmers in
the Nespresso case study in chapter 7. The geography of the
suppliers is crucial to
costing, e.g. a large supplier in an optimal coffee growing area
may have costing clout,
enabling it to push Nespresso for higher pricing. The geography
of Nespresso’s growers
can be studied by location intelligence and decisions made to
achieve more costing
evenness between suppliers, reducing pricing power.
Collaboration
Geospatial strategies should include an exploration of potential

benefits of
collaboration. In contrast to competitiveness, collaboration
involves mutual benefit to partner
firms, so each partner gains in market position, value, strength
of products and services, profits,
and brand recognition. Geospatial collaboration ranges from
informal sharing of ideas by
individuals, to large-scale pooling of technological knowledge
by firms, to formal collaboration
on product development, marketing research, joint ventures, and
co-branding.
Collaboration has been spurred to higher levels by digitization
(Kiron, 2017), which can
facilitate informal networks. An early study of GIS and
networks of collaboration (Harvey,
2001), based on 212 interviews of GIS employees, found that
collaboration of GIS employees at
firms in Switzerland tended to involve multiple interpersonal
networks that are frequently
formed and dissolved. Groups remain stable if the perspectives
of group members remain
consistent over time. The groups included private-firm
employees with a focus on

collaboration with data providers at the level of cantons,
governmental entities comparable to
US states (Harvey, 2001). The cantons benefited the firms by
providing a variety of spatial data
including demographic and cadastral information.
A more recent example is RapidSOS, discussed in chapter 2, a
growing smaller company
that collaborates with big-tech companies for data provision,
with local public emergency
services to enhance real-time information flow to emergency
workers, and with several large
GIS firms on development of services.
Ecosystems constitute another form of collaboration that has
been hastened by rapid
digital transformation sweeping through industry sectors. An
ecosystem is an orchestrated
network of collaboration that extends across many industry
sectors (Jacobides, 2019).
Companies in the ecosystem have many common standards and
share common services,
software, and platforms. An example is the iPhone, which
opened up to an ecosystem with
other companies to share apps in its iStore. In that case the

platform is shared collaboratively,
even though the app firms may be in competition in some other
business areas. In the case of
RapidSOS, that firm’s portal is strategically set up, so it can
serve as an ecosystem to other firms
in the emergency response sector. The post covid-19 business
world is likely to verge even
more strongly towards digitization, which will stimulate
strategies that encompass geospatial
169
ecosystems. A geospatial corporate hub might attract sufficient
external interactive
participants to be considered as an ecosystem.
Sustainable Advantage
Companies are increasingly being held accountable for the
impacts beyond the
“bottom-line.” The triple bottom line adds accountability for
social and environmental net
benefit. Some companies’ business and GIS strategies go

beyond the goal of location monetary
value-added to formalize the social and environmental goals.
In the case of Nespresso, Chapter 7 described how deeply i t is
engrained into their
strategy and practices. Figure 9.5 illustrates the extent to which
Nespresso embodies that
broad strategy for sustainable advantage. The priorities under
the “Positive Cup Framework,”
Nespresso focuses long-term sustainable coffee supplies,
analytics to support farmers,
transparent communication to customers, and responsible
practices in communities. These
priorities are executed via two dashboards: F.A.R.M.S to
analyze and management farm
activities and the AAA Sustainability dashboard to manage and
track sustainable practices and
key performance indicators. Finally, in terms of
implementation, the focus in on impacts in
terms of increased efficiencies in coffee production, progress
toward achieving 100% of
supplies from AAA compliant farms, and progress in achieving
11 (of the 17) United Nations
2030 goals. As part of the sustainability strategy, GIS and
location analytic strategy is to apply

mapping to farm coffee more sustainably.
Figure 9.5 The Nespresso case study from Chapter 7 is
illustrated with respect to its particular strategic components of
setting priorities, determining strategy, and implementing the
strategy
(Source: Author)
170
Concluding Case Study: Kentucky Fried Chicken
This case introduces locational strategies in the context of KFC,
a global fast-food
company that has a distributed, franchised organizational
structure. This structure contrasts
with UPS’s more centralized management structure. The
challenge for strategy shifts from
centrally optimizing a routing and logistics system to

supporting a variety of spatial needs
across numerous franchises with varied degrees of autonomy.
Kentucky Fried Chicken (KFC) is a global brand that represents
over half the unit
business of Yum! Brands Inc., a firm that also includes Taco
Bell as about one third of Yum!
units, while Pizza Hut accounts for a sixth. Yum! is basically
the franchisor but now operates
only about 2 percent of its franchises. KFC has hundreds of
franchise owners worldwide, except
for in China, where it was spun off as a separate company.
Franchise owners own between 1 to
over 800 franchises. They are often independent in their
thinking and strategies, although
constrained by the terms of the franchise agreements. The thrust
of the past decade has been
for KFC to eliminate its ownership of franchises, so Yum!’s
revenue shrank to $5.9 billion in
2018, but the company’s profitability has increased.
Until 2016, KFC did not have in-house GIS, although Yum!
Brands did have GIS globally.
However, KFC was siloed from Taco Bell and Pizza Hut, so
when the corporate manager of GIS

was hired in 2016, he was told, “We’re not going to run you
from the top; you’re going to run
yourself” (Joseph 2019). The GIS manager has a staff of two, a
GIS analyst and business analyst.
The GIS group interfaces with a much larger IT group, which
has vast responsibility in
supporting the back of the house for over 4,000 franchisee
restaurants as well as supporting
corporate functions.
The focus of the GIS group is to provide competitive spatial
tools to the thousands of
franchises worldwide. That includes customized applications,
data, and training. Although the
GIS group has access to franchisee data, it is careful not to
share the data among franchisees,
since they sometimes compete with each other.
The GIS group has key technology partners in Esri, Maptitude,
and Intalytics, now a part
of Kallibrate, which produces its branded SiteIntel platfor m.
The SiteIntel platform provides
location-based data visualization, forecasting, and reporting for
real estate and marketing,
based on a wide variety of current data sources. The GIS group

adopted SiteIntel and has
customized its platform in different versions for internal uses
versus external uses that are
franchisee-facing. This software indicates, at a large scale, i.e.,
in great detail of mapping, where
all the KFCs are located, where all competitors are, the location
of important generators of
customers, transportation routes, traffic volumes, and
demographics. The spatial tool has
functionality for the user to add and superimpose layers, run
reports, and perform some spatial
analysis. Additionally, the GIS analyst on the team receives
numerous general requests for
spatial analysis, location analytics, and professional mapping.
The GIS manager classifies KFC’s spatial maturity as about a
third of the way along, so
there is plenty of opportunity to elevate the GIS to a higher
maturity level. Several of the key
GIS accomplishments to date are building the GIS capabilities
into important parts of the
171

franchisee’s marketing plan and then loading the visualization
of promising locations for new
KPC restaurants. These can be viewed internally but also,
through SiteIntel, by franchisees
seeking growth areas. The potential locations on a map for high
growth are termed “market
calls” and the software enables them to be prioritized. Recently
KFC has acquired massive
mobile-device data, over which to overlay customer sentiment
from social media.
The Intalytics models also provide analytical tools based on the
competitive details in
the immediate neighborhood of a proposed or existing KFC
location, including locations of
competitors in nearby blocks, signage, traffic flows, vegetation
blocking view lines, and other
detailed neighborhood features. For larger geographies,
important work is being done in trade
area analysis, using demographics and geo-segmentation.
Sometimes Intalytics sends a team of
surveyors to a restaurant to ask questions, understand its
location, and perceive where the
restaurant is heading. This can be helpful in helping a

franchisee avoid choosing an
underperforming location or treading on another franchise’s
territory and customers. This is an
area in which social-media spatial big data could be helpful in
the future.
So far, KFC’s spatial strategy has been tactical and very
practical, which would
correspond to a moderate spatial maturity level. The KFC
strategic planning team can observe
other Yum! brands to find out what works spatially for their
restaurants in the US or overseas.
The strategy is to focus on development of GIS applications
rather than outcomes, because
KFC-corporate has reduced power over the spatial decision-
making, especially in overseas
franchises. Part of the spatial strategy is instilling franchisees
with knowledge of their markets,
of locational opportunities, and where they can build or be
looking to build. Ultimately the
spatial strategy is focused on the franchisees’ priorities.
In sum, GIS within KFC has been restricted to being a support
entity within a franchisor,
concentrating its spatial group to support the hundreds of

separately owned franchises and
4,000 plus franchisee restaurants. The small GIS team has
aligned its spatial strategy with the
corporate business strategy and with IT, and it has reached out
to collaborate with the other
internal units as well as with franchisees worldwide. With GIS
planning maturity at a moderate
stage, there is still room to provide enterprise-wide, competitive
location analytics to the
corporation in the future.
172
Chapter 10
Themes and Implications for Practice
Introduction
The book has provided a wide-ranging examination of Spatial
Business with the aim of

providing a contemporary foundation for understanding the
business and locational knowledge
base for integrating location analytics into the functions and
goals of business. As noted at the
outset, our approach was first to consider the Fundamentals of
Special Business (Part I), then
focus on Achieving Business and Societal value (Part II), and
finally consider managing and
leading Toward Spatial Excellence (Part III). This concluding
chapter summarizes key themes at
each phase and outlines a set of 10 Implications for Practice
that are derived from these
themes (See Figure 10.1).
Figure 10.1 Spatial Business: Themes and Implications,
showing ten themes and implications for practice associated
with
three sections of the book (lessons in light blue boxes and the
sections of the book in dark blue boxes)
Source: Author
173

Fundamentals of Spatial Business: Themes and Implications
1) Identify and Enhance the Location Value Chain
The book has emphasized the location value chain as an
important conceptual lens for
examining location analytics across a business enterprise.
Growing the value chain can elevate
an organization from a spatial novice, when GIS initiatives are
localized in a particular function,
to spatially mature, when GIS deployment, driven by business
needs and strategy, spans
enterprise-wide across multiple functions.
Location value underpins the location value chain. Not every
function in a business is
rooted in location. While not exhaustive, common functions
found in a location value chain
include:

erations
Organizations can use location analytics to heighten the value
contribution of individual
business units or combinations of business units. This in turn
enhances the customer
experience, creates opportunities for business expansion and
competitive separation and
generates benefits for employees and the wider community.

174
Implications for Practice:
and cultivate internal
champions. The book outlined a spatial decision cycle that
provides a rubric to enable
spatial thinking by considering key business goals, locational
elements of that goal,
location analytics that can be used to contribute to that goal,
and the data needed to
achieve desired produce valid and meaningful results. With a
firmly identified location
value-added that can be achieved, business managers can work
with both technical
experts and senior management to execute analytical processes
that can contribute to
gains in key parts of the business value chain.
location intelligence.

Location analytics can help achieve important strategic goals
such as customer growth,
effective operational management, and risk mitigation. This
may occur within the
context of a given function (such as marketing) , but ideally
would evolve through
connections to related value chain domains (such as R&D and
supply chain), providing
an avenue to achieve strategies and metrics not viable by other
means.
developing joint marketing
strategies, or growing supplier diversity, there is no
replacement for joint visibility and
measured action planning. For example, in the area of revenue
generation, location
analytics can used to identify and market to high performing
locations. In the area of
regulatory compliance, location analytics can demonstrate
compliance. In operations
and risk management, examples such CSX and Walgreens
demonstrated the value of
tangibly demonstrating the capabilities of location analytics to
important business goals,

especially filling both an immediate need and a strategic
opportunity.
Consider the benefits of shared value where data and insights
from one function may be
valuable to another. The Shopping Center Group (TSCG), a
leading national retail-only service
provider has four main service lines: tenant representations,
project leasing, retail property
sales, and property management of those retail properties. They
consider location analytics as
core to each one. While not so streamlined for every company,
TSCG executives consider the
use of location analytics to be a key contributor to their 30
percent growth and its emergence
as a commercial retail and information company.
2) Enable Spatial Maturity Pathway
The spatial maturity ladder is wide-ranging among
contemporary companies,
progressing from novice to enterprise-wide, competitive status.
The book outlines the concept
of spatial business maturity, drawing upon the analytics
maturity model proposed by Davenport

and Harris (2017). Factors that distinguish mature location
analytics competitors from the
others are:
-based business and customer insights
senior leadership
175
initiatives
-in-class GIS and location intelligence
technology
The spatial maturity model can offer a pathway for
organizations that may currently
have location analytics siloed within a specific function or
department but aspire to deploy

location intelligence as a competitive force and as a driver of
spatial transformation.
acceptance. As shared in the
John Deere case study, opportunities will come from all levels
of the company as
benefits are experienced. Such value will get expanded upon to
benefit other areas of
the business, and soon, like with John Deere, location
intelligence will be poised to
catalyze innovation in every part of the company's value chain
impacting farmers,
dealers, and consumers while transforming the company to a
techno-centric
agribusiness. A company will be confident that profitability is
maximized, current
resources are optimized, risk is mitigated or avoided, and
market share is captured. The
tools used in the “business of modern-day farming” echo that of
other business
development and operations leaders alike, helping them improve
yield, increase

productivity, lower costs, and achieve more precision ahead of
the trend.
analytical uses. Maps and interactive
business analytics
dashboards and applications provide immense value to
understanding market potential,
current status, plans, and the relationship between all of them.
Location analytics adds
significant value by quantifying and enriching data -
calculating, measuring, and
assigning - using real-world constraints and context with real-
time feedback. Companies
such as Travelers Insurance now employ a full range of location
analytics to assist a
range of business-critical functions (Travelers Insurance, 2021).
These include predicting
the location of natural disasters (for underwriting purposes),
analyzing damage locations
(for claim purposes), and identifying high-priority locational
impacts (for disaster
response). These tools have been used with remarkable success
for recent hurricanes on
the east coast and wildfires on the west coast (Claims Journal,
2019).

3) Match Location Analytics Approach to Business Goals and
Needs
By using analytics to make better decisions, organizations
generate value that manifests
in the form of business benefits, both tangible and intangible.
These include cost savings,
revenue growth via an increased share of wallet, uncovering
business opportunities and
untapped markets, increase in productivity, process
improvements resulting in improved asset
efficiency, enhanced brand recognition, increase in customer
satisfaction, and benefits to the
environment and society. A critical element in achieving this
success to match the right
analytical approach to the business goal or need being
addressed.
176
While there is no right place to begin, the methods of location
analytics often parallel

the spatial maturity of an organization. The descriptive
approach and visualization may gain
initial attention and develop links on the location value chain,
while the prescriptive and
predictive approaches will garner the greatest executive
visibility as they result in more
directive findings to support the future growth, competiti ve
advantage, and collaborative
potential for the company.
Hot spot mapping, route optimization, demand coverage,
cannibalization examination,
and relocation strategies are examples of spatial modeling
approaches. They support a
company’s ability to best understand its position and orient its
product or service to its
customers while optimizing resources. In cases like these,
location analytics provided
awareness, spurred enhancement of an existing process or the
creation of a new process, and
then evolved into supporting strategic plan development. In
digitally mature organizations, like
John Deere, digital technologies are not just an add-on to
existing processes and practices;
rather, they prompt such organizations to rethink how they do

business. Of these technologies,
business analytics—the paradigm of fact- and data-driven
decision-making—has been rated by
business leaders as the main driver of digital transformation in
their organizations, surpassing
AI, IoT, mobile, social media, robotic process automation,
manufacturing robots, and VR (Kane
et al. 2017).
Maps and visualizations
have always provided keen descriptive insights that help
decision-makers understand
what happened (in the past) or what is happening (in the
present) in their stores, sales
territories, and service areas, out in the field, or at a supplier
location thousands of
miles away. Today, powerful dashboards elevate the descriptive
power of location
analytics and broaden its reach. Dashboards composed of smart
maps can be used in
any part of the location value chain to provide situational
intelligence in real-time and

facilitate reporting, which is crucial for decision-makers.
Depending on business needs,
they may be internal- or external-facing.
-term business decisions. The predictive power
of location analytics is
becoming increasingly important as businesses navigate an
uncertain world. The cost of
business disruption can be tremendous, as was evident during
the COVID-19 pandemic.
Prescriptive analytics is tied directly to support of decision-
making--for strategic
planning or within an established system’s process. It reconciles
a broad array of factors
and criteria that may sometimes be conflicting. This provides a
foundation for decisions
and actions that enable process-, department-, and/or enterprise-
wide optimization,
ensuring business success, improving resiliency, and enhancing
competitiveness in both
the short- and long-term.
seek the insight of location to
help achieve their mission and adjust to the unanticipated

activities of the world. They
seek the simplicity in sorting through the business, social,
economic, infrastructural, and
177
climate insights which impact them today and aim to adjust in
order to achieve future
goals. Through the results of location analysis and data
visualization, companies can gain
insight and solidify understanding. They achieve location
intelligence which helps them
define their circumstances and set measurable strategic and
tactical courses for what to
do next.
4) Build a Data-Driven Spatial Business Architecture
The “Spatial Business Architecture” outlined in the book
provides a framework for
building a technical foundation for conducting effective
location analytics. The architecture
begins with the business goals and needs, then business users

who have responsibilities for
addressing these business goals and using location analytics to
do so. The architecture
continues with a series of location analytics tools, tools that
depend on various forms of
location data. Underlying all of these functions are the various
platforms that host spatial
business processes, such as the cloud, the enterprise, or mobile
services. The final component
is the net consequence in term of location intelligence that can
be used in provide insights,
inform decisions, and have an impact on business performance
relative to identified business
goals and needs.
The style and extensiveness of the Spatial Business Architecture
developed by business
can range from a relatively “light’ style, with online or desktop
use of internal customer data, to
a broader enterprise platform with diverse applications, data-
sets and user stakeholders. As the
enacted architecture grows, a mature digital business requires a
mature data strategy to set up
the proper architecture, flow, and governance of data. Without
one, masses of jumbled data

streams—a “data hairball”—can complicate product
development and fail the primary insights
they are intended to support. The development of mature data
strategies is designed to quickly
cut through the data clutter and align data streams with business
initiatives and priorities and
dictate the proper use of the data.
Today, location data is ubiquitous. With the increasing use of
smartphones, geotagged
sensors, and other IoT devices, and the rapid diffusion of
unmanned aerial systems (UASs), the
availability of location data is at an all-time high. Mobile
geolocation data has spawned an
entire industry of providers of this data that companies are just
beginning to discover. Also
prevalent is geodemographic segmentation data which helps
companies draw generalized
conclusions about customers and markets.
As companies become voracious consumers of mobile location
data, the ethical use of
data remains an urgent consideration (Thompson and Warzel
2019). However, with ethical
standards of data aggregation in place, with a heightened

awareness of implicit bias, and
proper internal governance policies companies can continue to
derive valuable intelligence
using location analytics.
178
age through location-savvy
information products. For location
forward companies like Travelers who are spatially mature,
managing, leveraging, and
planning for new location data gives them a competitive
advantage. Their science
originated in underwriting where the use of third-party data was
prevalent and location
intelligence was at the core of their business. Their third-party
data is part of the data
strategy. They have built their systems and algorithms with
location value at the
forefront. Because of this they can quickly take advantage of
new data in the market

and continue to provide premium services to their customers.
-crafted location analytics to strengthen
partnerships. As location analytics
becomes embedded in an organization, the resulting business
benefits can grow to
impact numerous goals and stakeholders. Building trust through
insightful and high-
quality data products yields new customers, new revenue
streams, and closer
partnerships internally and externally. Trust builds relationships
with customers through
achieving a competitive advantage like Travelers, Natura, and
Nespresso. Similarly, trust
builds relationships with suppliers and partners in having a
tangible and measurable way
to work better together.
data as part of the location analytics strategy.
Many companies are
considering how their data can be utilized to create a new
revenue stream or to make
services available for outside use. Central data organizations
with C-suite participation
and a Chief Data Officer to support the switch from data for

business unit reporting to a
more strategic level of use. For third-party data, the imagery
sector is enriching spatial
business by providing base maps, point clouds, space-time
imagery, AI-enhanced map
imagery, and raster analytics and modeling (Dangermond,
2021). Satellites, planes, and
drones tend to have digital image collectors, including radar
collectors, lidar, multi-
spectral collectors, digital cameras, and GPS. Various modes of
collection should be
considered for appropriateness for certain business applications,
as each mode has
pluses and minuses (Sarlitto, 2020). Social media with location
features has become
commonplace for individual “social” users, and increasingly
analytics teams in
businesses are employing the power of location-based social
media to better serve their
customers.
likely to play a central role in
producing location intelligence at scale to predict demand
spikes, identify high-margin

prospects, anticipate disruptions in the supply chain, and speed
up logistics delivery
services (Raad 2017). Already, in the insurance industry, deep-
learning-based models
are expediting decision-making in damage assessment, claims
processing, and fraud
detection. This can be used to forecast sales, manage inventory
levels, prescribe
location-specific target marketing campaigns along with
individualized engagement and
retention strategies that minimize the risk of attrition
(Davenport, 2018).
Achieving Business and Society Value: Themes and
Implications
179
5) Use Market and Consumer Intelligence to Drive Business
Growth
Location intelligence is used competitively in global, local, and
hyperlocal markets. Rich

geographic information increases competitiveness, fosters new
products and services, and
provides new ways to strengthen ties with suppliers and buyers.
Business expansion,
marketing, sales, and service have firm location value as a core
element to decision making in
short- and long-term growth opportunity assessment.
Marketing is strengthened by location intelligence because of
the ability to target,
measure, and communicate. The location of customers, company
stores, storage facilities,
competitor facilities, transporta tion routing, geodemographic
and social media metrics and
intensities, all contribute to the total picture. Depending on the
nature of the question asked,
location intelligence can be made available at different unit
scales, such as county, zip code,
city, census tract, and individual. For instance, marketing data
from major social media vendors
are utilized by RapidSOS to improve the accuracy and
timeliness of the emergency response of
its local and regional governmental clients.
New operations growth, new service revenue growth, and

business expansion are all
uniquely benefited by location thinking as models can predict
market share, overall sales,
revenue, and profit potential of trade areas which will drive
service and optimization. It is the
ability to measure and target spatially that allows companies to
see their results in satisfied
customers, higher market penetration, a higher net promoter
score, trusted service relationship
with suppliers, and improved company engagement at each
venue and online.
Development and Sales,
companies are increasingly combining proprietary customer data
(for example, website,
mobile, CRM) with data from social media feeds and household
virtual assistants to
generate insights about a customer's mindset and a holistic 360˚
persona. This can be
used to forecast sales, manage inventory levels, prescribe
location-specific target
marketing campaigns along with individualized engagement and

retention strategies
that minimize the risk of attrition (Davenport, 2018).
Increasingly, CRM systems are
beginning to add in spatial data access, and visualization as
omnichannel engagement
becomes widespread. This gives fuel to GeoAI models which
are being used to provide
rich, dynamic insights into customer behavior.
Applying this insight into
the physical world, this data in combination with new data from
third parties, like
anonymized human movement data are coupled with more
traditional socio-
demographics and consumer spending data to become the
foundation for decision-
support prescriptive modeling and ongoing monitoring. As a
classic example, in the
Kentucky Fried Chicken case study, location intelligence
furnished the firm and its
franchisees with rich and varied social media and demographic
information at the micro
level for decision-making on competitive locations of retail
units. Such information is

utilized to support real estate investments and lease agreements
in most retailers and
180
QSRs, as well as in an ongoing way to set up and evaluate KPIs
associated with each unit
in its trade area classification or cluster. Stronger correlations
can be drawn to improve
the connection between digital and physical interaction to
achieve higher customer
conversion metrics, improve the experience for the customer,
and find savings in
optimized product assortments.
n priority markets. In
marketing, companies
consistently measure their opportunity and learn about their
priority markets to engage
the best go-to-market course of action. A location-based
approach draws the
connection to the P’s of marketing through sales, investment,
and planning. This was

illustrated in the Fresh Direct case study. The output of
activities from one function
flows to the next from marketing through sourcing, distribution,
and delivery. It allows
for the coming together of disparate data and analysis to inform
the complete process,
avoiding disjointed work, and providing a manner to bring
functions together for better
planning and nimble response. In this case, there was a unique
beneficial by-product for
FreshDirect. They were able to optimally invest in trucks, some
with refrigeration and
some without because they knew they would be able to maintain
safety standards with
their planned delivery windows.
As businesses are
increasingly focused on the customer experience, the service
areas play an equally
critical role and benefit both the happiness of the customer as
well as cost and
operations efficiencies internally. Like with the FreshDirect
delivery trucks and CIDIU’s
environmental services, it is the ability to measure, monitor,

and target that drives the
business initiatives. By integrating IoT, GIS, and optimization
modeling and using
location-aware sensors, CIDIU S.p.A. was able to completely
eliminate the third shift for
the entire service area. In addition, the number of vehicles used
during a test period
decreased by 33% reducing waste-collection operational costs
and augmenting the
company’s competitiveness (Fadda et al, 2018). A third benefit
is showcased in the Cisco
case study where premium service areas were developed and
sold without incurring
additional operating expense resulting in near total profit.
-term market growth.
For long-term growth, the
ability to evaluate underpenetrated and underserved areas helps
a business weigh its
future investments through partnerships or acquisitions.
Location analytics helps set up
the desired outcome through thorough evaluation of multiple
variables like risk, country
development stage, competitive reach, transportation, utility
resourcing, compliance,

and climate variables. The results of which may lead to initial
sales testing or operations
research to get the feel for greenfield growth, potential
acquisitions, and sales potential
even 10-20 years ahead of planned investment. With location
intelligence, businesses
can conduct granular or hyper-segmented analyses of business
development
opportunities or reorient supply chains for significant savings
like in the Proctor &
Gamble example.
6) Measure, Manage, and Monitor the Operation
181
This is a highly varied, highly process-oriented part of the
organization and historically
the center of value. It spans from situational awareness to
facilities, to supply chain and
logistics. Quality, consistency, cost, and continuous
improvement needs are high in these
functions, and depending on the industry, so are capital

investment and sustainability efforts.
Increasing profitability with optimal resourcing has location
value chain linkages in service
delivery, business growth planning, vendor selection, asset
management, distribution, and
sustainable development.
In manufacturing, for example, production cannot happen
without safe buildings, well-
functioning machines, reliable services to make them run, and
transportation to get them to
market. Hundreds of millions of dollars in capital investment
are spent on new plants and the
tools to make products. Factory automation is continuously
advancing and there is an ongoing
interest to make products to order. These two needs alone
illustrate the need for location-
based asset management and distribution network planning as a
necessity. Additionally,
building automation, energy optimization, and predictive
maintenance are also focus areas for
companies maintaining physical space, offices, or leasable
space. Spatial data about facilities
and assets serves as a solid foundation for each of these and
provides the framework to see

them all in an integrated matter. At a minimum, this helps an
organization boost data accuracy,
reduce errors, adopt automated workflows, and improve
efficiency.
With the improved facility and process monitoring a company
will see higher product
yields, decreased expenses, and satisfied customers through
more timely delivery of quality
products. Companies will have a unique ability to nimbly
respond to significant pressures like
COVID-19 and disruptions with far greater speed and ease as
well as open doors to new
revenue streams. The combination of facility, supply chain, and
asset data in various
permutations with a location-driven approach will very quickly
alleviate the pressures such as
was the case in the aftermath of the Fukushima incident in
Japan.
-time tracking to achieve operational benefits.
Transcending industry verticals,
the real-time tracking and monitoring of asset location and

condition can improve
productivity, prevent breakdowns, ensure safety, and reduce
costs. The book provides
examples of GIS coupled with other technologies and data such
as IoT-based sensors,
drones, augmented reality, mixed reality, radio-frequency
identification (RFID), and
machine learning to provide sophisticated real-time geotagged
data and locational
insights of considerable business value for operational as well
as tactical decision-
making in near-real-time. This was exemplified the Tampa
broadband service dashboard
that monitored critical dimensions of broadband service
delivery. Location intelligence
can also guide the dynamic navigation of field assets (people
and vehicles), reducing
travel time and ensuring that service time windows are honored.
In the event of
breakdowns or emergencies, location intelligence can reroute
drivers and vehicles,
ensure safety, and maintain the timeliness of operations.
182

outdoor and mobile assets
are more commonplace to monitor, indoor spatial relationships
are increasingly
developed, measured, and tracked. As another cross-industry
function, manufacturers,
retailers, real estate companies, and public facilities benefit in
unique ways. In
manufacturing, efficient layouts can increase the productivity of
production lines. In
retail, customer experience is paramount, so companies are
improving their layouts to
avoid congestion, aligning basket analysis with product
placement in-store, as well as
engaging their loyal customers with a premier experience. This
helps firms design the
layout of facilities to increase shopper traffic as well as average
transaction values and
profitability (Hwangbo, Kim, Lee, and Kim 2017).
Facilities themselves are also a
critical node in the supply chain, the network of facilities for a

company is a balance of
cost, risk, and regulation. Digitizing and mapping a supply
chain as part of organizational
digital transformation can have several benefits. For a global
business, it can provide a
reliable operating picture of how the supply network chain
performs and where it might
be failing. In addition, a digital, fully mapped supply chain can
deliver valuable
situational awareness powered by real-time alerts and
notifications, asset tracking, and
monitoring. It can also identify geographic stress points that
may be prone to risks. This
can help accelerate a company’s response time during the
normal course of business
and enable it to plan, prepare and respond in an emergency.
chnologies to provide holistic views of
operations. Real-time tracking
and monitoring of asset location and condition can improve
productivity, maintain
uptime, ensure safety and reduce costs. In addition to IoT, GIS
technology is also being
coupled with the concept of a digital twin—a virtual replica or
electronic counterpart of

physical assets, processes, or systems (Tao, Zhang, Liu, and
Nee 2019). GIS mapping
provides a holistic overview of system performance - people,
assets, sensors, devices,
and other services on the day-to-day but also in relation to
disruptions. It allows for the
change in condition or disruption to trigger notifications,
maintenance, or necessary
interventions to assure a product’s safety, prevent costly
damage or loss, and
disruptions to customers.
details how managing a
supply chain effectively starts with combining internal
organizational data with external
data from varied sources. As these disparate data streams reside
in individual silos, their
business user needs, whether upstream- or downstream-facing,
are equally siloed.
However, as an interface, a Geospatial platform acts as an
integrative platform between
the siloed data streams and use cases. The platform enables
users across the enterprise
with different business needs to collaborate, drawing upon

descriptive visualization of
georeferenced datasets, which provides the foundation for
predictions, decisions, and
informed actions across the network.
183
7) Mitigate the Risk and Drive Toward Resiliency
Imagine a company’s asset igniting a fire due to poorly
managed maintenance. Perhaps
the asset starts a wildfire that ignites thousands of homes or a
plant. The fines, product
shortages, or bad press could financially damage or even
bankrupt a company. Or perhaps an
uncharacteristic freezing weather event or a hurricane halts
power distribution and stops an
entire material supply chain and creates housing challenges for
company associates, servicers,
and customers alike. Imagine also a washout from severe
weather rendered a railroad track
unusable as occurred with CSX railroad in 2018. While such

events can’t be predicted far in
advance, scenario planning, action planning, and ongoing
monitoring provide a more proactive
environment for risk response. Every aspect of a company can
be affected by risk yet there is
not one function that holds ultimate responsibility.
In confronting external risks such as geopolitical crises and
natural disasters, the lack of
a visible plan can render CEOs and their organizations
vulnerable. Recent surveys have shown
that during such times, two out of three CEOs feel concerned
about their ability to gather
information quickly and communicate accurately with internal
and external stakeholders
(Pricewaterhouse 2020). This was proven accurate upon the
start of the COVID 19- pandemic
where every company wanted to know how their operations
were going to suffer and what
regions may be more greatly affected. Hurricanes and
uncharacteristically bad weather make
such needs come alive as well.
Risk means different things to different companies and to
different people. Travelers

Insurance, Bass Pro Shops, Mid-South Synergy’s Electrical
Cooperative, and CSX manage their
risk and resiliency in different ways with location at its core.
Travelers has a competitive
advantage by pricing more accurately and processing claims
quickly for improved customer
service. Bass Pro Shops’ dashboard of store and associate status
provided much-needed
visibility in the changing climate of COVID-19 spread. Both the
Mid-South and CSX are asset-
based companies and need to have awareness of the status of
their assets to provide service to
their customer.
resiliency preparations concern
physical disruption, brand image, competitive advantage, and
impact on communities
and the environment. Using location analytics, companies can
gain a new way to
measure and initiate action ahead of time, gaining the advantage
of being proactive
instead of reactive. With improved visibility there is potential

to adjust to events and
have decreased operational downtime, safe teams, measured
climate action, increased
customer service, as well as partner and stakeholder
engagement.
Beyond man-made or natural
disaster disruption, compliance and sustainability issues are
also prevalent. The need to
understand a supplier or plant’s location in context to labor
practices, water, and energy
resources, is paramount to sustainability. Companies are
typically aware of the primary
address for their Tier 1 suppliers but have little insight into the
network of facilities
184
supporting the Tier 1 and their suppliers farther upstream.
Gathering data to improve
location understanding allows for companies to set baseline
metrics and initiate plans to

improve and or maintain standards across their supply chain and
in line with their
commitment to sustainable practices and ethical standards.
8) Enhance Corporate Social Responsibility
In 2019, leaders of almost 200 major corporations in the United
States redefined the
purpose of a corporation to promote an economy that works for
all. These Business Roundtable
leaders recognized that the success of a business enterprise can
no longer be viewed solely
through the lens of corporate profits and shareholder value.
Instead, they affirmed that
businesses play an essential role in improving social conditions,
both locally and globally. In
many ways, this is a rediscovery of centuries-old business
traditions where successful Japanese
traders and merchants understood that to build an enduring,
successful trade, every
transaction must benefit buyers, sellers, and local communities
(Kantor and Peters 2020).
This expansive role of the business to address social, racial,
economic, health, and

educational inequities has been exacerbated worldwide by the
COVID-19 pandemic. As
corporate leaders navigate their businesses through increasingly
uncertain business and
geopolitical environments in the post-COVID world and are
pressured to achieve growth, they
are also at the forefront of shaping their organizations' role in
confronting and addressing these
inequities. At the same time, they confront an equally urgent
challenge in the climate and
sustainability crises. In each of these areas—whether it is
building a racially diverse and
equitable workforce, committing to supply chain transparency,
engaging in sustainable business
practices and environmental stewardship, or going above and
beyond regulatory requirements
to be true agents of change—location analytics and intelligence
can play a role. Managers and
leaders of the 21st-century businesses are organizing in areas
such as racial justice, CSR,
sustainability, and shared-value. All these issues have
locational components.

there
were few ways to measure
business impact on society. With the ability to digitally model a
business in space and
time and bring together the extensive global data sets available
it is now possible to
examine the current state as a baseline and begin to measure
changes to be able to set
goals and monitor progress for the community, environment,
and human rights to build
even greater global resilience. Nike, Nespresso, and VF
Corporation are leading the way
in getting deep into their supply chains and making the product
origins and impact on
local communities known.
reputational risk and cost
of engaging with suppliers who unethically source raw materials
or manufacturing
partners who do not hire locally, pay fair wages, or engage in
child labor can be
immense. More and more companies are providing interactive
mapping to showcase
their global supply chains to engage with a variety of

stakeholders. USA-based Nike and
UK-based Marks & Spencer were both early adopters in making
their brands visible and
185
offering insights about individual factories (Bateman and
Bonanni, 2019). VF Corp. took
this a step further and created traceability maps of its brand
name products that
communicate to consumers exactly where raw materials for a
particular product was
sourced, where it was manufactured, assembled, and shipped for
distribution, and how
components of the product flow between different facilities in
the supply chain.
sights that balance stakeholder needs. The Natura
case study showcases how
a popular line of beauty products launched in the early 2000s
with ingredients native to
the Amazon rainforest in Brazil maintained its commitments to
biodiversity and

environmental stewardship while generating sustainable
competitive advantages for the
company (Esri 2015). After solving the initial difficulties of
sourcing logistics in the rain
forest, building strong supplier relationships (Boehe,
Pongeluppe, and Lazzarini 2014),
and building out the supply chain network the company’s
Geospatial platform now
gathers location intelligence to make decisions about sourcing,
pricing, and distribution
addressing the needs of all stakeholders - farming cooperatives,
consumers, and
shareholders. By using a "quadruple bottom line" approach that
balances financial,
environmental, social, and human objectives, Natura has
continued to diversify its
product offerings using an expanded array of supplier
communities and bio-ingredients
while simultaneously protecting the Amazon and committing to
the ethical sourcing of
biodiverse ingredients (Natura, 2020).
addressing resilience as related to
climate. AT&T is addressing consumers’ safety and security by

actively researching at-
risk zones and adjusting their fiber and cell networks to
improve coverage. Through
their Climate Resiliency initiative, the company is building a
Climate Change Analysis
Tool. Using data analysis, predictive modeling, and
visualization, this tool enables AT&T
to react to climate changes by making the adaptations necessary
to help increase safety,
service, and connectivity for its employees, customers, and
communities (AT&T, 2021)
case of JP Morgan
Chase, one of its exemplary
CSR projects has been a long-term program to help small
businesses thrive in the city of
Detroit, Michigan which had been economically declining for
many decades with small
businesses bankrupt. JPMorgan decided to foster a campaign
named “Invested in
Detroit” across the downturned neighborhoods to seek to rebuild
the city and its
economy. As the company was a signatory of the Business
Roundtable statements on
CSR and has embedded CSR as part of its culture, the bank

discovered it could help
these businesses most effectively by using loans which are
keyed to low-income areas to
help disadvantaged small enterprises and nonprofits. The bank
succeeded with loans to
three initial micro-districts and will scale scaled up to a dozen
more while rolling out this
CSR approach to depressed areas of other American cities.
JPMorgan is also realizing
eventual financial benefit since in Detroit it holds $20 billion in
deposits. Hence, the
"Invested in Detroit" project can stimulate long-term business
growth and conversion of
many new and renewed businesses into bankable entities adding
to JPMorgan's market
share of deposits and loans (Heimer, 2017).
186
present, the world remains
engaged in the COVID-19 pandemic dashboards, like the one
published by Johns

Hopkins University, where all industry, government, and health
leaders are using
location intelligence to come together to take their role in
providing visibility and
metrics of spread and hospitalizations, developing accurate
spatiotemporal forecasts of
disease transmission, identifying specific on the ground needs,
and coordinating with
targeted relief all over the globe with supplies, equipment, and
personnel. Direct Relief
and the Facebook AI Research (FAIR) team collaborated to
forecast the spread of COVID-
19 in the US combining reliable first- and third-party data on a
wide variety of important
factors such as confirmed cases, the prevalence of COVID-like
symptoms from self-
reported surveys, human movement trends and changes across
different categories of
places, doctor visits, COVID testing, and local weather patterns.
(Le, Ibrahim, Sagun,
Lacroix, and Nickel 2020).
Toward Spatial Excellence: Themes and Implications
9) Develop a Spatial Strategy and Capacity

A coherent spatial strategy and the capacity to execute this
strategy are both essential
for location analytics to make a significant impact. The strategy
brings together the business
goals and needs with the ability of location analytics to
contribute to those goals and needs.
Typical elements of a spatial strategy include the following:
as a component of the
company.
of the business issues, opportunities, and needs
that location analytics is
intended to address.
company business lines and
operations.
d from
the spatial strategy,
including costs and risks associated with pursuing these
benefits.

components and determination
of architecture elements needed to achieve the strategic
objectives.
An operational plan to implement the spatial strategy and key
performance indicators
to be measured and tracked.
A leadership team for spatial strategy should include both those
with technical and
business expertise to ensure the vision and mission capture both
the business strategy and role
of location analytics in achieving the business strategy. They
should be included as participants
187
in the vibrant back-and-forth of discussion, argument, and
eventually consensus on mission and
vision.

data and location
analytics increases the agility of the business, challenges the
status quo, connects dots
across the enterprise, drives profitable growth, and increases
competitiveness. Spatial
transformation changes the business profoundly, to the extent
that senior management
becomes the driver (Frankiewicz and Chamorro-Premuzic 2020).
There is an opportunity
for varied leadership of this discipline. The spatial strategy
should be closely aligned
with the digital transformation office, the project management
office, and the offices of
the CSO, CIO, CMO, and COO. These are the offices where the
funding streams will
ultimately stem and where the cross-functional needs ultimately
come together. These
offices will fund the programs and product teams aligned to
drive planning and
operational work.

the level of digital,
analytical, and spatial maturity of a company will aid in
establishing a frame of reference
for ongoing development and benchmarking to lead with vision
like that of BP,
Walgreens, or Nespresso. With only 20% of organizations
deeming their Digital
Transformation successful (Harvard Business Analytic Services
2020), the considerations
of readiness, resourcing, and talent are paramount.
-thinking
approach. Devising a spatial
strategy provides an opportunity to view the company across the
location value chain,
that is, as a system. This can provide the framework to take into
consideration new
business opportunities and cross-company collaborations. It
helps break down the
organizational walls and widens the range of impact. It also
provides greater awareness
of potential risks across functions.
10) Provide Spatial Leadership for Sustainable Advantage
The transition from a strategic plan to a successful and

impactful strategy is where the
“rubber meets the road”. Senior leadership can provide the
vision and critical support for
integrating location analytics into business planning and
operations, including as part of the
firm's overall business and IT strategies. As noted above, this
is best conducted in conjunction
with the location analytics unit or team. In the case of UPS,
although there was an extended
time period for the testing and retesting of truck routing and
delivery algorithms when
company senior leadership saw the clear benefit of location
analytics, this leant support,
funding, and encouragement of middle management to extend
location analytics across the
entire company and soon geospatial was added to the corporate
vision. In the case of
Nespresso, there became a critical mass of location value across
their sustainability value chain.
The Nespresso geospatial platforms supported the stakeholders
in various capacities resulting
in award winning achievements. They used the tools in daily
operations, spatial analysis, risk

188
analysis communication and in the development of new
partnerships to grow the AAA
sustainable development program for coffee.
would be a collaborative
leadership with representation from relevant business and
technical teams. This would
enable working relationships, improved connections to strategy,
and potentially new
revenue-generating opportunities that arise. In assessing
priorities, the following
considerations apply – (1) to determine what is the most
important of the strategic
initiatives based on the expected value of benefits, (2) what is
the enterprise’s capacity
to undertake an initiative, based on the resources present, and
(3) can an initiative
succeed based on benefits and risks (Peppard and Ward, 2016).

With proper
relationships with leaders, teams will be more effective and
more engaged. With proper
geospatial leadership, product teams can emerge from silos and
become
are one of
the strongest influencers in
local and well as global affairs. Companies can measure their
commitment to
communities and the environment, stewardship giving them a
competitive advantage in
the eyes of their consumers now and the eyes of all the
stakeholders for the future.
Location intelligence plays a key role in supporting and
advancing such initiatives by
providing location-specific data on communities, workforce,
and producing data and
information products for different stakeholders. With such
visibility, companies can
more confidently confirm their global businesses and suppliers’
movement and actions
toward improving resource utilization, physical environment,
treatment of labor, and
stewardship to the community. Companies that invest in

location intelligence are well-
positioned to develop and successfully execute ESG plans and
commitments.
Concluding Thoughts
Location information has never been more important to
businesses. Spatially mature
businesses deploy location intelligence to fully realize the
power of location data to better
understand and serve their customers and responsibly grow the
business for the benefit of the
enterprise and its employees, to manage risk for the sake of all
stakeholders. The location value
chain model provides aspiring organizations with a pathway for
spatial transformation. The use
of location analytics in such organizations is driven by well -
defined business needs and usually
produces high impact and value. However, without well-defined
business needs and a spatial
strategy, GIS and location analytics may end up as just another
piece of an organization’s
analytics/technology stack.
In closing, we reiterate the important role of management and

leadership in
contemporary spatial businesses. Several of the case studies
have shown that a key facilitator
of spatial maturity is sponsorship and support by the senior
leadership of business projects
involving location intelligence. From a management standpoint,
this book emphasizes
collaboration, risk tolerance, overcoming resistance, and a
consistent, value-driven focus on
189
business and societal gains. With advances in technology,
location intelligence and analytics,
use cases will continue to evolve. However, we hope that the
principles of leadership,
management, spatial strategy, and decision-making highlighted
in this book will not only
endure but will also provide inspiration for businesses to
compete and lead with location
intelligence.

190
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