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  • 1. Article Title PageBenchmarking the location of health centers at Jeddah city: A GIS approachAuthor DetailsAuthor 1 Name: Abdulkader Ali MuradDepartment: Department of Urban & Regional PlanningUniversity/Institution: King Abdulaziz UniversityTown/City: JeddahCountry: Saudi ArabiaCorresponding author: Abdulkader Ali MuradCorresponding Author’s Email: Gis_planning@yahoo.comAcknowledgments (if applicable): n/aBiographical Details (if applicable): n/aStructured Abstract: Purpose - The purpose of this paper is to discuss a GIS application created for health care planning atJeddah city, Saudi Arabia. The application covers important health care facilities planning issues including defining accessibility tohealth care facilities, identifying and classifying the distribution of health demand at Jeddah city and modeling spatial variation ofpatient locations.Design/methodology/approach - In order to build this application a geo-database is created that covers points, lines and polygonfeatures such as health care facility location, road network and population districts. In addition, raster surface models are producedusing 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 carefacilities and see if these locations are concentrated at certain city districts. In addition, local health planners can use the createdmodels 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 beused 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, JeddahArticle Classification: Research paperFor internal production use onlyRunning Heads:
  • 2. Benchmarking the location of health centers at Jeddah city: A GIS approachAbstractPurpose- The purpose of this paper is to discuss a GIS application created for healthcare planning at Jeddah city, Saudi Arabia. The application covers important healthcare facilities planning issues including defining accessibility to health care facilities,identifying and classifying the distribution of health demand at Jeddah city andmodeling spatial variation of patient locations.Design/methodology/approach- In order to build this application a geo-database iscreated that covers points, lines and polygon features such as health care facilitylocation, road network and population districts. In addition, raster surface models areproduced using Kriging function which produces raster surfaces for predicting healthdemand values at the study area.Findings- The outputs of this application can be used to help health care planners inevaluating the existing location of health care facilities and see if these locations areconcentrated at certain city districts. In addition, local health planners can use thecreated models in deciding on where to allocate new health care facility at Jeddahcity.Originality/value- This application is considered as a spatial decision support systemfor health planners in Jeddah city. It can be used to define and evaluate location ofhealth 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. IntroductionHealth care facilities at any region can be divided into two main types that are knownas primary health centers and hospitals. The former provides basic health careservices and the latter provides services for specialist health treatment. Healthauthorities have always aimed to provide health care for all residents using a fairaccess policy that is characterized as providing the right service at the right time in theright place (Murad, 2006). To ensure adequate health care planning, health serviceplanners and policy makers need accurate and reliable measures of health facilities sothat true services shortage areas can be accurately identified and resources allocated tothose needy areas to alleviate the problem. Health care planning in a given location isinfluenced by many factors, including the availability of health services in the area(supply), the number of people living in that location (demand), the population’shealth status, the socio-economic and financial resources available to the population,people’s knowledge about health and the health care system, and geographicalimpedance between population and health services (YI QI, 2009). Among the manyfactors that influence health care services, two of them are critical: physician supplyand population demand. Both of these are spatially distributed, but it is rare that theirdistributions perfectly match (Luo, 2004).One of the main issues that health planners need to cover at any built up area isrelated to evaluating health accessibility. Measures of geographical accessibility havealso been proposed and critiqued in the planning and medical geography literature(Guagliardo, 2004). Such measures range from the conceptually simple counting ofthe number of facilities within a specified distance from a given location to moresophisticated spatial interaction models. These measures can be implemented usingGeographical Information Systems (GIS). GIS plays an essential role in helping 2
  • 4. public health organizations understand population health and make decisions. Withthe powerful tools and solutions that GIS technology brings to the desktop, healthplanners can improve understanding of community health needs and design effectiveinterventions. GIS technology offers varied solutions including ones that improvefield data collection and reporting and others that support disease surveillance andanalysis with online mapping and spatial statistics. In addition, GIS, improve theability to communicate with several health situation, such as environmentalcontamination, to decision makers.Geographical Information Systems (GIS) can be used for several health studies.Examples of these studies include examining disease rates, examining variations inhealth and the use of health services. Wilkinson et al. (1998) addresses the potentialapplications of GIS in health geographical studies. These applications are: diseasemapping and geographical correlation studies, patterns of health service use andaccess, environmental hazards and disease clusters, and the modeling of the healthimpacts of environmental hazards. Jacquez, 1998, added that GIS could be used forexposure assessment, identification of study populations, disease mapping, and publichealth surveillance. There are several examples in the literature that discuss thepotential GIS application in health care facility planning. For example, GIS is used inUsed for monitoring vector borne disease, water borne diseases, environmental health,modeling exposure to electromagnetic fields, quantifying lead hazards in aneighborhood, predicting child pedestrian injuries and for the analysis of diseasepolicy and planning (Coggon et al., 1997).Rytkonen et al. (2003), discussed an interesting GIS application for analysing theincidence of type 1 diabetes among children in Finland. They observed the incidenceof 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 basedhealth study, investigating the relationship between environmental factors and theneed for the treatment of lung problems. It is considered an interesting case,demonstrating how the data mining of GIS, combined with healthcare outcomes, canbe effective in modifying clinical research. One example of using GIS and GPS inhealth care is found in Gesler et al. (2004), where these technologies are used to mapout residence activity spaces, using symbols and standard deviational ellipses andsites where diabetes information has the potential to be welcomed, for a sample oflow 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 121participants and demonstrates how this approach can be used by healthcare providersand researchers to implement a community-based diabetes prevention programme.2. GIS and health care applications: BackgroundOne of the basic objectives of healthcare Planning in any part of the world is to havean equivalent access to health care for all, regardless of ability to pay. This means thatevery residence should have equivalent chance to go to clinics and hospitals. To meetthis objective and other ones, health authorities are required to make careful analysisabout the real demands and supplies of health care facilities at their areas. Theseanalysis and studies can be classified into three main groups, which are a) spatialchanges in health status, b) spatial epidemiology, and c) health care facilitiesaccessibility and utilization. Each one of these topics has a spatial dimension, whichmeans that GIS can be used for their studies. The next part will elaborate more oneach 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 thespace. Health authorities always investigate and analyze the health status at their areasand make sure that health needs are satisfied. Locality definition is considered as animportant issue for health care facilities planning studies. The idea here is todetermine the socio-economic classifications for the area surrounding certain healthfacilities 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 tomap and tabulate the distributions of such status. A good example of using GIS forlinking social profiles with health needs is found by Hirschfield et al., 1995, whichhave produced patient profiles for a health facility catchment area. Such studiesusually involve matching point-referenced, post coded health data with areasocioeconomic data, particularly deprivation indicators (Gatrell and Senior 1999). Forexample, Health status can be viewed through comparing the actual number ofmoralities in an area with the national average, taking into account age and sexvariations in the area concerned (Birkin et al, 1996). Here GIS can be usedsuccessfully 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 andanalysis tools of GIS can be applied to present out the regions that have high rates ofmortality. The regions with high rates require more attention from health authoritiesin order to improve their existing health status. In addition to mortality, there aremuch other health status indicators that are used by health authorities, such as fertilityrates, 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 areseveral questions that are commonly asked in spatial epidemiology studies, whichinclude: where are the incidences located? what are the environmental characteristicsof these areas?, what are relations between health incidences and the environment atother locations?, and what patterns are evolving? (Nicol, 1991). GIS is considered as auseful tool for answering the preceding questions. For example, GIS can define theactual location of health events, then overlay analysis can be used to create newspatial relationships and to tag the various socio-economic and environmentalinformation to the health data.There are several studies that have applied GIS to these issues. For instant Brown etal, 1991, have used GIS for the mapping of spatial variations in health care provisionin Merseyside, UK. Wrigley, 1991, have also used GIS in mapping incidence diseasesin relation to population types. Another example of GIS applications in epidemiologyis 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 toresearch relationships between health and the environment, to aid policies andmanagement (ibid). Most of spatial epidemiology studies must be based upon accurateknowledge of the population. Therefore, access to details of population compositionand socio-economic characteristics are very necessary for these studies.Spatial epidemiology studies are concerned with finding good description of spatialincidence of diseases as well as the modeling of such incidence. One way ofdescribing the spatial distribution of a certain disease is by visualizing the GISchoropleth 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 affectedfrom the related disease. Further analysis and modeling of the spatial incidence ofdiseases can be carried out using for example Kernal or density estimation techniquethat is used in predicting the spatial variation in diseases risk (Gatrell and Senior1999).Any health care study requires a huge set of data which need to be handled andcaptured into GIS software. Once this step is completed, then GIS users andresearchers will move to the following step of the application, the data exploratorystep. Gatrell and Senior (1999) defined the GIS data exploratory step as the phasewhich 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 beenexpanded. This type of function can be applied on point, polygon or line features. Forexample, focal and local GIS functions are used in modern electronic atlases ofmortality and morbidity to highlight areas where disease rates are unusually high orlow. ArcGIS software has included these functions within its spatial analysisextension which produces a raster output in which the value at each location is afunction of the input cells in some specified neighborhood of the location (McCoyand Johnston, 2001). In addition, ArcGIS software has a very useful extension knownas the Geostatistical Analyst, which can be used for modeling any health point baseddata, such as the location of patients, and can easily create a continuous surface frommeasured sample points stored in a point-feature layer. It derives a surface using thevalues from the measured locations to predict values for each location in thelandscape (Johnston, et al., 2001). One of these functions is called Kriging (which isused 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 ofproducing a prediction surface and providing some measures of the certainty oraccuracy of a prediction. Health studies that look into the relationship between airpollution and health status can use this function to define air quality, based on samplemeasured points. The presented study has used Kriging function for the purpose ofmdeling health demand flows at Jeddah city. The results of this function is discussedat the next section.3. Application of GIS for health care planningThe aim of this section is to discuss how GIS can be used to analyze the location ofhealth care centers in Jeddah, Saudi Arabia. The application covers important healthcare planning issues which are : a- defining the level of accessibility to health carecenters, b- identifying the spatial distribution of health care demand, and c- modelingthe distribution of health care demand using GIS Kriging function. The first step increating 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-baseOne of the main tasks that should be looked at carefully during the building of anyGIS application, is regarding creating the required data and then integrating these datawithin the GIS application. These data fall into three main GIS data features known aspoints, lines and polygons. Point data are restored as a single x,y coordinate, withattributes describing the conditions of these points. Usually geographical features thatare too small to be depicted as lines or areas, are created in GIS as points data. Forthis application, the location of health centers in Jeddah city is created as a pointfeature, and all attribute data about health centers which include number physiciansand 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 shapethat represents geographical features too narrow to depict as area (Zeiler 1999). GISsoftware stores lines as a series of ordered x,y coordinates, with the relevantattributes. For the presented application, the road network of Jeddah city isrepresented as a line feature, with attributes regarding the length and type of each roadin this city. The third GIS data feature is known as polygon date set. This type offeatures is modelled in GIS as a series of segments that enclose an area and form a setof closed area (Zeiler, 1999). City districts coverage is an example of this type of GISdata that is created for the presented study. This coverage includes attributes such asdistrict name and area, and size of population and households for each district(fig.3) .3.1 Accessibility to Health supplyThe literature on accessibility measures showed a need for quantitative indicators ofaccessibility for different kinds of public services including health care (Murad,2007). Such indicators would serve as instruments in the comparisons of accessibilityin different parts of the region and in the evaluation of alternative plans for newservice facilities and transportation links. Examples of accessibility indicators are :provider-to-population ratio, distance to the nearest provider, average distance to a setof 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 producesaccessibility indicators to health centers in Jeddah city. One way of definingaccessibility to health centers is by knowing how far patients live from their nearestcenters. Based on local standards, every health center should cover a catchment areaextending 2 KM radius wide. In order to define the level of accessibly to healthcenters, 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 locationand city districts. Based on this output, several parts of the city are located at areaswith more than 2 KM accessibility zone. These areas are mainly situated north andeast 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 partsof Jeddah city that are not located within the 2 km accessibility zones. These aremainly north and east of the city as well as some of the western parts of the city. It isalso clear from this figure that existing health centers are serving larger catchmentarea than the standard size. Based on this output, there are different parts of Jeddahcity that are having low health accessibility service. These parts includeAlMohammadia district located north of Jeddah, and Alhamra district at the west ofthe city.Health planners can use this model to help them in deciding about where to build anew health center in Jeddah city. For example, the areas that are located outside the2Km accessibility zones can be used as a guide for allocating any new additionalhealth centers in Jeddah city. Health planners and officers can present this model tothe regional or national health authorities for asking about building new health centersat several city parts.3.2 Identifying Health demand distributionOne of the main issues that is covered is this application is related to using ArcGISsoftware for describing the spatial distribution of health demand data. However,before covering this issue it was important to decide on about the suitable spatialresolution (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 studyarea the scale or geographic extent of the study?” and “what is the appropriate unit ofanalysis the spatial resolution?”. In many cases, the answers to these questions aredetermined 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 isone type of health demand is aggregated to the level of health center location. Thereare 39 health centers distributed at Jeddah city. Every one of those centers has recordsabout the size of registered diabetic patients. These records can be used in GIS todefine the spatial pattern of diabetic patients in Jeddah city. The results of these dataare 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 identifyrelationships between diabetic data and other related data.The first step that was made at this part of the study, was to create a point coverageshowing location of all health centers at Jeddah city, and then links diabetic data tothis 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 atAl-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 applicationto make a spatial comparison (Based on mean value) between health centers to findout centers that are having large amount of diabetes patients. The fact that healthstatus varies a cross space in widely known and applies at all spatial scale across theurban and regional hierarchy. For example standard mortality ratios (SMRs) arecalculated by comparing the actual number of mortalities in an area with the nationalaverage taking into account age and sex variations in the area concerned. If an areawas 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 standarddiabetic rates (SDRs) for Jeddah city. Fig 6 shows SDR values and indicates that theareas with the highest rates trend to be in the central and southern parts of the city. Inaddition, two main areas (Al Rabwa and Al Bawadi) located north of Jeddah city arealso having high SDR rates.3.3 Modeling spatial variation of patient locationsThe literature of health analysis field indicates that there are several approaches andmodels that can be used to model variation in health data. For example, Bayesianmodel-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 ofemerging neoplasm in Spain. Ying and Weimin, 2006, have also used Bayesianmodeling technique in assessing spatial variations of hospitalized children and youthin the province of British Columbia. In addition to Bayesian models, Collins, 1998added that regression models could be used in a combination with GIS to modelhealth and environmental data for Huddersfield area at the UK.Geographical information systems are developed today to include several usefulmodels that can be used for defining spatial variations of health data. One of thesemodels is known as Kriging models, which are also known as geastatistical models,and considered as optimal interpolators that produce estimates which are unbiased andhave known minimum variance. This technique is based upon the theory ofregionalized variables and utilizes the spatial structure of the data and involves theconstruction of a variogram and the fitting of an appropriate model (ibid). Thepresented study has selected this technique to model the spatial variation of diabeticdisease at Jeddah city. Geostatistical methods are based on statistical models thatinclude auto correlation (statistical relationships among the measured points), andhave the capability of producing the diction surface and provide some measure of thecertainty or accuracy of the predictions. Kriging weights the surrounding measuredvalues to derive a prediction for an unmeasured location. The general formula for thistechnique is as followingZ(s) = M + ε (s)Where 13
  • 15. Z(s) is the predicted value at the s location,M is a known constant mean, andε (s) is the random errors processKriging is used at the presented study within ArcGIS geostatistical analyst extensionwhich has advanced tool bar containing tools for exploratory spatial data analysis anda 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 selectedsimple kriging which is based on a known constant mean. In the case of diabetic dataat Jeddah city 260 is the mean value for the collected data. Fig. 7 shows the output ofthis model and it defines how diabetes patients are spread out at all city parts but withdifferent amounts. It also shows how the highest concentrations of diabetes which arefound at Al-Rabwah, Bani Malik, Al-Jamiah, and Al-Rowais, are also spread at theareas closer to them. For example, an area called Al-Syliamaniah is have a remarkablediabetes 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 indicatesthat spatially distributed objects are spatially correlated, in other words, things that areclose together tend to have similar characteristics. Therefore, all areas located nearthese four districts are getting higher values of diabetes values. Meanwhile, thenorthern areas which are close to Obhur are getting lower diabetes values becauseObhur 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 aremore likely to have more diabetic patients. These parts can be reached for healthprotection purposes and for the management of patients living these parts.4. ConclusionUsing GIS for health care planning is considered as one of the important and usefulGIS applications. Health planners can use this technology to evaluate the location ofhealth services in any built up area. In order to use this technology several types ofspatial data should be collected by health planners. The presented paper has collectedpoints, lines, and polygons data for the purpose of evaluating the location of healthsupply and demand in Jeddah city. This paper has demonstrated that GIS can be usedto identify the level of accessibility to health care facilities. The results of accessibilityanalysis show that existing health centers are serving larger catchment area than thestandard size. Based on this output, there are different parts of Jeddah city that arehaving low health accessibility service. In addition, the created application hadexplored the patterns of health demand and predicted the spatial variation of patientsat Jeddah city. The outputs of this application indicate that diabetes patients arelocated mainly at Al-Rabwah, Bani Malik, Al-Jamiah, and Al-Rowais at Jeddah city.5. ReferencesAndes, N. and Davis, J. ,1995, Linking public health data using geographicalinformation system techniques: Alaskan community characteristics and infantmortality. Stat. in Med. 42(6), 481 – 90.Birkin, M., Clarke, G., Clarke, M. and Wilson, A. ,1996, Intelligent GIS: Locationdecisions and strategic planning. Cambridge: Geo information. 15
  • 17. Braga, M., Cislaghi, C., Luppi, G. and Tasco, C. ,1998, A multipurpose, interactivemortality atlas of Italy. In GIS and health (A. Gatrell and M. Loytonen, eds), pp. 125– 39. London: Taylor and Francis.Brown, P., Hirschfield, A. and Batey, P. ,1991, Applications of geodemographicmethods in the analysis of health condition incidence data. Papers in Reg. Sci. 70, 39– 44.Cerrito, P., Atnes, G. and Foibes. R. ,2003, The analysis of asthma and exposure datausing geographic information systems and data mining information, SIAMInternational Conference on Data Mining.Coggon, D., Geoffrey, R. and Barker, D ,1997, Epidemiology for the Uninitiated,Southampton: BMJ Publishing Group.Collins, S., Smallbone, K. and Briggs, D. ,1995, A GIS approach to modeling smallarea variationions in air pollution within a complex urban environment. InInnovations in GIS2 (P. Fisher, ed), pp. 125 – 39. London: Taylor and Francis.Chou, Y. ,1997, Exploring spatial analysis in geographic information systems. SantaFe: Onward Press.ESRI ,2001, Getting to know Arc GIS desktop. Redlands: ESRI.ESRI ,2000, GIS in health. Redlands: ESRI.ESRI ,1997, Getting to know Arcview GIS. Cambridge: Geo information.ESRI ,1992, Network analysis. Redlands: ESRI.Gatrell, A. and Loytonen, M. ,1998, GIS and health research an introduction, in: A.Gathell and M. Loytonen (Eds) GIS and Health, London: Taylor and Francis.Gatrell, A. and Senior, M. ,1999, Health and health care applications, in: P. Longley,Goodchild, M., Maguire, D., and Rhind D. (Eds) Geographical Information Systems,New York: John Wiley and Sons, pp.925–938. 16
  • 18. Guagliardo M, Ronico C, Cheung I, Chacko E, Josef J, 2004. Physician’saccessibility: an urban case of pediatric providers. Health Place 10, 273-283.Hirschfield A, Brown P; and Bundred P; 1995, the spatial analysis of communityhealth services on wirral using Geographical Information System, Journal of theInternational research society, vol. 46, pp. 147-59.Jacquez, G. ,1998, GIS as an enabling technology, in: A. Gathell and M. Loytonen(Eds) GIS and Health, London: Taylor and Francis.Jordan, H, Roderick, P, Martin, D, and Barnett, S, 2004, Distance, rurality, and theneed for care: access to health services in South West England, Int. J. HealthGeograph. 3 (21) (2004) 1–9.Jones, A. and Bentham, G. ,1995, Emergency medical service accessibility andoutcome from road traffic accidents. Pub. Heal. 109, 169 – 77.Lupien, A.,Moreland, W. and Dangermond, J. ,1987, Network analysis is geographicinformation system. Phot. Gramm. Eng. and Rem. Sens. 53(10), 1417 – 21.Murad, A, 2006, Creating a GIS application for health services at Jeddah city,Computers in Biology & Medicine, 37, 879-889.Murad A, 2007. A GIS application for modeling accessibility to health care centers inJeddah city. In: GIS for Health and the Environment: Development in the Asia PacificRegion. C Lai, A Mak (eds). Springer, Berlin, Germany, pp. 57-70.Murad, A, 2008, Creating a GIS-based epidemiological application for Jeddah city,Int. J. of Health care Technology and Management, Vol.9, No.5/6, 540-551.Nicol J; 1991, Geographic Information System within the national Healths, the scopefor implementations, planning out look, vol. 34, No. 1, pp. 37-42.Roovali, L, and Kiivet, R, 2006,Geographical variations in hospital use in Estonia,Health & Place 12 (2), 195–202. 17
  • 19. Rytkonen, M., Moltchanova, E., Ranta, J., Taskinen, O., Tuomilehto, J. andKarbonen, M. ,2003, The incidence of type 1 diabetes among children in Finland –rural-urban difference, Health & Place, Vol. 9, No. 4, pp.315–325.Wilkinson P, Grundy C, Landon M and Stevenson S, 1998. GIS in Public Health. In:GIS and Health. A Gatrell, M Loytonen (eds). Taylor & Francis, London, UK, pp.179- 189.Zeiler, M. ,1999, Modeling our World: The ESRI Guide to Geodatabase Design,Redlands: ESRI. 18
  • 20. Y # Y # Y # Y # Y #Y # Y # # Y Y # Y # # Y Y # Y # Y # Y # N Y # Y # Red Sea Y #W E S YY ## Y # Y # Y # Y #No. of Physicians # 1- 2 Y Y # Y # Y # # Y # Y Y # 3- 4 Y # # Y Y # # Y 5- 6 Y # Y #Y# 7- 8 Road 10000 0 10000 Meters . Fig. 1 Classification of health centers based on number of Physicians 19
  • 21. ³ # ³ ³ ³ ## # ³ # # ³ # ³ ³ # # ³ # ³ # ³ ³ # ³ # ³# # ³ # N ³ ³ # Red SeaW E ³ # S ³³ ## ³ # # ³ ³ ³ # #No. of Dentists # # ³ ³ ## ³ ³³ # ³ # # ³ ³ # 0 ³ # # ³ 1³# 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 S10000 0 10000 20000 metersFig. 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 S10000 0 10000 20000 metersFig. 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 S20000 0 20000 meters Fig. 7 Predicted diabetes patients spread at Jeddah city 25