1. Article Title Page
Benchmarking the location of health centers at Jeddah city: A GIS approach
Author Details
Author 1 Name: Abdulkader Ali Murad
Department: Department of Urban & Regional Planning
University/Institution: King Abdulaziz University
Town/City: Jeddah
Country: Saudi Arabia
Corresponding author: Abdulkader Ali Murad
Corresponding Author’s Email: Gis_planning@yahoo.com
Acknowledgments (if applicable): n/a
Biographical Details (if applicable): n/a
Structured Abstract: Purpose - The purpose of this paper is to discuss a GIS application created for health care planning at
Jeddah city, Saudi Arabia. The application covers important health care facilities planning issues including defining accessibility to
health care facilities, identifying and classifying the distribution of health demand at Jeddah city and modeling spatial variation of
patient locations.
Design/methodology/approach - In order to build this application a geo-database is created that covers points, lines and polygon
features such as health care facility location, road network and population districts. In addition, raster surface models are produced
using Kriging function which produces raster surfaces for predicting health demand values at the study area.
Findings - The outputs of this application can be used to help health care planners in evaluating the existing location of health care
facilities and see if these locations are concentrated at certain city districts. In addition, local health planners can use the created
models in deciding on where to allocate new health care facility at Jeddah city.
Originality/value - This application is considered as a spatial decision support system for health planners in Jeddah city. It can be
used to define and evaluate location of health centers as well as to identify the spatial accessibility to health centers.
Keywords: GIS, Health care planning, Heath demand, Health center, Jeddah
Article Classification: Research paper
For internal production use only
Running Heads:

2. Benchmarking the location of health centers at Jeddah city: A GIS
approach
Abstract
Purpose- The purpose of this paper is to discuss a GIS application created for health
care planning at Jeddah city, Saudi Arabia. The application covers important health
care facilities planning issues including defining accessibility to health care facilities,
identifying and classifying the distribution of health demand at Jeddah city and
modeling spatial variation of patient locations.
Design/methodology/approach- In order to build this application a geo-database is
created that covers points, lines and polygon features such as health care facility
location, road network and population districts. In addition, raster surface models are
produced using Kriging function which produces raster surfaces for predicting health
demand values at the study area.
Findings- The outputs of this application can be used to help health care planners in
evaluating the existing location of health care facilities and see if these locations are
concentrated at certain city districts. In addition, local health planners can use the
created models in deciding on where to allocate new health care facility at Jeddah
city.
Originality/value- This application is considered as a spatial decision support system
for health planners in Jeddah city. It can be used to define and evaluate location of
health centers as well as to identify the spatial accessibility to health centers.
Key Words: GIS, Health care planning, Heath demand, Health center, Jeddah.
1

3. 1. Introduction
Health care facilities at any region can be divided into two main types that are known
as primary health centers and hospitals. The former provides basic health care
services and the latter provides services for specialist health treatment. Health
authorities have always aimed to provide health care for all residents using a fair
access policy that is characterized as providing the right service at the right time in the
right place (Murad, 2006). To ensure adequate health care planning, health service
planners and policy makers need accurate and reliable measures of health facilities so
that true services shortage areas can be accurately identified and resources allocated to
those needy areas to alleviate the problem. Health care planning in a given location is
influenced by many factors, including the availability of health services in the area
(supply), the number of people living in that location (demand), the population’s
health status, the socio-economic and financial resources available to the population,
people’s knowledge about health and the health care system, and geographical
impedance between population and health services (YI QI, 2009). Among the many
factors that influence health care services, two of them are critical: physician supply
and population demand. Both of these are spatially distributed, but it is rare that their
distributions perfectly match (Luo, 2004).
One of the main issues that health planners need to cover at any built up area is
related to evaluating health accessibility. Measures of geographical accessibility have
also been proposed and critiqued in the planning and medical geography literature
(Guagliardo, 2004). Such measures range from the conceptually simple counting of
the number of facilities within a specified distance from a given location to more
sophisticated spatial interaction models. These measures can be implemented using
Geographical Information Systems (GIS). GIS plays an essential role in helping
2

4. public health organizations understand population health and make decisions. With
the powerful tools and solutions that GIS technology brings to the desktop, health
planners can improve understanding of community health needs and design effective
interventions. GIS technology offers varied solutions including ones that improve
field data collection and reporting and others that support disease surveillance and
analysis with online mapping and spatial statistics. In addition, GIS, improve the
ability to communicate with several health situation, such as environmental
contamination, to decision makers.
Geographical Information Systems (GIS) can be used for several health studies.
Examples of these studies include examining disease rates, examining variations in
health and the use of health services. Wilkinson et al. (1998) addresses the potential
applications of GIS in health geographical studies. These applications are: disease
mapping and geographical correlation studies, patterns of health service use and
access, environmental hazards and disease clusters, and the modeling of the health
impacts of environmental hazards. Jacquez, 1998, added that GIS could be used for
exposure assessment, identification of study populations, disease mapping, and public
health surveillance. There are several examples in the literature that discuss the
potential GIS application in health care facility planning. For example, GIS is used in
Used for monitoring vector borne disease, water borne diseases, environmental health,
modeling exposure to electromagnetic fields, quantifying lead hazards in a
neighborhood, predicting child pedestrian injuries and for the analysis of disease
policy and planning (Coggon et al., 1997).
Rytkonen et al. (2003), discussed an interesting GIS application for analysing the
incidence of type 1 diabetes among children in Finland. They observed the incidence
of type 1 diabetes per 100,000 persons separately in urban areas, urban-adjacent rural
3

5. areas, rural health and remote areas. Cerrito et al. (2003) presented another GIS based
health study, investigating the relationship between environmental factors and the
need for the treatment of lung problems. It is considered an interesting case,
demonstrating how the data mining of GIS, combined with healthcare outcomes, can
be effective in modifying clinical research. One example of using GIS and GPS in
health care is found in Gesler et al. (2004), where these technologies are used to map
out residence activity spaces, using symbols and standard deviational ellipses and
sites where diabetes information has the potential to be welcomed, for a sample of
low income females and males. This example shows how ‘prevention of diabetes’
projects can use GPS and GIS tools to collect and record the activity spaces of 121
participants and demonstrates how this approach can be used by healthcare providers
and researchers to implement a community-based diabetes prevention programme.
2. GIS and health care applications: Background
One of the basic objectives of healthcare Planning in any part of the world is to have
an equivalent access to health care for all, regardless of ability to pay. This means that
every residence should have equivalent chance to go to clinics and hospitals. To meet
this objective and other ones, health authorities are required to make careful analysis
about the real demands and supplies of health care facilities at their areas. These
analysis and studies can be classified into three main groups, which are a) spatial
changes in health status, b) spatial epidemiology, and c) health care facilities
accessibility and utilization. Each one of these topics has a spatial dimension, which
means that GIS can be used for their studies. The next part will elaborate more on
each group and illustrate the possible uses of GIS on them.
2.1 Spatial Changes in Health Status
4

6. One of the facts about health status in a micro/macro scale is that it changes across the
space. Health authorities always investigate and analyze the health status at their areas
and make sure that health needs are satisfied. Locality definition is considered as an
important issue for health care facilities planning studies. The idea here is to
determine the socio-economic classifications for the area surrounding certain health
facilities and then relate the local profiles of such an area with the health care needs.
Once the socio-economic status of any location is defined, then GIS can be used to
map and tabulate the distributions of such status. A good example of using GIS for
linking social profiles with health needs is found by Hirschfield et al., 1995, which
have produced patient profiles for a health facility catchment area. Such studies
usually involve matching point-referenced, post coded health data with area
socioeconomic data, particularly deprivation indicators (Gatrell and Senior 1999). For
example, Health status can be viewed through comparing the actual number of
moralities in an area with the national average, taking into account age and sex
variations in the area concerned (Birkin et al, 1996). Here GIS can be used
successfully for describing spatial variations of mortality at parts of any country.
Once the mortality rate of each region is entered into the GIS, the mapping and
analysis tools of GIS can be applied to present out the regions that have high rates of
mortality. The regions with high rates require more attention from health authorities
in order to improve their existing health status. In addition to mortality, there are
much other health status indicators that are used by health authorities, such as fertility
rates, which help to assess and monitor the required health services.
2.2 Spatial Epidemiology
5

7. The second area of health care research is known as spatial epidemiology. There are
several questions that are commonly asked in spatial epidemiology studies, which
include: where are the incidences located? what are the environmental characteristics
of these areas?, what are relations between health incidences and the environment at
other locations?, and what patterns are evolving? (Nicol, 1991). GIS is considered as a
useful tool for answering the preceding questions. For example, GIS can define the
actual location of health events, then overlay analysis can be used to create new
spatial relationships and to tag the various socio-economic and environmental
information to the health data.
There are several studies that have applied GIS to these issues. For instant Brown et
al, 1991, have used GIS for the mapping of spatial variations in health care provision
in Merseyside, UK. Wrigley, 1991, have also used GIS in mapping incidence diseases
in relation to population types. Another example of GIS applications in epidemiology
is called the Health and Environment Geographical Information System (HEGIS),
which is being established in Europe by the world health organization (Nicol, 1991).
It involves the creation of European wide environment data set, and the aim is to
research relationships between health and the environment, to aid policies and
management (ibid). Most of spatial epidemiology studies must be based upon accurate
knowledge of the population. Therefore, access to details of population composition
and socio-economic characteristics are very necessary for these studies.
Spatial epidemiology studies are concerned with finding good description of spatial
incidence of diseases as well as the modeling of such incidence. One way of
describing the spatial distribution of a certain disease is by visualizing the GIS
choropleth maps that show the spatial distributions of such a disease. In such maps,
6

8. disease rates are plotted over the base map to define the areas that are highly affected
from the related disease. Further analysis and modeling of the spatial incidence of
diseases can be carried out using for example Kernal or density estimation technique
that is used in predicting the spatial variation in diseases risk (Gatrell and Senior
1999).
Any health care study requires a huge set of data which need to be handled and
captured into GIS software. Once this step is completed, then GIS users and
researchers will move to the following step of the application, the data exploratory
step. Gatrell and Senior (1999) defined the GIS data exploratory step as the phase
which goes beyond the map, to the use of statistical tools in an informal, pattern-
seeking vein. It is considered an area in which major research efforts have been
expanded. This type of function can be applied on point, polygon or line features. For
example, focal and local GIS functions are used in modern electronic atlases of
mortality and morbidity to highlight areas where disease rates are unusually high or
low. ArcGIS software has included these functions within its spatial analysis
extension which produces a raster output in which the value at each location is a
function of the input cells in some specified neighborhood of the location (McCoy
and Johnston, 2001). In addition, ArcGIS software has a very useful extension known
as the Geostatistical Analyst, which can be used for modeling any health point based
data, such as the location of patients, and can easily create a continuous surface from
measured sample points stored in a point-feature layer. It derives a surface using the
values from the measured locations to predict values for each location in the
landscape (Johnston, et al., 2001). One of these functions is called Kriging (which is
used by the presented application) that can be used for modeling health point data. It
7

9. is considered as one of the deterministic interpolation methods which capable of
producing a prediction surface and providing some measures of the certainty or
accuracy of a prediction. Health studies that look into the relationship between air
pollution and health status can use this function to define air quality, based on sample
measured points. The presented study has used Kriging function for the purpose of
mdeling health demand flows at Jeddah city. The results of this function is discussed
at the next section.
3. Application of GIS for health care planning
The aim of this section is to discuss how GIS can be used to analyze the location of
health care centers in Jeddah, Saudi Arabia. The application covers important health
care planning issues which are : a- defining the level of accessibility to health care
centers, b- identifying the spatial distribution of health care demand, and c- modeling
the distribution of health care demand using GIS Kriging function. The first step in
creating this application was to build the needed geo-database for health care centers.
The next section will discuss the process of building this geo-database.
3.1 The Data-base
One of the main tasks that should be looked at carefully during the building of any
GIS application, is regarding creating the required data and then integrating these data
within the GIS application. These data fall into three main GIS data features known as
points, lines and polygons. Point data are restored as a single x,y coordinate, with
attributes describing the conditions of these points. Usually geographical features that
are too small to be depicted as lines or areas, are created in GIS as points data. For
this application, the location of health centers in Jeddah city is created as a point
feature, and all attribute data about health centers which include number physicians
and number of dentist (fig 1 and 2) are saved in the attribute table of this file. The
8

10. second main GIS data feature is the line feature, which has a one dimensional shape
that represents geographical features too narrow to depict as area (Zeiler 1999). GIS
software stores lines as a series of ordered x,y coordinates, with the relevant
attributes. For the presented application, the road network of Jeddah city is
represented as a line feature, with attributes regarding the length and type of each road
in this city. The third GIS data feature is known as polygon date set. This type of
features is modelled in GIS as a series of segments that enclose an area and form a set
of closed area (Zeiler, 1999). City districts coverage is an example of this type of GIS
data that is created for the presented study. This coverage includes attributes such as
district name and area, and size of population and households for each district(fig.3) .
3.1 Accessibility to Health supply
The literature on accessibility measures showed a need for quantitative indicators of
accessibility for different kinds of public services including health care (Murad,
2007). Such indicators would serve as instruments in the comparisons of accessibility
in different parts of the region and in the evaluation of alternative plans for new
service facilities and transportation links. Examples of accessibility indicators are :
provider-to-population ratio, distance to the nearest provider, average distance to a set
of providers and gravitational models of provider influence (Guaglirado et al, 2003).
Each one of these indicators can be used to evaluate accessibility of health centers.
The presented application has selected distance to provider method and produces
accessibility indicators to health centers in Jeddah city. One way of defining
accessibility to health centers is by knowing how far patients live from their nearest
centers. Based on local standards, every health center should cover a catchment area
extending 2 KM radius wide. In order to define the level of accessibly to health
centers, GIS proximity analysis was used and the output of this model (figure 4)
9

11. classifies the city into deferent zones based on the distance between clinic location
and city districts. Based on this output, several parts of the city are located at areas
with more than 2 KM accessibility zone. These areas are mainly situated north and
east of the city with some to the west.
The results of this function are shown at Fig. 1 and it is clear that there is several parts
of Jeddah city that are not located within the 2 km accessibility zones. These are
mainly north and east of the city as well as some of the western parts of the city. It is
also clear from this figure that existing health centers are serving larger catchment
area than the standard size. Based on this output, there are different parts of Jeddah
city that are having low health accessibility service. These parts include
AlMohammadia district located north of Jeddah, and Alhamra district at the west of
the city.
Health planners can use this model to help them in deciding about where to build a
new health center in Jeddah city. For example, the areas that are located outside the
2Km accessibility zones can be used as a guide for allocating any new additional
health centers in Jeddah city. Health planners and officers can present this model to
the regional or national health authorities for asking about building new health centers
at several city parts.
3.2 Identifying Health demand distribution
One of the main issues that is covered is this application is related to using ArcGIS
software for describing the spatial distribution of health demand data. However,
before covering this issue it was important to decide on about the suitable spatial
resolution (unit of analysis) for the presented application. Among the first questions to
10

12. be answered when using GIS for health research are: “what is the appropriate study
area the scale or geographic extent of the study?” and “what is the appropriate unit of
analysis the spatial resolution?”. In many cases, the answers to these questions are
determined by:
- The availability of data for all possible geographic, the known or probable
geographical extent of the problem to be studied;
- The physical integration, transportation systems, cultural factors, and social
dynamics of the particular region;
- Existing political and jurisdictional boundaries;
- The geography of the existing health care infrastructure and service areas;
- The geographic interests of the project partners, collaborators, or funders;
- The funding sources and parameters; and many other considerations and
constraints unique to each project (Maantay, 2005).
Based on the data available for this application, diabetes patient information which is
one type of health demand is aggregated to the level of health center location. There
are 39 health centers distributed at Jeddah city. Every one of those centers has records
about the size of registered diabetic patients. These records can be used in GIS to
define the spatial pattern of diabetic patients in Jeddah city. The results of these data
are very useful for identifying pattern and location of diabetic disease in Jeddah city.
These data also can be related to other physical or environmental data to identify
relationships between diabetic data and other related data.
The first step that was made at this part of the study, was to create a point coverage
showing location of all health centers at Jeddah city, and then links diabetic data to
this coverage. Once this step is covered, the following task was to use GIS
11

13. classification methods for describing variations of diabetic patients in Jeddah city.
Fig. 5 shows the output of the spatial distribution of diabetic disease at Jeddah city.
The resulted distribution indicates that diabetes patients are concentrated mainly at
Al-Rabwah, Bani Malik and Al-Sabail districts. These areas are covering north,
central and southern city districts.
In addition to classifying diabetic patient data, GIS is used at the presented application
to make a spatial comparison (Based on mean value) between health centers to find
out centers that are having large amount of diabetes patients. The fact that health
status varies a cross space in widely known and applies at all spatial scale across the
urban and regional hierarchy. For example standard mortality ratios (SMRs) are
calculated by comparing the actual number of mortalities in an area with the national
average taking into account age and sex variations in the area concerned. If an area
was generating deaths at the national average, its SMR value would be exactly 100
(Birkin et al, 1998). The same principle is applied at this study to calculate standard
diabetic rates (SDRs) for Jeddah city. Fig 6 shows SDR values and indicates that the
areas with the highest rates trend to be in the central and southern parts of the city. In
addition, two main areas (Al Rabwa and Al Bawadi) located north of Jeddah city are
also having high SDR rates.
3.3 Modeling spatial variation of patient locations
The literature of health analysis field indicates that there are several approaches and
models that can be used to model variation in health data. For example, Bayesian
model-based approach is used at Finland to model variations in the incidence of
12

14. childhood type I diabetes between urban/rural municipalities (Rytkonen et al, 2003).
The same modeling technique also used by Lopez-Abente, 1998, for the analysis of
emerging neoplasm in Spain. Ying and Weimin, 2006, have also used Bayesian
modeling technique in assessing spatial variations of hospitalized children and youth
in the province of British Columbia. In addition to Bayesian models, Collins, 1998
added that regression models could be used in a combination with GIS to model
health and environmental data for Huddersfield area at the UK.
Geographical information systems are developed today to include several useful
models that can be used for defining spatial variations of health data. One of these
models is known as Kriging models, which are also known as geastatistical models,
and considered as optimal interpolators that produce estimates which are unbiased and
have known minimum variance. This technique is based upon the theory of
regionalized variables and utilizes the spatial structure of the data and involves the
construction of a variogram and the fitting of an appropriate model (ibid). The
presented study has selected this technique to model the spatial variation of diabetic
disease at Jeddah city. Geostatistical methods are based on statistical models that
include auto correlation (statistical relationships among the measured points), and
have the capability of producing the diction surface and provide some measure of the
certainty or accuracy of the predictions. Kriging weights the surrounding measured
values to derive a prediction for an unmeasured location. The general formula for this
technique is as following
Z(s) = M + ε (s)
Where
13

15. Z(s) is the predicted value at the s location,
M is a known constant mean, and
ε (s) is the random errors process
Kriging is used at the presented study within ArcGIS geostatistical analyst extension
which has advanced tool bar containing tools for exploratory spatial data analysis and
a geostatistical wizard for creating a statistically valid surface (Johnstan et al, 2001).
In this software extension, there are different kriging methods including ordinary,
simple, universal and probability Kriging. The presented application has selected
simple kriging which is based on a known constant mean. In the case of diabetic data
at Jeddah city 260 is the mean value for the collected data. Fig. 7 shows the output of
this model and it defines how diabetes patients are spread out at all city parts but with
different amounts. It also shows how the highest concentrations of diabetes which are
found at Al-Rabwah, Bani Malik, Al-Jamiah, and Al-Rowais, are also spread at the
areas closer to them. For example, an area called Al-Syliamaniah is have a remarkable
diabetes patients because it is very close to a higher diabetes location called Al-
Jamiah. In fact this result is based on the Kriging model assumption which indicates
that spatially distributed objects are spatially correlated, in other words, things that are
close together tend to have similar characteristics. Therefore, all areas located near
these four districts are getting higher values of diabetes values. Meanwhile, the
northern areas which are close to Obhur are getting lower diabetes values because
Obhur health center is having zero diabetes patients.
14

16. This model can be used by health planners at Jeddah city to define city parts that are
more likely to have more diabetic patients. These parts can be reached for health
protection purposes and for the management of patients living these parts.
4. Conclusion
Using GIS for health care planning is considered as one of the important and useful
GIS applications. Health planners can use this technology to evaluate the location of
health services in any built up area. In order to use this technology several types of
spatial data should be collected by health planners. The presented paper has collected
points, lines, and polygons data for the purpose of evaluating the location of health
supply and demand in Jeddah city. This paper has demonstrated that GIS can be used
to identify the level of accessibility to health care facilities. The results of accessibility
analysis show that existing health centers are serving larger catchment area than the
standard size. Based on this output, there are different parts of Jeddah city that are
having low health accessibility service. In addition, the created application had
explored the patterns of health demand and predicted the spatial variation of patients
at Jeddah city. The outputs of this application indicate that diabetes patients are
located mainly at Al-Rabwah, Bani Malik, Al-Jamiah, and Al-Rowais at Jeddah city.
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³ ³
#
Red Sea
W E ³
#
S ³³
## ³
#
#
³ ³ ³
# #
No. of Dentists
# #
³ ³
## ³
³³ # ³
# #
³
³
# 0 ³
#
#
³ 1
³
# 2
³
#
³
#
Road
10000 0 10000 Meters
Fig. 2 Classification of health centers based on number of Dentists
20

22. Fig. 3. The population distribution in Jeddah city districts
21

23. #
# #
# #
#
# #
#
#
#
#
#
#
Red Sea #
#
#
#
# # # #
# #
# # #
# #
#
#
#
#
#
# Health centers
N
Road
Priximity zones W E
0 - 2000
2000 - 4000 S
4000 - 6000
6000 - 8000
10000 0 10000 20000 Meters
Fig. 4 Accessibility to health care facilities in Jeddah city
22

24. #
Al-Rabwah
# #
#
##
##
# # Bani Malik
#
Red Sea #
## #
# #
# #
#
# #
# # #
Al-Rowais # # # #
## #
#
#
#
Al-Sabail
Diabetes Patiants
# 0
# 1 - 189
# 190 - 290
# 291 - 745
# 746 - 2572
N
Road W E
City Districts S
10000 0 10000 20000 meters
Fig. 5 Spatial Distribution of Diabetes patients at Jeddah City
23

25. #
Al-Rabwah
# #
#
##
##
#
# Bani Malik
#
Red Sea #
#
## # #
# #
#
# # #
# #
Al-Rowais # # # #
## #
#
#
#
Al-Sabail
SDR
# 0
# 0 - 58.9
# 58.9 - 90.3
# 90.3 - 232.1
# 232.1 - 801.2
N
Road W E
City Districts S
10000 0 10000 20000 meters
Fig. 6 Standards Diabetes Rates (SDRs) at Jeddah City
24

26. Al-Rabwah
Bani Malik
Red Sea
Al-Rowais Al-Jamah
Road
Predicted Diabetes
0.101 - 106.196
106.196 - 212.292
212.292 - 318.387
318.387 - 424.483 N
424.483 - 530.579
530.579 - 636.674 W E
636.674 - 742.77
No Data S
20000 0 20000 meters
Fig. 7 Predicted diabetes patients spread at Jeddah city
25