ASSESSMENT OF AMBULANCE RESPONSE PERFORMANCE USING A GEOGRAPHIC INFORMATION SYSTEM

ASSESSMENT OF AMBULANCE RESPONSE PERFORMANCE USING A GEOGRAPHIC
INFORMATION SYSTEM
Jeremy Peters
Department of Geography
Wilfrid Laurier University
Waterloo, Ontario
Canada
Keir Consultants Inc.
3331 Bloor St. W.
Toronto, Ontario M8X 1E7
Canada
(416) 234-2040 ext. 23
Dr. Brent Hall
Faculty of Environmental Studies
University of Waterloo
Waterloo, Ontario N2L 3G1
Canada
19 March, 1998
Abstract
The accessibility, distribution and utilization of emergency health care services have a great
impact on the well being of society. The impact of these factors is heightened by the fact that
ambulance resources, which are an important component of emergency health care, are often
insufficient, resulting in performances below those required to respond reliably to emergency
calls from demand areas. This paper presents and operationalises a framework developed in a
Geographic Information System for assessing ambulance response performance. The results and
their extensions in future work are useful in planning the deployment of this service as they allow
planners to account for response time variations, often caused when ambulances are not available
at the station closest to a call.
Keywords: ambulance response performance, response anomalies, geographic information
system, planning
1. Introduction
Availability of and access to health services are fundamental to the productivity, well-
being and development of society. Emergency health care services are especially important, as
they provide medical attention for those who have the most immediate and critical health needs.
Hence, it is important to evaluate emergency medical service provision to determine whether
these services are currently and will in the future meet the needs of society.
The accessibility, distribution and utilisation of emergency health care services have a
great impact on the effectiveness, efficiency and equity of health service delivery. The impact of

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these factors is heightened by the fact that ambulance resources, which are an important
component of emergency health care, are often insufficient, resulting in performances below
those required to respond reliably to emergency calls from service areas. When ambulances are
not available at the station closest to a call, response time becomes a random variable, causing
anomalies and considerable variation in ambulance performance. Hence, one of the primary
objectives of the provision of emergency ambulance services is to deploy a limited number of
ambulances in a way that ensures adequate response times are met.
Geographic Information Systems (GIS) technology is beginning to be used in emergency
health care service deployment as a planning tool. GIS provides emergency health service
planners with the ability to organise, manipulate and map large volumes of spatially referenced
call data and to communicate spatial concepts to decision makers responsible for service
deployment planning. Using GIS, decision makers are able to understand geographic patterns
and trends in ambulance response performance that would otherwise be difficult to ascertain.
Despite the advantages offered by the use of GIS in assessing ambulance service
performance, the current body of research on this topic is limited both in terms of the number of
applications reported in the literature and in their coverage of three important spatio-temporal
dimensions of response time patterns and trends. These dimensions are the result of ‘real world’
random variables, such as ambulance travel time, that cause anomalies and considerable variation
in response performance.
The first dimension of response time patterns and trends is the analysis and visualisation
of response time anomalies and the ‘normal’ variation in performance levels. Station deployment
decisions that should be based on well-defined areas of normal or consistent performance levels
can only be clearly visualised if anomaly response times are treated separately. The second
dimension is the appropriate use of a complementary set of response time performance indicators
to evaluate effectively trends in ambulance performance over space, time and by type of incident.
In addition to average response time performance indicators, it is important to evaluate
performance in relation to the response time standards of ambulance services and in terms of
trends in response performance over time. The third dimension is the use of tools to help explain
performance indicator patterns and trends. In order to inform better deployment decisions it is
important to understand why an ambulance service has not met performance standards in a
particular area. In many cases, seemingly unusual spatial patterns and trends in performance can
be explained by analysing the variation in response times and examining the variables that affect
this.

3
1.1 Objectives
This paper presents a GIS-based application that utilises a robust and easy-to-use
methodology, for improving the visualisation and analysis of ambulance service performance and
evaluating the consistency of ambulance response performance over both space and time. The
paper addresses deficiencies in the approaches reported in the literature and provides a
methodology that can be used by service deployment planners. The specific objectives of the
paper are:
1. to present a conceptual framework for evaluating and improving emergency health service
vehicle response;
2. from this, to develop a valid GIS-based spatio-temporal methodology for the assessment of
emergency health service vehicle response;
3. to develop an easy-to-use interface, using commercial GIS software, to assist planners in
mapping the spatio-temporal patterns of emergency health service vehicle response;
4. to demonstrate the usefulness of the approach and methods presented in the thesis using
empirical data, from the Ontario Ministry of Health (MOH), in a case study.
1.2 Structure
The paper contains four further sections. Section two places the research within the
broader context of the current body of literature related to the joint roles of space, time and GIS
technology in emergency health service delivery. A conceptual framework and an analytic model
for evaluating and improving emergency health service vehicle response are presented. Section
three describes the research methodology, including the case study and data requirements,
analytic functionality and user interface design. Section four presents and discusses an
application to analyse and visualise ambulance response performance to life threatening call data
supplied by the MOH. The implications of the findings are summarised. Section five concludes
the paper with a brief discussion of the research contribution and a statement of future work
required to develop a more comprehensive ambulance service GIS-based Decision Support
System (DSS).
2. Review
Emergency service planners and decision makers need better geographical methods of
planning and analysis to manage the practical difficulties of inadequate resources, widely
dispersed and unevenly distributed populations, and everyday operational variables (i.e. non-
station ambulances response) that affect ambulance service performance. Birkin, Clarke, Clarke

4
and Wilson (1996) note the importance of geographical methods in the planning and management
of health-care services:
These various dimensions of spatial variation in health-care status, utilisation,
resource allocation, and organisation have profound implications for the planning
and management of health-care services. It is essential that methods of planning
and analysis used to determine the form, level and location of service and
resource provision reflect the important geographical components underpinning
the health-care system. In other words, the planning process should have an
explicit geographical focus. [Birkin, Clarke, Clarke and Wilson, 1996, p.125]
The problems faced in undertaking the planning process, in general, are typically ones of
information availability and access to tools that will identify and evaluate a range of scenarios.
Ambulance delivery services usually maintain large databases of comprehensive records,
including the address locations, of all the calls that they respond to. With these data, the
integration of model-based methods and GIS technology can be used to identify and evaluate a
range of response scenarios that are inherently geographical in nature.
GIS technology is increasingly used and recognised as an important planning tool for the
acquisition, organisation, manipulation, analysis and display of large volumes of spatially
referenced data. The success of GIS implementation in the planning field has been notable in
areas of spatial data creation, task automation and enhanced map production [Hall and Feick,
1997]. Further, Openshaw [1991] has identified spatial pattern description and spatial pattern
relationships as the primary spatial analytic needs that GIS is suited to address. He proposes
three different application contexts within these areas: “testing a priori hypothesis about pattern’s
and relationships present in spatial data; efficient spatial pattern and relationship description; and,
analysis for the purposes of decision support and spatial planning” [Hall, Bowerman and
Feick,1997, p.1]. Historically, predictive and explanatory models lacked the capabilities of GIS
to organise data and communicate spatial concepts to decision makers [Feick, 1991]. Hence,
model development within the flexible framework of a GIS offers potentially rich information for
decision-making.
The integration of model-based methods and GIS technology can have substantial
benefits for managing and analysing data to produce information relevant to decision making and
in simulating the effects of different planning decisions [Feick, 1991]. In this context, GIS
technology is particularly relevant in its potential to provide a unifying framework to facilitate the
development of real-world applications of geographical models and to enhance problem
understanding through the visualisation of spatial, map-based data patterns not immediately
evident in raw data. The role of GIS in the evaluation and planning of emergency services is of

5
increasing significance because of the spatial nature of their operations and information
management requirements.
New methods of GIS-based spatial analysis are currently being developed to analyse the
quality and timeliness of emergency services including automated ambulance, fire, and police
dispatching, logistics, tracking and routing applications [see for example, Ward, 1994, Barry,
1991, Gamble-Risley, 1997, Bridgehouse, 1993, GIS Newslink, 1993, and GIS Newslink, 1994].
These systems use a variety of associated technologies including Global Positioning Systems
(GPS), Automatic Vehicle Location (AVL), Computer-Aided Dispatch (CAD), routing
algorithms, electronic maps and in-vehicle navigation systems to provide real time tracking,
dispatching and routing of emergency vehicles. Dispatch managers use AVL to track the location
of the ambulance fleet through GPS transponders attached to the vehicles and GIS based
computer maps at the dispatch facility. Further, GIS is used in CAD to locate the address of an
incident on a geo-coded street network or property database and as a decision support mechanism
to determine the optimum unit and route to respond to each call.
Although many such systems are used in jurisdictions world-wide, there is relatively little
documentation in either the academic or popular literature. One of the few cases studies reported,
the Emergency Medical Services in Pinellas County, Florida, uses a real time fleet management
system located at the Sunstar Communication Center for emergency response [Badillo, 1993].
This system geo-codes and displays on a wall-sized, color-coded, digital map the address given to
the dispatcher by a 911 caller. The digital map also tracks and displays the location, heading,
direction, and status of each vehicle in the fleet. The system uses current information on the
location, type, and status of each vehicle to select the optimal unit to respond to each call. The
communication centre dispatcher then transmits a signal to notify the selected vehicle. The signal
triggers the emergency vehicle’s on-board computer to display a map of the surrounding area
showing the vehicle’s current location, the location of the emergency, and the direction to that
location. The computer display also shows pertinent information below the map, such as,
patient’s name, nature of the injury, and whether lights and siren are required. The driver then
transmits a signal back to notify the communication centre that the ambulance has taken the call.
Some emergency service organizations, such as the MOH Emergency Health Services
(EHS) branch, use GIS software to analyze the spatio-temporal patterns of historical call data to
assess ambulance response and help plan service deployment based on these historical patterns.
Further, the London Ambulance Service (LAS), in the United Kingdom, recently invested in
MapInfo desktop mapping software for the same purposes. Their application calculates and maps

6
the number of calls made in each ambulance station’s territory. A statistical model is used to
calculate how many calls are expected in an area (potential demand) compared with how many
calls actually take place (realized demand). This application also analyzes ambulance response
by mapping the number of calls that exceed the nationwide target of 14 minutes to reach the
location of an emergency. A review of this application concludes that “since the installation of
the new control room systems, 95% of the calls were reached within 14 minutes, compared with
70% last year, and the use of MapInfo (software) has helped both demonstrate and reach this
dramatic rise in efficiency” [MapWorld, 1997, p.16].
Despite the advantages offered by the use of GIS in evaluating and improving emergency
response performance, neither the MOH’s EHS branch nor the LAS applications fully examine
the three spatio-temporal dimensions noted earlier of response time patterns and trends in
assessing ambulance performance. These dimensions are the result of ‘real world’ random
variables, such as travel time, that cause anomalies and considerable variation in ambulance
performance. An approach to incorporating these factors in a GIS to analyse ambulance
performance is now presented.
2.1 Analytic Model
The model shown in Figure 1 uses a set of five attributes for ambulance call data to
assess response performance. These attributes include the number of calls, the response time for
each call, the purpose of each call, the date and time when each call is received, and the station
location of the ambulance that responds to a call. Information availability can vary considerably
between ambulance services in terms of what data are collected and available for analysis.
Hence, the intent of the model is to demonstrate how existing ambulance call data can be used
more effectively.
The first dimension in this model is the independent analysis and visualisation of
response time anomalies and the ‘normal’ variation in performance levels. This dimension is
important, as the identification of areas where response times need to be improved should be
based on well-defined areas of consistent performance levels that can only be clearly visualised if
anomalies are removed. Moreover, it is important to visualise anomaly response times
independently in order to help understand their cause, distribution, statistical significance and to
identify their occurrence in the future.

7
Figure 1: An analytic model to assess ambulance response time performance.
Anomalous response times can be identified statistically and then treated separately from
other response times for calculating response time performance indicators. For example,
response times can be converted into standardised z-scores to filter cases that are within 95
percent statistical probability, assuming a normal distribution, of the mean response time and
anomalous cases that are more than two standard deviation units from the mean.
The second dimension in Figure 1 defines a complementary set of response time
indicators to evaluate trends in ambulance performance over space, time and by type of incident.
Average response time indicators should be calculated based on the ‘normal’ variation in
performance levels so that anomalies do not skew the averages and distort the spatial
representation of ambulance performance. Moreover, a spatial performance indicator, using the
Ambulance
call data
Geo-reference
and aggregate
call data by
service area
Calculate
response time
anomalies for each
service area
Determine consistent
performance levels for
each service area
Calculate & map
average response
time performance
indicator
Calculate & map percent of
calls within response time
threshold performance
indicator
Create territories
serviced by each
ambulance station
Calculate & map
change in average
response time
performance
indicator
Generate response
time frequency
distribution tables
Generate call volume
frequency
distribution tables
Evaluate ambulance performance patterns and trends across space and time to
determine the historical efficiency and effectiveness of ambulance services in
relation to their catchment populations.
Service area
spatial layer
Dimension 2: Response time performance indicator development and application.
Dimension 1: The independent analysis and visualisation
of response time anomalies and the ‘normal’ variation in
performance levels.
Dimension 3: Methods to help explain performance indicator patterns and trends.

8
percent of calls within a response time standard, can be used to provide a second measure to
compare with the average response time indicator’s representation of performance. This indicator
provides an effective measure for visualising performance in relation to an ambulance service’s
response time standards. A relative performance indicator can also be used to measure all calls
together since it is not affected by anomalous response times.
In addition to using these two indicators to identify areas of adequate and inadequate
performance for a given time period, it is also important to have a response time indicator that can
evaluate trends in ambulance performance over time and by type of call. Planners should know
which of these contexts have improved, remained constant, or worsened over time or by type of
incident to help identify areas and time periods where ambulance performance needs to be
improved.
A performance indicator that shows the statistical change in average response times for
two or more time periods or types of calls can provide an effective measure of trends in
ambulance performance. Such an indicator can test hypotheses whether or not response times
have, for example, significantly improved after service modification; are better on weekends than
week days; are worse during rush hour than the rest of the day; or are better for life-threatening
types than less serious types of calls. GIS-based spatial analysis can be used to subtract and
compare two or more average response times for service areas. In addition, statistical analysis,
using analysis of variance (ANOVA), can be used to determine the probability of a statistical
difference in two or more comparisons.
The third dimension in Figure 1 addresses the question of whether or not an ambulance
service is not meeting performance standards in a particular area. In many cases, seemingly
unusual spatial patterns and trends in performance can be explained by analysing the variation in
response time and the variables that affect this. For instance, frequency distribution tables can be
generated to compare the frequency of unique response times and the variation in those response
times represented by z-scores. The frequency of and variation in response times can help explain
why a performance indicator value for one area is different from neighbouring areas.
Further, variables such as the location of an ambulance responding to a call, route taken,
traffic flow, time of day, season/weather, and road conditions and restrictions can all cause
anomalies and sometimes considerable variation in response times. These variables impact on
response times by determining the distance and speed at which ambulances travel to respond to
calls. The location of an ambulance at the time of response and its route taken are particularly
important in helping to explain performance indicator patterns and trends because these variables

9
are directly controlled and planned for by ambulance services. The other variables can be
manipulated to varying degrees through resource deployment and route planning.
As noted above, information availability can vary considerably between ambulance
services in terms of their capacity to identify the location of an ambulance at the time of a call
and the route taken in response. At a minimum, the station that the ambulance is based at can be
identified to help explain the impact of distance on response times. Using this information,
territories ‘normally’ serviced by each ambulance station can be defined and mapped. Then
frequency distribution tables can be generated to compare the volume of calls answered by each
station and the average response times for those calls in any given size area within each territory.
Using these methods, it is possible to depict relatively easily using GIS the Euclidean (straight-
line) distances travelled by ambulances based at each station in relation to their performance
levels and in relation to the distances and performance levels of other ambulances. With the
integration of appropriate statistical and geographical modelling methods and outputs into a GIS
application that examine these spatio-temporal dimensions, historical call data can be used to
assess more accurately ambulance response performance.
A GIS-based framework for utilising facility-based spatial performance indicators is
presented in Figure 2. This framework describes the operational GIS design for the analytic
model discussed above. The framework describes the interaction between eleven components,
including ambulance call data, spatial data, a graphical user interface, spatial models, GIS,
statistical analysis, performance indicator calculations, cartographic presentation, mapping,
evaluation, and decision making.
Within this framework, an intuitive and easily navigable graphical user interface (GUI)
allows planners to interact with all features of the GIS environment. The GUI is a conceptual link
between the user’s interaction with a computer-based application and what it offers them as a
decision support tool. It includes all of the considerations that planners require to understand the
application and to communicate effectively with it [Medyckyj-Scott and Hearnshaw, 1993]. The
GUI is also task-oriented, in that it enables users to generate performance indictor maps by
specifying the parameters they need to customise their output without having to deal with a
complex series of functions. The GUI is the only part of the application that is visible and with
which users interact. Thus, for the emergency health service planner, it is 'the system' and its
usability is of crucial importance.

10
Figure 2: A GIS framework to evaluate ambulance response performance.
The application uses the parameters specified in the GUI to evaluate ambulance
performance through the use of appropriate statistical and spatial modelling methods and map
outputs in the form of response-based spatial performance indicators. Models are designed for
each type of performance indicator as well as the methods used to help explain performance
indicator patterns and trends. Statistical analysis and GIS database manipulation, spatial analysis
and mapping functionality are programmed to operationalise each model. For example,
Structured Query Language (SQL) is used to manipulate and help transform ambulance call data.
SQL is used for many tasks in the system, such as selecting subsets of call data based on
parameters specified through the GUI. SQL is used to transform call data into performance
indicators, demand indicators, response time variation indicators (e.g. z-scores) and frequency
distribution tables. In order to accomplish these tasks, SQL uses aggregate functions to calculate
averages and count records as well as data modification requests (e.g. update) to transform data
such response times into z-scores using a customised algorithm.
In addition to GIS database manipulation, spatial and statistical analyses are used to
transform call data into performance indicators and to generate service territories or catchments.
Geographic
Information
System
Ambulance
call data
GUI
Spatial
data
Statistical
analysis
Spatial
models
Performance
indicator
calculations
Mapping
Evaluation Decision
making
Cartographic
Presentation
User

11
Overlay analysis is a GIS function that spatially compares map features and attributes across two
or more map layers. This is used to generate a single change in average response times from two
different maps, each showing average response times for a different time snapshot or for different
types of calls. Further, ANOVA is used to show where the difference in average response times
is statistically significant. Feature generalisation is a GIS function that determines contiguous
groupings of identically valued map features and attributes to make an underlying pattern more
apparent. This tool is used to generate service territories by combining service areas where a
station has historically responded to the majority of calls. The interaction between the model,
GUI, data, statistical analysis, and GIS components of the framework operationalises the
calculation and mapping of each performance indicator.
In the evaluation component of the GIS framework, the spatial performance indicator
outputs are used to evaluate a range of scenarios. Emergency health care service planners can
assess the historical spatial distribution of supply relative to demand. Further, planners can also
determine the underlying patterns of accessibility in relation to emergency service response
standards and identify areas with deficient service provision. Moreover, they can determine the
time and nature of the calls that have a significant impact on response performance.
Interactive tools are also used in the evaluation to generate frequency distribution tables
to help explain performance indicator patterns and response time anomalies. This allows planners
to determine if unsatisfactory responses are caused by responses from locations other than the
closest station. Once planners determine which station has responded to a call, they can
determine the pervasiveness of the problem in terms of the frequency and variation in response
times in relation to response time standards.
This information can help decision-makers plan service deployment in order to improve
service provision by identifying areas where ambulance response is consistently outside the time
standard. Using this information, decision makers can better target when, where, and for what
type of calls ambulance response performance should be improved.
3. Data and Application Design
In order to operationalise the analytic model and GIS framework presented in the
previous section, data from the Province of Ontario Ministry of Health (MOH) are used. The
MOH’s Central Ambulance Communication Centres (CACCs) use historical database records of
call data to represent the history of demand for and response of ambulance services to calls. Until

12
recently, two geographic reports, called ‘GeoPlot Reports’, were manually produced to show
choropleth maps of the total number of calls and average response time for each cell of a 1 km2
UTM grid that covers the Province of Ontario. To achieve this, every emergency call is geo-
referenced as it is received to its appropriate grid cell according to user-defined periods of time
(dates, day(s) of the week and time of day) and types of calls (e.g. Priority Code 4 - life
threatening). The GeoPlot reports have now been automated, using MapInfo GIS software.
However, their usefulness still falls far short of the needs expressed in the analytic model in
Figure 1 and GIS framework in Figure 2.
Five types of historical call data are used to implement the procedures discussed in
Section 2 and to assess ambulance response performance. These data include the number of
ambulance calls; the response time for each call (the elapsed time between the receipt of a request
for an ambulance and its arrival on the scene); the purpose of each call (the priority code), the
date and time when each call is received, and the station location of the ambulance that responded
to the call (the station code).
The number of calls that occur in each grid cell provides an indicator of the areas where
various levels of demand have occurred (and are likely to occur in the future). This demand
information is essential in helping to locate new ambulance services and to minimise response
times in areas that have the greatest need for this service. Response time data can be transformed
into spatial performance indicators to measure, for example, ambulance performance in relation
to response time standards and the demand for services. The nature or purpose of a call indicates
the type of demand and services required so that ambulance performance can be measured in
relation to service standards and demand areas. For example, calls classified as life-threatening
generally have a shorter response time requirement than other calls and tend to occur in specific
areas, such as retirement communities, than those that are classified as urgent but not life
threatening. The date and time of a call is used to evaluate demand and performance in relation
to a specified time frame and also to establish daily trends in receipt of calls. This information
can be used to evaluate, for example, whether there are discernible lulls in activity and whether
there are repetitive peak times when life threatening health events occur (such as early in the
morning or late at night). The station location of an ambulance provides a means of estimating
the (straight line) distance travelled to respond to a call. This information, in addition to being
useful in planning future deployment strategies, helps explain ambulance response time
anomalies.
3.1 Operationalisation

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Several methods were employed to operationalise the analytic model and GIS design
framework with the data described above. These methods are described in terms of five
processes that are operationalised together to address the three dimensions in the analytic model
and their relationships. The relationships between the five processes discussed in this section are
illustrated in Figure 3.
Figure 3: Relationship between the five processes used to operationalise the analytic model and
GIS design framework
The first process, Response Time Variation and Anomaly Identification, addresses the
first dimension in Figure 1. This process enables calls to be treated as normal or anomalous for
the appropriate application of the second process, Response Time Performance Indicators. The
Change in Average Response Time Performance Indicator is the only indicator among the three
in the analytic model (e.g. Response Time Threshold, Average Response Time and Change in
Average Response Time) that is described here, as the process is similar to the others.
This indicator specifically addresses the second dimension in Figure 1, namely to
visualise spatio-temporal patterns and trends in ambulance performance by type of incident. The
third (Call Volume Frequency Distribution), fourth (Response Time Frequency Distribution) and
fifth (Service Territory Generation) processes all address the third dimension in Figure 1 and
provide complementary methods to help explain performance indicator patterns and trends
described through the second process. The Service Territory Generation process requires call
volume information generated in the Call Volume Frequency Distribution process to generate
territory polygons made up of adjacent grid cells where a single station has responded to at least a
user-specified proportion of calls.
Response Time
Variation and Anomaly
Identification
Call Volume Frequency
Distribution
Service Territory
Definition
Response Time
Frequency Distribution
Response Time
Performance Indicators

14
The model used in the first process defines two types of statistical anomalies, including
single calls in a response UTM grid cell and response times that have a z-score of ≥ ± 2.0. This
model provides the user with the options to exclude calls with or without one or both types of
anomaly from further analysis. A single call is considered an anomaly in this case because it is a
poor indicator of a consistent performance level. A response time is considered an anomaly if it
is within five percent of the values with the greatest variance from the mean (beyond two
standard deviations).
The output from this process and final step is a call database table. This table represents
either all selected ambulance calls, selected calls without response time anomalies, or selected
calls with response times that represent anomalies. If the user chooses the default option, then the
existing selected call data set becomes the default call table. If the user chooses the option to
remove statistical response time anomalies from the selected call data set, then all call records
that do not fit the anomaly criteria are selected, based on the z-score and total call frequencies, to
create a call table without anomalies. Or, if the user chooses the option to map anomalies
independent of all other response times, then all call records that meet the anomaly criteria are
selected to create a table representing calls with anomaly response times. The resulting table
enables anomaly and non-anomaly calls to be treated independently for the appropriate
application of response time performance indicators.
The Change in Average Response Time Performance Indicator process operationalises
the second dimension of the analytic model to provide an effective measure of trends in
ambulance performance over space, time and by type of incident. This indicator is calculated and
thematically mapped to show the change in average response time for two different periods of
time or types of calls.
The actual model used for this defines the performance indicator as the difference
between two average response times and calculates the probability of a statistical difference in the
averages using a one-way ANOVA. Negative change (e.g. –2 minutes) in average response time
minutes, shows an improvement in ambulance response, whereas positive change (e.g. 2 minutes)
in average response time minutes, shows a decline in ambulance response time performance.
The Call Volume Frequency Distribution process operationalises the third dimension of
the analytic model to help explain performance indicator patterns and trends in terms of the
distances ambulances based at each station travel in relation to their performance levels. This
process generates a frequency distribution table showing the volume of calls responded to from
each station and the average response time for any given area by station on a performance

15
indicator thematic map. This process helps to explain the various geographical factors of the
service delivery system that intervene to either increase or decrease response time.
The model used in this process defines an interactive performance indicator map, where
the user can select a ‘Call Volume Frequency Distribution’ button from the GUI and, using the
mouse, query the graphic display of response times by selecting one or more grid cells. The
frequency distribution of responses for all stations that responded to selected calls, are tabulated
and displayed. This interactive model provides planners with a tool to enquire easily about
performance indicator patterns in terms of the ambulance stations that respond to emergency
calls.
The Response Time Frequency Distribution process also operationalises the third
dimension of the analytic model to explain performance indicator patterns and trends.
Specifically, this process calculates the frequency of and variation in unique response times. The
resulting frequency distribution table shows the frequency of unique response times and displays
the associated z-scores for each response time for any given area on a thematic map.
Lastly, the Service Territory Generation process operationalises the third dimension of
the analytic model. This process allows the user to establish the functional catchments of
ambulance service depots by examining the (straight line) distances ambulances travel from their
base station. Territories historically serviced by selected ambulance stations are defined and
overlaid on thematic maps generated in the Response Time Performance Indicator process.
The model used in this process defines an ambulance station’s functional catchment as
the area covered by adjacent grid cells where a station has dispatched an ambulance to respond to
a user-specified proportion of calls in each grid cell. For example, if 75 percent is specified as the
majority of calls, then a station’s catchment area is defined by all adjacent grid cells in which 75
percent or more of the calls were responded to by ambulances dispatched from that station. This
approach allows a user to define catchments dynamically, according to a user-specified
proportion of calls because there is no statistical or EHS industry standard for defining the
percent of calls that comprise ‘normal’ catchment areas.
3.2 Interface Design and Navigability
Considerable thought was put into the design and functionality of the GUI for the GIS
application as this facilitates navigation through its functions. The three main components that
make up the GUI are “the windowing system, a set of tools for creating windows and their
characteristics; the imaging model, controls the drawing of the screen representations such as
fonts and icons; and the application program interface, acts as an interface to the program

16
operations and controls feedback from screen representations” [Raper in Masser and Blakemore,
1991, p. 105].
As part of a user-centred, task-based GIS design methodology, primary design objectives
such as task-orientation, user-control, standardisation and consistency were used. Task-oriented
design focuses on testing a priori hypotheses about the patterns and relationships present in the
ambulance call data. This is accomplished using ‘wizards’ or ‘task assistants’ to invoke a series
of logically connected dialogues to step the user through the task of specifying parameters in
order to create performance indicator thematic maps. The parameters used include, in order of
specification, the type of indicator map to produce, the historical call database and spatial
reference grid area to use, the options for handling anomaly response times, the options for
creating service territories, and the parameters for the historical call data to use (e.g. the time
frame of calls, the type of calls, and the station(s) that answered the calls).
An appropriate default option for each parameter is specified in the ‘task assistant’ to
help the user make the appropriate choices for each task. By default, no option to handle
anomaly response times is set for average response time and response time standard indicators
because any or none of the options could be equally appropriate depending on the user’s
objectives. The option to select calls without both types of anomalies is set as the default for the
change in average response performance indicator because analysing the change in average
response times for anomaly calls exclusively does not provide useful information and the
anomalies skew the averages. Furthermore, by default, an option to create service territories is
not set because functional territories are not a part of any performance indicator and should only
be generated to help explain performance indicator patterns and trends.
Figure 4 shows the content and sequencing of a performance indicator thematic map ‘task
assistant’ from the application GUI. This assistant consists of two main dialogues and two
secondary dialogues, but could include additional dialogues to break up the steps for specifying
parameters into more manageable parts. The first dialogue (Create Thematic Map for UTM Grid
Area) is used to specify all of the parameters for generating a performance indicator, except for
the parameters for the historical call data, which are specified in the second dialogue. The first
dialogue also has two buttons to access two secondary dialogues for selecting the historical call
database and spatial reference grid area to use. The task-oriented design enables users to generate
performance indicator maps by specifying the parameters they need to customise maps without
having to deal with a complex series of GIS functions.

17
Since useful software must be responsive to user needs, user-control is fundamental to the
GUI design process. The user needs the flexibility and tools necessary to customise thematic map
creation for their own individual needs while maintaining cartographic standards by setting
Figure 4: MOH GSR application performance indicator thematic map task assistant
default options to compare different geographic areas. Further, users should have the option to
modify default map design settings after the thematic map is generated to enhance the
visualisation of a particular scenario. In this context, a ‘task assistant’ should have the ability to
control which map layers (e.g. municipal boundaries, and ambulance services) and labels (e.g.

18
average response times) are displayed, and to modify the thematic ranges, colour shading,
symbols and legends.
For example, Figure 5 shows the map layout assistant (Select Layout Options) that is
used to modify which map layers and labels are displayed on maps of ambulance responses to
calls. Users also need the flexibility of interactive buttons to query any area on a performance
indicator map to help explain performance patterns by generating call volume and response time
frequency distribution tables. A main pull down menu and button pad can be used to access these
‘task assistants’ and buttons respectively to provide users with the desired flexibility and control.
Figure 5: Application map layout task assistant
4. Results
The above methodology was applied to three study areas (Niagara Falls, St. Catharines
and Welland) in the Regional Municipality of Niagara in southern Ontario, Canada for Priority 4
(life threatening) calls, during a 2 year period from 1995 to 1996. The analysis was based on
MOH response time standards which require that life threatening calls have an average response
time of eight minutes or less. Further, the methodology was applied to the study areas and call
data to address each of the three dimensions of response time patterns and trends. The fact that
the three ambulance services are in the same vicinity and respond to each other’s calls, when an
ambulance from the closest station is not available, helps to demonstrate the impact of ambulance
availability and distance travelled on ambulance response performance. The results are
summarised below.
The Response Time Variation and Anomaly Identification process was applied to address
the first dimension of Figure 1 and thus assess the value of the independent analysis and
visualisation of response time anomalies and the ‘normal’ variation in ambulance response times.

19
As noted earlier, this model provides the options to exclude calls with or without one or both
types of anomaly (e.g. single calls in a grid cell and response times that have a z-score of ≥ ± 2.0)
from further analysis.
After the Response Time Variation and Anomaly Identification model was applied to
exclude both types of anomalies, Figure 6 shows that response time performance indicators are
generally within MOH standards for all three study areas. In St. Catharines and Niagara Falls in
particular, large areas of 9 km2
and 5 km2
respectively changed status in terms of meeting MOH
standards after anomalous calls were filtered from response time calculations. A clearer and
more consistent pattern of ambulance response is shown in both these study areas after anomalies
were removed. Figure 7 shows that the distribution of response time anomalies is similar to
overall demand and can therefore impact response performance everywhere there is demand.
This is especially true on the peripheries of the study areas, where demand is lowest and response
time variation is greatest. The results show that by applying the Response Time Variation and
Anomaly Identification process, planners can better identify areas where ambulance response is
consistently outside the response time standard. This more accurate representation of the
underlying patterns of accessibility can then help decision-makers plan service deployment in
order to improve service provision efficiency and consumer equity for those areas.
The Change in Average Response Time Performance Indicator process was used to
address the second dimension of the analytic model, and thus help provide a complementary set
of response time performance indicators. This process provides planners with an effective
measure of change in ambulance performance over space, time and by type of incident. Unlike
the average and relative response time indicators, the results of this process show where and when
the socio-spatial differentiation (e.g. various geographical factors, socio-demographic
characteristics of the consumers, and organisational characteristics of the service delivery system)
of actual demand (the normative need for ambulance services) is a significant factor in relation to
the corresponding spatial and temporal patterns of realised response times.

20
NIAGARA
FALLS
ST.
CATHARINES
GRID CELL
AVERAGE
RESPONSE
TIME
ROUNDED
TO THE
NEAREST
10 SECONDS
WELLAND
6.4
5.55
7.3
8.3
5.34.1
7.4
7.4
6.3
4.5
6.4
5
5.2
5.4
7.1
7.2
8.2
7.5
7.1
7.5
5.5
8.5
8.4
6.4
8
7.4 6.4
7.1 7.2
7.5 8.3
4.4
6 7.1
4.5
4.3
4.4
4.1 3.4
5
8.5
5.4
6.2
5.4
7 7.3
7.3 7
7.4
5.15.3
5.5
5.4
10.5
8
8
8.6
9.4
5.3
4.5
6.4
6.3
64.5
5.1
5.1
5.1
5.2
7
5.56.4
3.4
4.1
6.4
3.3
6.2
4.5
6.4
5.4
7.5
11
7.1
6.4
6.3 5.3
7.3 5.5 5.2
8.4 7.2
6.5
7.3 7
9.3
8.29.3
8.5
10.2
8.2
6.1
7.2
7.5
7.3 7.2
7.1
9.2
8.3
6.4
5.3 6.5
7.1
8.1
8
7
8.2
7.4
76.1
8
5.5 6.2
5.5
6.25.5
6.26.3
6.15.3
6.2
7.3
7.5
6.5
6.3
6.1
5.2 5
5.4
6.3 6
6.4 6.1
6.2
5.2 5.4
6.5
8.5
8.3 9.3
7
7.5
7.1 6.5 6.2
7.4
6.2
10
6.4
7.2
7.46.3
11
8.1
10.4
7.2 7.5
7.3
8.3
8.5
Average Response Time
Minutes per 1km Cell
> 11:00 (0)
8:10 to 11:00 (32)
4:10 to 8:00 (141)
0:10 to 4:00 (3)
0 (27)
Figure 6: Average response time performance indicator
Specifically, in Figure 8 the socio-spatial differentiation of response times based on the nature
and urgency of demand shows a significant difference and clear pattern in all three study areas in
grid cells where Priority Code 4 calls have a lower average response time than Priority Code 3
calls. The MOH analysis of the demand for and response of ambulance services focuses on these
two types of calls, where ambulance response and the deployment of ambulance services are most
important. In all three study areas, there is a clear and consistent pattern of grid cells shaded in
light and dark grey, that display the change in average response time value, showing a strong
probability of a significant difference where the average response times for Code 4 are

21
Fort
NIAGARA
FALLS
ST.
CATHARINES
GRID CELL
NUMBER
OF
CALLS
Ontario
WELLAND
1
7
2 7
2 2
22
1
1
1
5
1
1
23
2
1 2
1
2
1 1
11
2 1
1
1
15
1 1
43
4
1
1
2
3
2
4
2
2
5
3
1
1
1
5
2
1
1
3
3
5
4
4
1
5
5
3
21
13
3
1
2
2 7
1
1
2
3
11
4 3
1 5
4 6
2
3
1
1 1
3
2
1
131
3 3
35
1
2
1 3
1
1 2
2
1
1
Calls Per 1km Cell
4 to 7 (20)
2 to 3 (42)
1 (47)
Figure 7: Call distribution and frequency
lower than the Code 3 averages. This common pattern shows how organisational factors of
ambulance service delivery, such as how MOH prioritises requests for ambulance services, have a
significant impact on response time performance.
The socio-spatial differentiation of response times based on day and night ambulance
work-shifts shows a significant difference in more grid cells in all three study areas, where the
day-time shift has a lower average response time, than the night-time shift than vice-versa. These
results show that by applying the Change in Average Response Time Performance Indicator
process planners can determine the time and nature of the calls that have a significant impact on

22
response performance. Hence, planners can better target when, where, and for what type of calls
ambulance response performance should be improved.
Fort
NIAGARA
FALLS
ST.
CATHARINES
GRID CELL
CHANGE IN
AVERAGE
RESPONSE
TIME
ROUNDED
TO THE
NEAREST
10 SECONDS
Significant Difference
in Average Response
Times at 0.05 Level
of Significance
Ontario
WELLAND
-5.1-3.5 -5 -3.5 -5.5-6.1
-4 -3.5-2.4 -4.4
-2.4
-2.4
-2.2-3.2 -4 -6.4
-2.5-3 -2.5-2.5-2.2 -3
-3.2-14.5-4.3-4.4 -2.1-8.5
-1.6-2 -4.5-1.2-2.3
-2.4-2.5 -3 -2 -1.2-2.4
-1.4-1.2 -2.2-2.3 -2.2-3.4
-2-3.1-4.3-3.4-3.1-3.3
-4-4.3
-3.4-3.5
-2.4
-4.5-3.4-6.1
-2.3-3.5-3.2
-4.5-2.5-4.5-2.4 -3 -2.4-4.2
-3.1 -2 -2.3 -6 -2.3-3.2
-3.3-2.3-2.4-3.4
-4.5
-3.5
-4.1-8.1-4.2-4.3-3.3
-3.2-7.2-3.4-4.1-3.5
-3.1-3.2-3.4
-3.5
-2.4
-5.3
-3.2
-3 -4.3
-3 -4.1
-4 -3.2
-4-3.4 -4 -3.1 -3.5-3.2
-4-5.1
-6.1
-3.3-2.2-2.5
-4.4-2.3
-5.5
-3.5
-11 -2.2
-6.4 -6.1
-2.4 -3.5 -4
-2.4
-3.2-1.4
-2.3-3
-8.3 -3 -3.3-3.5
-7.4-2-4.4-2.2-4.4
-2.4-2.2 -2-3-3.2
-1.4-17 -3.2-2.2-2.4-2.2
Change in Avg. Resp. Time
Minutes per 1km Cell
>= 3:00 (0)
0:01 to 2:59 (2)
0:00 (0)
-0:01 to -2:59 (66)
<= -3:00 (103)
all others (32)
Figure 8: Change in avgerage response time (Code 4 – 3)
In regard to the third dimension, the value of the Call Volume Frequency Distribution,
Response Time Frequency Distribution and Service Territory Generation processes are evaluated
in terms of how these models help explain response time anomalies and performance indicator
patterns in the three study areas. This allows planners to determine if unsatisfactory response is
the result of responding to calls by stations other than the closest station. Once planners
determine which station is responsible for a call response, the Response Time Frequency

23
Distribution process is used to determine the pervasiveness of the problem in terms of the
frequency and variation in response times in relation to response time standards. For example, in
the case of Niagara Falls, it was determined that one unusually high average response time of 10
minutes and 50 seconds in the south west corner of the study area (Figure 6) was skewed by one
poor response time belonging to an ambulance based in St. Catherines (station 220 00)
responding to a call in the area (Tables 1 and 2). This unusually high average response time is in
the only grid cell outside the functional catchment for the southern part of the study area where
the Niagara Falls station (143 00) responded to 75 percent or more of the calls.
Table 1: Call Volume Frequency Distribution table for Niagara Falls
Niagara Falls: Figure 6
Resp. time Number Percent z-score
(minutes) of calls of calls
6 1 20 -1
8 1 20 -0.6
10 1 20 -0.2
11 1 20 0
19 1 20 1.6
Table 2: Response Time Frequency Distribution table for Niagara Falls
Table 3 contains a summary of the Call Volume Frequency Distribution process applied
to explain the response time anomalies in Figure 7 according to the various stations that
responded to calls. The summary of the Call Volume Frequency Distribution tables for all three
study areas shows that calls responded to by ambulances based at stations outside each study area
represent a much greater proportion of calls whose response times represent statistical anomalies
(Figure 7) than the normal variation in performance (Figure 6). Generally, the Call Volume
Frequency Distribution process shows that when ambulances respond to calls from a location
other than the closest station response time becomes a random variable, often causing anomalies
and response performances below those required to respond reliably to calls from demand areas.
Niagara Falls: Figure 6
Station Number Percent Average
of calls of calls response
143 00 3 60 8:15
143 02 1 20 9:49
220 00 1 20 19:28

24
Table 3: Summary of the Call Volume Frequency Distribution tables for all three study areas
5. Conclusion
This paper examined issues and models relating to the evaluation of emergency health
service performance. The goal of this work is to help plan the deployment of a limited number of
resources in a way that ensures ambulance service is available within a maximum time with
guaranteed reliability. The paper examined the use of GIS technology as a planning tool for
improving the visualisation of analytic results of performance assessment.
The introduction of the paper noted the fundamental importance of the accessibility,
distribution and utilisation of ambulance services to the effectiveness, efficiency and equity of
emergency health care service provision. However, ambulance resources are often insufficient
and sometimes inefficiently and inequitably distributed in the target population, resulting in
response times below those required to respond reliably to emergency calls. The complexity of
this problem is heightened by the fact that when ambulances are not available at the station
closest to a call response time anomalies occur and cause considerable variation in ambulance
performance.
The paper has made a contribution to current knowledge, in the following ways. First, it
presents and operationalises an analytic model and GIS design framework for assessing response
performance. As noted previously, the current body of related research is limited in its discussion
of spatio-temporal dimensions of response time patterns and trends, including the independent
analysis and visualisation of response time anomalies and the ‘normal’ variation in performance
levels; the appropriate application of a complimentary set of response time performance
indicators; and the use of tools to help explain performance indicator patterns and trends. The
analytic model and GIS design framework developed provide a conceptual framework to address
these analytical deficiencies in existing approaches.
St. Catharines Niagara Falls Welland Total
# of calls by local stations: Fig. 6 2878 1908 922 5708
# of calls by other stations Fig. 6 520 166 56 742
# of calls by local stations: Fig. 7 70 54 26 150
# of calls by other stations: Fig. 7 52 33 14 99
% of calls by local stations: Fig. 6 84.7 92 94.3 88.5
% of calls by other stations Fig. 6 15.3 8 5.7 11.5
% of calls by local stations: Fig. 7 57.4 62.1 65 60.2
% of calls by other stations: Fig. 7 42.6 37.9 35 39.8
Avg. Response: Fig. 6 6:34 5:44 5:51 6:11
Avg. Response: Fig. 7 15:12 12:49 12:15 13:54

25
Second, the paper describes the operationalisation of a GIS-based application that utilises
a robust and easy-to-use methodology, for improving the visualisation and analysis of ambulance
response performance. As noted previously, GIS provides emergency health service planners
with a unifying framework to facilitate the development of real-world applications of
geographical models and to communicate geographic patterns and trends that would otherwise be
unknown in ambulance response performance to decision makers.
Despite the advantages offered by the use of GIS in improving emergency response
performance, existing GIS-based applications reported in the literature do not fully examine the
three spatio-temporal dimensions noted earlier of response time patterns and trends. With the
integration of the proposed statistical and geographical modelling methods and outputs, historical
call data can be used to assess more accurately ambulance response performance. Thus, the
proposed methodology can have substantial benefits for managing and analysing data to produce
information relevant to decision making.
Third, the paper has provided several useful spatial models and methods to assist
emergency health care service planners and decision-makers to evaluate and improve response
performance for ambulance services. Described below are some of the areas which require
further research that can extend the spatial models and methods provided in the paper in order to
develop further the prototype application into a more comprehensive ambulance service GIS-
based decision support system (DSS).
A GIS-based DSS should include methods for the socio-spatial differentiation of actual
demand and especially the potential need for ambulance services. These methods can be used to
measure the relative importance of organisational factors and socio-demographic characteristics
of consumers in relation to the corresponding spatial and temporal patterns of call response. The
spatio-temporal characteristics of demand can be explained by socio-demographic variables (e.g.
population density, population age, income), land use variables (e.g. land values and land use
mix) and activity variables (e.g. traffic volume) although it is difficult to isolate the influence of
any one factor. In this context, a statistical model can be developed to calculate and compare the
number of calls with specified characteristics that are expected in an area (potential demand) with
the number of calls that actually took place with the same characteristics (realised demand). This
would allow the evaluation of response performance to be enhanced by modelling differential
response for potential for individual use behaviour in relation to a surrogate measure of need.
Further, measures of potential geographic accessibility can be incorporated into the
analytic model and the GIS framework to compare respectively the optimal solution to ambulance
service location and response performance, to the existing system. This would allow the

26
evaluation of response performance to be enhanced by modelling the potential for individual use
behaviour in relation to a surrogate measure of need that accounts for the variation in actual
response times. Specifically, the development of appropriate probabilistic location-allocation
models can contribute to the emergency vehicle response planning process by determining the
locational configuration of facilities to reduce distance deterrence and by determining the
allocation of resources to increase service capacity.
In addition to location-allocation analysis, methods for “what if“ analysis should provide
the ability to calculate and compare the spatial distribution of expected response times based on
alternative spatial distributions of potential need, land use variables and street network variables
in relation to alternative ambulance deployment configurations. In a GIS-based DSS the
evaluation of ambulance response should include the ability to model demand based on potential
changes to the various geographical, socio-demographic, and organisational factors that influence
the accessibility and utilisation of emergency health services. It is important to compare
alternative distributions of potential need in order to help plan service deployment based on
demographic projections for various planning periods. It is also important to compare different
land use scenarios in order to help plan service deployment based on anticipated land-use changes
outlined in planning documents. Further, it is important to compare potential changes to
attributes of the road network (e.g. new roads, left/right turns, traffic lights, stop signs, speed
limits, construction, seasonal conditions and time of day traffic flow) in order to calculate a more
reliable measure of expected response time under various potential road network configurations.
Similar to location-allocation analysis, this model can use a single line road network and
impedance factors to compute the shortest expected response time for each grid cell between each
ambulance location and each grid cell centroid. In this way, “what if“ analysis can be used to
generate a number of alternative configurations for comparison and to provide a benchmark to
help plan service deployment.
A GIS-based DSS should also be able to manage effectively the various scenarios and
alternative configurations that it produces using “what if” analysis as well as current and
historical data. It is important to have the ability to save scenarios for future analysis and
comparison in order to enumerate the relative costs and benefits for each alternative
configuration. A scenario-based approach can be used in such a system to store the results of
multiple analyses in a single scenario file with meta-data (data describing data) describing the
scenario parameters. In this way, planners can open and regenerate previously saved scenarios
containing multiple analyses for comparison. Such a scenario-based approach can form the basis
of a more comprehensive ambulance service GIS-based DSS. This type of system can have

27
substantial benefits for managing and analysing data to produce information relevant to decision
making and in simulating the effects of different planning decisions.
5. References
Badillo, A.S. (1993). Transportation and Navigation. In G.H. Castle III (ed.) Profiting from a
Geographic Information System. : GIS World, Inc., Fort Collins, 161-176.
Barry, D. (1991). “Fleet Management Makes Advances with Digital Mapping Technology”, GIS
World, 4(7): 74-77.
Birkin, M., Clarke, G., Clarke, M. and Wilson, A. (1996). Intelligent GIS: Location decisions and
strategic planning. New York: John Wiley & Sons Inc.
Bridgehouse, B. (1993). Emergency Health Services: A GIS Application. Proceedings of the 5th
Canadian Conference on GIS, Ottawa, March 23-25, 611- 622.
Feick, Robert D. (1991). The Design of an Exploratory Model for Planning School Facility
Systems. A thesis presented to the University of Waterloo in fulfilment of the thesis requirement
for the degree of Master of Arts in Geography.
Gamble-Risley, Michelle. (1997). “Emergency Tools Provide Disaster Relief”, Government
Technology, 10(8): 64-68.
GIS Newslink. (1993). “Emergency Mapping Response Service Implemented (Dauphin County,
PA)”, GIS World, 6(7): 11.
GIS Newslink. (1994). “EIS Implements Real-Time Emergency Tracking”, GIS World, 7(12):13.
Hall, G. Brent and Robert D. Feick (1997). “Consensus-building in a Multi-Participant Spatial
Decision Support System”. Waterloo: University of Waterloo, Faculty of Environmental Studies.
Joseph A.E. and D.R. Philips. (1984). Accessibility and Utilisation: Geographical Perspectives
on Health Care Delivery. New York: Harper & Row.
Masser, I. and M. Blakemore, eds. (1991). Handling Geographical Information: Methodology
and Potential Applications. New York: John Wiley & Sons, Inc.
Medyckyj-Scott, D. and H.M. Hearnshaw, eds. (1993). Human Factors in Geographical
Information Systems. London: Belhaven Press.
Openshaw, Stan. (1991). Developing Appropriate Spatial Analysis Methods for GIS. in Maguire,
D.J., Goodchild, M.F., Rhind, D.W., (eds) Geographical Information Systems: principles and
applications, Longman, London, pp. 389 – 402, Vol. 1.
Super Solution, (1997). “Mapping Ambulance Priority”, MapWorld Magazine, 2(1): 16.
Ward, A. (1994). “Saving lives in the West Country - using GIS to improve ambulance response
times”, Mapping Awareness, April: 36-37.

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