unica360 spatial microdata list

SUMMARY:
This document describes the territorial and potential market microdata sets
developed by unica360.
microtarget: sociodemographics &
market microdata
SERVICE DESCRIPTION
4/3/20
info@unica360.com
www.unica360.com

sociodemographics & market microdata
www.unica360.com +34 659 889 032 info@unica360.com page 1
1. BACKGROUND AND OBJECTIVES ................................................................................................2
2. POTENTIAL MARKET DATA (EX. USE CASES) ...............................................................................2
3. SOCIODEMOGRAPHIC, TERRITORIAL AND MARKET RELATED (API)............................................3
3.1. Sociodemographics, income and purchasing power by category........................................3
3.2. Commercial offers, equipment indexes ................................................................................4
3.3. Tourist attraction...................................................................................................................4
3.4. Characterization of buildings, real estate and urban environments......................................5
3.5. Labor population...................................................................................................................5
3.6. B2B Demand.........................................................................................................................5
3.7. Floating population, passerby...............................................................................................5
3.8. Vehicle traffic ........................................................................................................................6
3.9. Urbanity-centrality.................................................................................................................6
5. ANNEX, DESCRIPTION OF FILES.................................................................................................12
5.1. Sociodemographics, income and purchasing power by category. ............................................12
5.2. Commercial offers, equipment indexes......................................................................................12
5.3. Tourist attraction ........................................................................................................................13
5.4 Characterization of buildings, real estate and urban environments ............................................13
5.5. Labor population ........................................................................................................................15
5.6. B2B Demands ............................................................................................................................15
5.7. Pedestrian dynamics, passerby .................................................................................................15
5.8. Vehicle traffic..............................................................................................................................15
5.9. Urbanity-centrality......................................................................................................................15

Unica360 develops customer analysis and geomarketing services.
In the context of these projects, we have assembled and maintained a series of
potential-market spatial data sets, which have been extremely useful to:
▪ Characterize the environment of commercial establishments.
▪ Estimate demand based on the environment type.
▪ Enrich customer data: for example, we can to some extent infer information on
the consumer profile depending on where he lives.
In recent years, the amount of raw data available/collected (big data) has exploded:
business analysis, modelling/learning and prediction have acquired a paramount
importance for companies (data driven). As a consequence, we are experiencing an
increasing demand of refined, targeted data.
We have therefore modeled and standardized our datasets to exploit them in
different ways:
▪ In house: as a base for consulting and modelling ad hoc projects.
▪ Integrated: geomarketing, location intelligence applications.
▪ As a product: datasets delivered as a table (flat file).
▪ As a service: building and API.
▪ Retail (physical shops or services): characterization of the surroundings of each
establishment, segmentation of the commercial network, selection of optimal
location when expanding business, benchmark against competitor points of
sale.
▪ E-commerce: enrichment of customer profiles with information known to
correlate with the probability to shop online: income, habitat, commercial offer
in immediate surroundings, probability of having children, flat/house types,...
▪ FMCG: enrichment of potential retail customers, demand prediction, optimal
local product assortment based on local demand.
▪ Business analytics: customer data enrichment, points of sale distributions,
optimal commercial routes, logistics centers locations and characterization... all
in a data analytics and Big data environment.
▪ Online real estate: real estate data enrichment, search by area types, report
generation depending on the different zones, lead qualification, automatic
descriptives and optimized SEO.

▪ Smart city and transport: optimal location and dimensioning of transport fleets,
simulation of journeys to quantify transportation needs under different,
disrupted conditions, distributed city design.
▪ Out of Home advertising: Analysis of the different advertising channels
depending on the target audience, to maximize campaigns results.
▪ Marketing research: pannel member data qualification, sample design and
segmentation.
▪ Home delivery services: customer data enrichment with their
sociodemographics, types of habitats (urban vs. rural vs. disseminated), home
types, family type and size,...
Other sectors (banking, insurances, personal services, ...) can also gain a great
competitive advantage enriching data about points of sales and customers.
The API currently serves the following types of data:
3.1. Sociodemographics, income and purchasing power by category. Residential
population containing the following dimensions:
▪ Population by sex and age, number of households and families
▪ Average household income
▪ Immigrant proportion, by types
▪ Education level
▪ The average purchasing power per household (classified with the 12
ECOICOP groups) modeled using the National Household Budget Survey,
by the Spanish National Statistics Office, census and other sources

Picture 1. Total and average expenses per household in a trade area with 3,351 households
3.2. Commercial offers, equipment indexes. Indexes of commercial places, hotels,
cultural venues, health establishments, points of leisure, transport resources
that can be consulted in two ways:
▪ Detailed activity: bars, casinos, churches, laundries, schools, vets …
▪ Grouped by activity dimensions:
o Retail
o Food & drink
o Public transport
o Tourism
o Services
o Health
o Culture
o Education
3.3. Tourist attraction. The results of a tourist attraction model, estimates the
number of tourists that visit or pass by each grid 100 meters, on an annual
basis. The model is broken by nationality:
▪ Spanish tourists
▪ Foreign, non-Spanish tourists
The model is based on the different presence of offering for all the activities
related to tourism:
▪ Dwellings: hotels, camping, tourist flats
▪ Beach tourism

▪ Cultural tourism: museums, monuments and their actual popularity, number
of visitors
▪ Horeca: coffee, bars, restaurants, night leisure
▪ Turismo residencial: residencias turísticas
More information about the micro tourist, tourist attraction model.
3.4. Characterization of buildings, real estate and urban environments. Exploiting
the cadastral data and other public resources to provide information about:
▪ Buildings, house properties, ??locales and total farms???, built surfaces.
▪ Properties surface and purpose (housing, commercial, office, industrial,...).
▪ Single-family house portal, garden size.
▪ Buildings by construction date.
▪ ????Habitats by surface???.
▪ Quality of construction.
▪ The maximum, average and minimum number of floors of the buildings.
3.5. Labor population The results of a model of presence of working persons, which
is critical to estimate the commercial demand (restaurants, gyms, pharmacy
...) of a given area.
▪ Total working population.
▪ Population working in headquarters.
▪ Population working in branches.
▪ Labor population segmentated by the different data sources: company
databases, points of interest, online directories, land registry.
3.6. B2B Demand. The presence of companies and branches, segmented by
activity and size. The model reports:
▪ Activity: CNAE code, corrected and enriched by text mining algorithms
▪ Size and number of employees
▪ Branch offices
3.7. Floating population, passerby. An index of passersby, i.e. people who pass
through a given section of roads -tipically, the section between two corners in
an urban area-.

Different types of journeys are defined, depending on the characteristics of
origin and destination:
▪ origins: {residential, work, accommodation, public transport}
▪ destinations: {commercial / leisure, hotels, transportation, tourism}
The result is the 16 origin-destination displacement matrices derived from the
combinations of origins and destinations.
This "multilayer" approach allows the results to "fine-tune" the model results
to accurately target a wide range of urban planning issues and business
applications.
3.8. Vehicle traffic. The vehicle traffic volume index derived for all the links in a
network of roads, depending on the types of displacement, that can be:
▪ Frequent urban or inter-rurban short distance commuting.
▪ Occasional: long distance commuting, travel.
Like the passerby model, it was developed using simulation agents and Monte
Carlo techniques. The current deliverable is a 0-100 index, where the lowest
value road sections are saturated at “0”, in order to maximize the model's
capacity for discrimination in its intermediate and high values.
3.9. Urbanity-centrality. A Classification of micro areas (100 meters grid) according
to the population density, the degree of urbanization and urban centrality. It
classifies the areas in 13 categories, from belonging to high density and
populated city centers (maximum "urbanity", "centrality") to fragmented, low
density rural, far from any urban nucleus (maximum "rurality", "periphery").
This classification is very useful in various sectors: to identify areas of high
demand for home delivery or e-commerce services, logistics and last-mile
optimization, optimal design of collect & delivery network.

Picture 2. 100m grid, The index of hotel industry in Madrid, Salamanca District
Picture 3. 100m grid, the average expenses per household in health, Salamanca District

Picture 4. 100m grid, the total expenses per household in health, Salamanca District
Picture 5. 100m grid, % of housing area> 150m², Salamanca District

Picture 6. 100m grid, Madrid, n companies in CNAE 6820 - Self-renting real estate
Picture 7. Index of passers-by by section of road in Barcelona

Picture 8. Vehicle traffic index by section of road in Madrid
Picture 9. Typology of urbanity-centrality by 100m grid in Valencia

4. GRANULARITY, 100M GRID
The data can be delivered on a variety of spatial aggregation levels:
▪ Municipality
▪ Postal Code
▪ Census section
▪ 100m grid
This last level represents the best granularity. It has several advantages:
▪ The independence of the cartographic process, simplifying the assignment
of coordinate points to squares in the grid, also avoiding layer updates -
that are needed for census tracts, municipalities, urban districts-.
▪ Geometric stability, simplifying density calculations.
▪ Being the center of each square based on round hundreds of meters in the
EPSG: 3857 projected coordinate system, it is easy to convert the results
to another geographic layer without need of the reference cartography (grid
100m).
▪ This coordinate reference system -EPSG:3857- is universal and used by the
most popular web map services (Google Maps).

5.1. Sociodemographics, income and purchasing power by category. Residential
population containing the following dimensions:
5.2. Commercial offers, equipment indexes. Indexes of commercial places, hotels,
cultural venues, health establishments, points of leisure, transport resources
that can be consulted in two ways:

5.3. Tourist attraction. The results of a tourist attraction model, estimates the
number of tourists that visit or pass by each grid 100 meters, on an annual
basis. The model is broken by nationality:
5.4 Characterization of buildings, real estate and urban environments. Exploiting
the cadastral data and other public resources to provide information about:

5.5. Labor population The results of a model of presence of working persons, which
is critical to estimate the commercial demand (restaurants, gyms, pharmacy
...) of a given area.
5.6. B2B Demands
The dataset includes the activity codes -CNAE-, number of companies and
size indicators for the companies
5.7. Pedestrian dynamics, passerby
5.8. Vehicle traffic
5.9. Urbanity-centrality

unica360 spatial microdata list

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