geographic information systems and medical geography: toward a new synergy

copy 2007 The AuthorJournal Compilation copy 2007 Blackwell Publishing Ltd

Geography Compass

13 (2007) 556ndash582 101111j1749-8198200700027x

Geographic Information Systems and Medical Geography Toward a New Synergy

Daniel Z

Sui

Department of Geography Texas AampM University

Abstract

The past decade has witnessed wide-ranging applications of geographic informationsystems (GIS) in public health The literature on GIS and medical geography haspredominantly focused on how GIS can be applied as analytical and visualizationtools to examine the geographic aspects of disease and health services While GISapplications in medical geography are important and have been growing rapidlythis article argues that advances in medical geography can also have significantimplications on the further development of GIScience This article presents acomprehensive review of the reciprocal interaction between GIS and medicalgeography and calls for a new synergy between both fields in future research and

education efforts

1 Introduction

Fundamental contributions to a better understanding of crucial issues inpublic health have been identified as enduring legacies of a geographicperspective (National Research Council [NRC] 1997) As we havelearned from human history that global trade often promotes global illnessthe on-going economic globalization has further increased the complexitiesand rapidity of health events at the global level (Cliff and Haggett 2003Mayer 2005 Shin 2004) The rapid development of geographic informationsystems (GIS) during the past two decades has broadened both geographersrsquoand health scientistsrsquo capability to study public health issues from spatialtemporal and social perspectives in ways unprecedented in previous eras(Clarke et al 1996 Kistemann et al 2002 Richards and Croner 1999ab Schroder 2006) Until recently the voluminous literature on GIS andmedical geography has predominantly focused on how GIS can be appliedas an analytical and visualization tool to examine geographic aspects ofboth disease and health services (Albert et al 2000 Cromley and McLafferty2002 Croner et al 1996) While GIS applications in medical geographyare important and have continued to grow in both breadth and depth inrecent years (Gong et al 2006 Maheswaran and Craglia 2004) this articleargues that the interactions between GIS and medical geography should

copy 2007 The Author

Geography Compass

13 (2007) 556ndash582 101111j1749-8198200700027xJournal Compilation copy 2007 Blackwell Publishing Ltd

GIS and medical geography 557

be reciprocal Advances in medical geography can also have significantimplications on the further development of GIS as science The goal ofthis paper is threefold

1 to present an overview of recent GIS applications in medical geography2 to examine the implications of recent advances of medical geography

for GIS and3 to stimulate further discussions on how GIS and medical geography

can be more synergistically integrated for their mutual enhancement

This article is organized into four sections After this brief introductionan overview of recent GIS applications is presented in section 1 Section2 discusses how advances in medical geography can be incorporated inthe future development of GIS Section 3 outlines how a new synergybetween GIS and medical geography can be sought at the methodogicalas well as ontological and epistemological levels The last section containsa summary and conclusion

Before we proceed two caveats are needed in order to avoid unnecessaryconfusions among some readers First there is a long and rich historyrelated to the mapping of diseases as Tom Koch (2005) demonstrated sovividly in his

Cartographies of Disease

In addition to the almost mythologizedstudy on the 1845 London cholera epidemics by John Snow there werenumerous studies on various disease during the last 300 years such as the1694 plague map of Bari Italy Finkersquos 1792 disease map of the world orthe 1798 yellow fever map of New York by Seaman The maps producedby Peterman (1852) and Haviland (1875) (cited in Pickle 2002) alsodeserve attention Although we have better methods for map productionand dissemination the conceptual issues we are struggling with today arestill strikingly similar to those contemplated by our predecessors Indeedwe still have a lot to learn from the rich history of medical mapping

Second this article adopts a broad inclusive definition for both lsquoGISrsquoand lsquomedical geographyrsquo In particular GIS refers to the science system andservices related to the handling of geospatial information across multiplespatial and temporal scales (Longley et al 2001) According to Gatrell (2002)medical geography is closely related to public health and epidemiology Itseeks an understanding of spatial epidemiology or incidence of diseaseMedical geography is also concerned with operations of the healthcaresystem in a spatial setting In this article lsquomedical geographyrsquo is usedloosely as a big-tent concept that includes all geographic aspects of healthand disease that have been studied by medical geographers epidemiologistsand public health researchers In this article I treat medical geography aspart of health geography In the literature however distinctions are oftenmade between medical and health geography with medical geographydominated by biomedical models favoring intensive quantitative methodswhereas health geography relies more on socio-ecological models andoften employs more extensive qualitative approaches such as participant

558 GIS and medical geography


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observation semistructured interview and recording detailed narratives(Gesler 2006 Mayer 2000)

2 GIS for Medical Geography

The development of GIS during the past two decades has contributed toresearch related to multiple aspects of medical geography For a bettergrasp of the literature this section first reviews GIS applications in medicalgeography from a functionaltechnical perspective and then from anapplication perspective related to specific diseases and healthcare services

21

the functionaltechnical perspective

Although a GIS generally integrates data encoding storage analysis andmapping capabilities from a functionaltechnical perspective GIS applicationstend to have slightly different emphases depending on each applicationcontext A glimpse of the massive literature on GIS applications in medicalgeography quickly reveals three major distinctive research directions ndash data-base development analysis and modeling and mapping and visualization

211 The Database ViewAn accurate georeferenced database linked to the appropriate healthdata is the foundation for any successful applications of GIS in medicalgeography Spatial data can be either discrete (as points lines or polygonsin a vector format) or continuous (as grids in a raster format) in a typicalGIS database Health-related attribute data are mostly ratio data ndashalthough they could also exist in nominal ordinal or interval scalesGovernment agencies from local to global levels are the primary gatherersand custodians of health data However private organizations haverecently also gotten involved in the health data business Table 1 summarizesmajor data sources from both public and private sources

Similar to most areas of GIS applications access to the most up-to-dateaccurate and complete georeferenced database is absolutely the first step

Table 1 Some major data sources for public health information

Source Web URL

World Health Organization httpwwwwhointThe US Centers for Disease Control and Prevention and the National Center for Health Statistics

httpwwwcdcgovnchs httpseercancergov

Public Health Partners httpphpartnersorghealth_statshtmlPublic Health Agency of Canada httpwwwphac-aspcgccaEnvironmental Systems Research Institute httpwwwesricom


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in the successful applications of GIS in public health The geocodingprocess can be very problematic in cancer research (Rushton et al 2006)Krieger et al (2001 2005) reported that the accuracy of geocoding frommost national health database ranges from 44 to 84 with a 95confidence level This high level of inaccuracy in geocoding could furtherpropagate and have significant impacts in the mapping and analysisFurthermore lumping or splitting spatial units can have major impacts onspatial analyses as demonstrated by Gregorio et al (2005 2006) in theirprostate cancer case studies Statistical methods have been developed toaddress the problems of changing census boundaries (Gotway and Young2002 Gregor and Ell 2005 Hay et al 2005) and to conduct boundaryanalysis ( Jacquez et al 2000) As Hao et al (2006) have shown in a recentcancer-mapping project alternative geographic boundaries like con-gressional districts can be used to identify disease patterns that are morerelevant to the public and decision makers To geographers these findingsare consistent with earlier results of the so-called modifiable areal unitproblem (MAUP) (Sui 1999) MAUP has two subsets of problems ndash thescaling and zoning issue Results of area-based analysis are highly contingentupon the scale used (the scaling issue) and the boundaries deployed at thesame scale (the zoning issue)

When sensitive health information is used how to balance accessibilityand privacyconfidentiality has been one of the major concerns whenusing geospatial databases in public health (Gordis and Gold 1980Romano-Critchley and Sommerville 1999) Legally speaking differentcountries have different rules regarding the use and release of individualhealth data (Gostin et al 1996) In the United States the confidentialityof publicly released health and socio-economic data is often mandated byboth state and federal laws (McLaughlin 2002 Olvingson et al 2003Quinn 1992) When linked with geospatial information health informationis even more revealing and poses major threat to personal privacy (Kwanand Schuurman 2004) An unlimited and unregulated access to georeferencedhealth information could contribute to an emerging Panopticon

1

(Cheekand Rudge 1994) Methods were developed for spatially aggregatingindividual records on-line (Cromley et al 2004) Armstrong et al (1999)developed a set of geographic masking techniques to preserve confiden-tiality Boulos et al (2005) developed an agent-based software to preservehealth data confidentiality Kwan et al (2004) further tested these maskingtechniques and their effectiveness These authors found not surprisinglythat there is a trade-off between preservation of confidentiality and theaccuracy of analyses

212 The Analytical ViewSpatial analytical functions were (and to a large extent still are) definingfunctionalities of GIS especially in the context of public health applications(Gatrell and Rigby 2003 Rushton 2003) They include but certainly are



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not limited to simple functions such as descriptive statistical functionsbuffers and overlays and geostatistical modeling (Berke 2004 Lawson2001 Waller and Gotway 2004)

Geographic information systems are more robust for exploratoryanalysis than confirmatory analysis (Bell 2002 Moore and Carpenter 1999Pickle 2002) Exploratory analysis starts from data and its goal is toinductively assess departure from randomness whereas the confirmatoryanalysis starts from hypotheses and its goal is to deductively test whetherempirical results are consistent with the hypotheses made Althoughanalytical functions in GIS have vastly improved during the past 10 yearsmany GIS applications in health often need to link GIS with other morepowerful analytical tools such as SaTScan SPSS SAS or WinBUG vialoose coupling

Spatial processes are typically characterized by two properties spatialdependency (the tendency is for stronger relationships between closerthan distant entities) and spatial heterogeneity (the intrinsic uniqueness ofevery location relatively to other locations within a system) Althoughrecent advances in epidemiology for estimating risk variations haveincreasingly paid attention to spatial heterogeneity most applications ofGIS-based spatial analysis addressed the spatial dependence of healthevents This is evidenced by the larger proportion of literature on theclustering of diseases ndash unusually health events at the aggregated levelConfirmation of clustering can not only shed light on the causes (etiology)by further examining other socio-economic or environmental factors butalso identify possible outbreaks for infectious diseases Commonly usedcluster methods include distanceadjacency-based (Moranrsquos I or otherlocal indicators of spatial association) moving windows (Kulldorff rsquosScanStatistic Besag and Newellrsquos R) and Kernel estimate techniques(Aamodt et al 2006 Kulldorff 1997 1999) Other clustering testing methodsinclude Cuzick and Edwardsrsquo k-nearest neighbors Tangorsquos maximizedexcess events test Bonetti and Paganorsquos nonparametric M-statisticSwartzrsquos entropy test and Whittermorersquos test (Sonesson and Bock 2003)

213 The MappingVisualization ViewOne of the most important goals of GIS in health sciences is to bettercommunicate health-related information to both the general public andexperts in the field The communication role of GIS has not been alwaysfulfilled by the powerful analytical functions of GIS To present the resultsof analysis or visualize health information map is still one of the mostimportant uses of GIS In many interesting ways GIS-based mapping andvisualization applications are the continuation of an old visual culture inmedical imaging (Cartwright 1995 Kevles 1998) But GIS have alsotransfigured medical mapping in many interesting ways (Koch and Denike2004) GIS have enabled health professionals to explore a variety of alter-native mapping and visualization tools including dasymetric maps (Eicher


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and Brewer 2001) cartograms (Sui and Holt forthcoming) and productionof atlases (Pickle and Mugiole 1999) Furthermore mapping has beenexpanded to multiple scales from the genetic (Hall 2003) to the globallevels (Mayer 2005)

However despite the increasing variety of maps being used in publichealth and disease analyses cartodiversity (refers to the different types ofmaps used in a particular application) in public health research is stillsurprisingly low I concur with Martinrsquos (2005) assessment that publichealth researchers have yet to take advantage of the new mapping techniquesmade available by recent advances in GIS mapping and visualizationtechnologies Three-fifths of the maps published in leading health journalsbetween 2000 and 2004 were choropleth maps (Martin 2005) notwith-standing the choropleth mapsrsquo tendencies to create misleading impressionsof phenomena being well known (Crampton 2003 Monmonier 1996)In addition a major limitation of choropleth maps in public healthapplications relates to the fact that geographic units ( like census blocks zipcodes or counties) are rarely defined in terms of the population (infectedor at-risk) under investigation Large sparsely populated areas tend tovisually dominate the whole map whereas small areas with high popula-tion density often receive less attention then they deserve This often leadsto cognitive misperceptions about the spatial distribution of a particulardisease Choropleth maps are less desirable for visualizing public healthdata because they force risk ndash a continuous value ndash to be displayed inarbitrarily defined discrete areas Furthermore for small geographic areasit is difficult to calculate reliable rates as areas with no cases are oftentreated as having zero values As a result choropleth maps often lead tosome of the most egregious visual distortions found in visualization

Disease spatial analysis maps often suffer from similar drawbacks Newadvances in creating cartograms can potentially alleviate the problemsbrought by choropleth maps (Dorling 1996 Gastner and Newman 2004Tobler 2004) and deserve more attention by GIS users (Sui 2006)Cartograms are maps in which the size of the geographic units such ascountries or statesprovinces appears in proportion to the population orto some other analogous property A cartogram can be contiguous non-contiguous (Figure 1) Cartographically speaking a cartogram can bedefined as a non-conventional map projection that consciously distortseither area or distance or both in order to reveal patterns not readilyapparent in a more traditional base map Cartograms have been referredas density equalization map projections or isodemographic maps in publichealth literature because densities are constant throughout a cartogram

Traditional thematic maps using a topographic map base often give afalse impression of the spatial patterns of human geography as in mostcases many area units have a large territory but very small populationwhereas some relatively small units often have large populations Notsurprisingly in some extreme cases most of the patterns are simply not



Geography Compass


visible at all Sui and Holtrsquos (forthcoming) recent study on the effectivenessof cartograms both analytically and cognitively has lent further supportfor its wider application in public health

22

the application perspective

As for applications related to substantive issues in medical geographyliterature can be subdivided following the two dominating traditions

Fig 1 Contiguous versus non-contiguous cartogramSource httpwwwncgiaucsbeduprojectsCartogram_Central

Fig 2 Homunculus a medical cartogram


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in medical geography (Mayer 1982) ndash either focusing on diseases or onhealth services But because areas of prevalent diseases often requireextensive and specific health services these two topics have to beintimately related

221 Disease and Disease EcologyGeographic information systems have been widely used in the geographicanalysis of diseases by geographers epidemiologists and public healthresearchers (Melnick 2002) Applications include analyzing the distributionof particular diseases (identification of hot spots) scrutinizing diseaseecologies (possible causal relationships between particular diseases andenvironmental factors) disease diffusion through time and multilevelanalyses to differentiate compositional and contextual factors influencingdiseases Macrolevel geographic analyses often help to suggest possiblecausal factors in pathogenesis even provide clues for biological mechanismof pathogenesis (Mayer 1983 1992)

As for specific types of diseases both infectious and chronic diseaseshave been covered by GIS Case studies related to infectious diseasesinclude SARS (Lai et al 2004) AIDS (Khalakdina et al 2003) cholera(Ali et al 2002) tuberculosis (Tanser and Wilkinson 1999) hepatitis C(Trooskin et al 2005) and schistomomiasis (Xu et al 2006 Yang et al2005) GIS have been applied at prevention surveillance and monitoringstages of infectious diseases And after 911 GIS have also been proposedas an integral part of the information infrastructure to fight bio-terrorismillustrated by the spread of anthrax and risks presented by biomedicalweapons (Cutter et al 2003) GIS has also been used to examine manytypes of non-infectious diseases including breast cancer (Selvin et al 1998)cervical cancer (Barry and Breen 2004) prostate cancer (Mather 2006)leukaemia (Badrinath et al 1999) and leprosy (World Health Organization2005a b) The US Centers for Disease Control and Prevention proposedGIS as one of the backbone technologies for the national comprehensivecancer control plan

In recent years weather-related diseases have also been extensivelystudied using GIS (Waring et al 2005) Respiratory health risks pediatricasthma and their relationship to socio-economic variables have beenreported in the literature (Guidry and Margolis 2005 Kimes et al 2004Maantay 2007) Furthermore GIS have not only been applied to examinehuman diseases but also to study plant and animal as well as zoonoticdiseases ranging from foot and mouth mad cow disease and the diffusionof West Nile Virus (BCCDC 2006 Ruiz 2002 Shuai et al 2006 Ward2005)

222 Planning Health Care and Health ServicesGeographic information systems have also demonstrated their great utilityin the allocation and planning of healthcare services and particularly in



Geography Compass


community healthcare needs assessment (Melnick 2002) By linkingGIS with other socio-economic data from censuses GIS can be used toinvestigate the spatial patterns of health outcomes in relation to the socio-economic characteristics of diseases gaps in healthcare provision andmonitoring the impacts of healthcare policies Successful GIS applicationsto evaluate access to healthcare services have made GIS an indispensabletool for public health officials assisting to analyze and plan for communityhealth and to better address inequities in the provision of healthcareservices (Gesler et al 2004 Mitchella et al 2002) Optimization and spatialinteraction modeling are often integrated with GIS for planning healthcare and health services Recent advances in radio frequency identificationand wearable global positioning system devices have enable many hospitalsto track individual patients (Albrecht and McIntyre 2005 Kendall 2005)

Efforts have also been made to develop synthetic customized systemsthat link all the conventional GIS functions with decision-making modelsfor health-related issues in the context of a spatial decision support system(SDSS) (Densham 1991) At the core SDSS is essentially a customizedGIS integrating multicriteria decision models optimization and location-allocation models (Rushton 2004) Jankowski and Ewart (1996) reportedthe development a SDSS for rural health practices Ibaugh and Rushton(2003) developed a SDSS for coordinating healthcare services in Iowa

3 Medical Geography for GIS

Undoubtedly GIS have provided medical geographers new sets of verypowerful tools for exploring both disease patterns and health services Themassive literature on GIS and medical geography is dominated by papersfrom the perspective of lsquoGIS for medical geographyrsquo as discussed theprevious section Yet I want to emphasize here that the interactionbetween GIS and medical geography is really a two-way street Newadvances in medical geography also offer GIScientists new ways to exploremany fundamental issues in GIScience such as the ontology of spacerepresentation of space and time scale data mining and knowledgediscovery visualization and issues related to ethics and law (McMasterand Usery 2005)

Many biologicalmedical models such as the famous double helixstructure for DNA as so vividly illustrated in a recently NRC (2006a b)released report often embody a rich set of ideas in geographic imaginationAnd yet few (if any) of these biologically motivated geographic imagina-tions have been systematically studied much less being incorporated intogeography and GIS to both advance GIScience or better design GIS toolsI argue that GIScientists have a lot to learn from medical geography AsMayer (1990) so persuasively argued medical geography is really at thecore of many of the fundamental traditions in geography The diverseconceptual apparatus and methodological frameworks used by medical


Geography Compass



geographers are in fact central to the concerns of GIScience In thissection due to page limits I will highlight only three areas to stimulateand broaden the discussion

31

the small-world network and alternative ontologies

Ontologies are axiomatic frameworks for knowledge representation thatrequire commitments and agreements to ensure mutual understanding andinteroperability among diverse groups of people (Harvey 2003 Kuhn2001) How to represent the world accurately and effectively at the onto-logical level is also one of the major concerns of GIScience So far GISrepresentations are dominated either by a field- or an object-view of theworld following an ontology exclusively framed by Euclidean geometrySome deficiencies of GIS representations in the context of disease analysisand modeling especially in the diffusion of infectious diseases have beenrecognized by both GIS and medical geography experts (Krieger 2001Richards et al 1999 Rushton et al 2000) Due to its inherent staticrepresentation of the world GIS seems ill-suited for modeling and simulatingthe propagation of infectious diseases Recent breakthroughs in the studyof complex networks more popularly known as small-world networksand their applications in modeling diffusion of diseases can potentiallyoffer GIScientists an alternative ontology to better represent the real world(Sui 2006)

The small-world network is one of the major recent advances in thestudy of complex networks and has been one of the major componentsof the lsquonew science of networksrsquo (Barabasi 2002 Newman et al 2006)The small-world network model incorporated two major characteristicsof social contact networks ndash high clustering and high connectivity Con-sequently it has been widely considered as a potential model for socialcontact networks where dynamic diffusions such as epidemics can takeplace The interplay between dynamics taking place in complex networksand the structural properties of complex networks has been considered asone of the mainstream studies on complex networks (Newman 2002)

The small-world phenomenon also popularly known as the lsquosix degreesof separationrsquo implies that there is always a short chain of acquaintanceslinking any random pair of strangers in social contact networks The studyof the small-world problem can be traced back to the 1960s when Milgram(1967) empirically proved in a sociological study that the small-worldphenomenon exists in human contact networks Experimental studiesshowed that the small-world network is a ubiquitous underlying structureexisting in a variety of real-world networks including social informationtechnological and biological networks (Newman 2002) Watts and Strogatz(1998) identified the first small-world network model which is believedto exist somewhere between a completely random network model and acompletely regular network model and has both the high clustering of



Geography Compass


regular networks and the high connectivity of random networks werepresent Their finding was followed by a surge of small-world networkstudies (Newman et al 2006)

But small-world networks have a limited number of long-range randomlinks If combined with densely knit lumps small-world networks canproduce a remarkably low degree of separation between each person andeveryone else in the world The growing literature on small-worldnetworks reveals that it only takes a few shortcuts between cliques to turna large world into a small world ndash what Gladwell (2000) called lsquothe lawof the fewrsquo Studies on small-world networks confirm that minor changesin the arrangement of the links between members of a network candramatically alter the rate at which elements like information computerviruses and infectious diseases spread through a system the lsquogeographyrsquoexhibited in a small-world network is nothing like what we have accustomedto (Warren et al 2002)

Following and expanding the original methodology developed by Wattsand Strogatz (1998) my colleague and I have conducted a simulation onthe emergence of small-world networks between completely regular andcompletely random networks in a two-dimensional scenario (Xu and Suiforthcoming) In addition the effects of the small-world network onepidemic dynamics and different control strategies were investigated

As recent literature (Newman et al 2006) has consistently validatednon-trivial local order if combined with a fraction of long-range randomshortcuts can easily form small-world networks where large changes indynamic behavior can be driven by subtle modifications in the networkstructure Small-world networks are capable of self-organizing co-evolvingand dynamically developing new curvatures of time-space that are absentfrom random or regular networks

The small-world network as one of the highlights in recent advancesin network science has been considered as a potential model to explainepidemic diffusion through social contact networks with vertices repre-senting individuals and links representing their social contacts (Shirley andRushton 2005)

Bian (2005) developed a stochastic approach to examine the effects ofhuman contact network topology on spatial and temporal dispersion ofinfectious diseases This approach is based on an individual-based andspatially explicit conceptual framework that Bian (2004) had developedearlier Discrete individuals and their locations in both a home environ-ment and a workplace are explicitly represented through a dual layer anda dual-scale network The peak of infectious diseases depends on theproperties and structure of the network which can be represented bysix indices ndash the ratio of family and workplace size transmission rate ofthe disease number of direct connections of an individual covariancebetween home and workplace locations shortest path between individualsand length of the infectious period Different combinations of these


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indices can characterize communities with different family and workplacecompositions and connections Bian (2005) also demonstrated that anunderstanding of these effects helps foresee the spatial and temporaldispersion patterns of health threats in different communities and focuson preventive measures targeting communities at high risk The effect ofthese parameters on the peak time of an epidemic the total number ofinfected at the peak time and the total number of infected during anepidemic was analyzed by simulating an influenza epidemic Bianrsquos resultsindicated that high priority should be given to communities that arecharacterized by large family size diverse workplaces within a family andlarge number of connections by family members

In a more recent study applying the small-world network model todisease simulation Xu and Sui (forthcoming) found that changes in thetwo structural properties of the small-world network (ie average pathlength and clustering coefficient) have significantly different effects inepidemics taking place in the network The epidemic dynamics can becharacterized by two properties (ie the maximum epidemic size and thetime to maximum epidemic size) The average path length in the networkhas linear relations with both properties A small average path lengthcorresponds to a large maximum epidemic size and a short time to reachthe maximum epidemic size The maximum epidemic size and the timeto maximum epidemic size can rapidly peak if the clustering coefficientis very high When the clustering coefficient is lower than 09 it does notaffect the epidemics significantly

The small-world network is both a highly connected (small averagepath length) and a highly clustered (high clustering coefficient) networkConsequently epidemics taking place in small-world networks take arelatively shorter time to reach large epidemic sizes Xu and Sui (forthcoming)also revealed that the effectiveness of control strategies is affected bynetwork configuration and proportion of vaccinated population Amongthe different control strategies simulated in a small-world network thetargeted control strategy is the most effective while acquaintance vacci-nation is effective only when it involves a high rate of vaccination

The so-called network science has been developing rapidly (NRC 2005)due to the growing interests and research efforts devoted to the study ofsmall-world networks A new ontology derived from both theoreticalwork in network science and empirical testing in epidemiology can lead tomajor contributions to the development of GIScience in the near future

32

the hierarchical bayesian modeling and multilevel analysis

The complexity of disease etiologies often requires geographicl analysis ofdiseases at multiple levels Medical geographers and epidemiologists havedeveloped a set of sophisticated frameworks to conduct multilevelmodeling and analysis (Diez-roux 2000 2001 Duncan et al 1996 Jones



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and Duncan 1995 Jun et al 2004 Langford et al 1999 Subramanianet al 2001) These multilevel frameworks can be easily incorporated intoa GIS-based analysis and modeling process Multilevel modeling offers aconceptually rich analytical perspective on the geography of healthenabling us to evaluate whether health variations from places (compositionaleffects) or due to places themselves are different in forms of environmentalquality or other attributes (contextual effects) are most relevant Hierar-chical Bayesian approaches which are widely used in public healthapplications (Aratoacute et al 2006 Bell and Broemelling 2000 Riccio et al2006) can be deployed as a methodology to conduct multilevel analysisThese approaches are not only conceptually consistent with a place-based focus for the new medical geography but also better differentiatethe compositional versus contextual factors in medical geographicstudies

Bayesian approaches have been widely used in statistics and sciencesover the past decade (Bolstad 2004) One of their major advantages isthe ability of predicatively forecasting risks even in the presence of sparsedata or rare events (Withers 2002) In the public health area application ofBayesian methods in disease mapping risk assessment and prediction havebeen numerous (Besag and Newell 1991 Wakefield et al 2000 Walleret al 1997) The ability to incorporate prior knowledge without therestriction of classical distributional assumptions makes Bayesian inferencea potent forecasting tool in a wide variety of situations (Withers 2002)However there has been few research incorporating Bayesian approachesinto GIS-based modeling processes to analyze disease patterns and mappinghealth risks

The hierarchical Bayesian approach can be used more specifically tospatially estimate relative health risks (eg the mortality or morbidity ofparticular diseases) by incorporating information from adjacent spatialunits in order to get a better evaluation of health risks within each spatialunit (Bolstad 2004) Relative risk maps developed from conventionalstatistical models often feature large outlying relative risks in small areasand hence show high uncertainty They also fail to catch the similarity ofrelative risks in nearby or adjacent regions but an appropriately tailoredhierarchical Bayesian approach can incorporate spatial assumptions andenable the customary Bayesian lsquoborrow of strengthrsquo from neighboringregions This eliminates high uncertainty and hence generates a betterestimation so-called posterior estimation

Hierarchical Bayesian modeling uses multiple levels of analysis in aniterative way (Carlin and Louis 1996) Unlike conventional statisticalinference which derives the average estimates of parameters hierarchicalBayesian modeling produces parameter estimates for each analytical unitThrough the use of spatial statistics it also identifies and flags lsquoextravariancersquo (Congdon 2001) If there is high uncertainty in a regressionmodel results can only explain a small amount of variance But in a


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hierarchical Bayesian model the unexplained lsquoextra variancersquo is usuallyidentified as either spatially correlated effects or heterogeneity effects (Bestand Wakefield 1999)

Hierarchical Bayesian modeling involves two stages First a likelihoodmodel based on the relative risk vector of diseases is usually specified andthen a prior model over the space of possible relative risks is developedduring the second stage Using software packages such as WinBUGS(Spiegelhalter et al 2004) or GeoBUGS (Thomas et al 2004) it is possibleto yield a set of posterior means for relative risks given observed incidenceor prevalence rates The set of posterior means for relative risks can thenbe used to create a map to visualize high- or low-risk segments Crudemaps were developed from the likelihood model (the first stage) only butoften feature large outlying relative risks in small areas Hence crude mapsusually show a high level of uncertainty due to small sample sizes in thesmall areas They also fail to catch similarities of relative risks in nearbyor adjacent regions As demonstrated by recent studies using diverseenvironmental and public health data (Aratoacute et al 2006 Mather et al2006 Riccio et al 2006) an appropriately tailored Bayesian approach iscapable of incorporating spatial assumptions and help smoothing noisymaps by borrowing strength from the neighbors of mapping units withsmall populations

As Jacquez (2004) so perceptively observed even though many of thebuilt-in analytical functions in GIS are ill-suited for analyzing diseasepatterns sophisticated statistical approaches such as hierarchical Bayesianmodels can certainly improve the toolbox repertoire of GIScientists andmedical geographers

33

multidimensional scaling and non-linear mapping

Medical imaging and visualization have witnessed major breakthroughsduring the past half-century (Dijck 2005) Many of the imaging orvisualization techniques specifically related to the medical field could havemajor implications for GIScience Although cartograms are still foreign tosome GIS users the concept behind cartogram is nothing new to medicalresearchers For example the idea behind the creation of Homunculus ndasha two-dimensional representation of a human body with parts sizedproportional to nerve ends or motor sensors ndash is conceptually consistentwith cartograms (Fig 2) Since Paracelsus first used the term in the 16thcentury (Wikipedia 2005) various different images of the homunculushave been created and used in medical science (Classen et al 1998 Foldiak1993) To most people a homunculus gives a rather distorted view of thehuman body and yet from a neurological science perspective it presentsa more realistic and accurate picture of whowhat we really are It can beargued that cartograms are essentially the geographic version of homunculusor vice versa



Geography Compass


One mapping method that is particularly relevant to enhance themappingvisualization capabilities of GIS is the so-called non-linearmapping through multidimensional scaling (MDS) (Cliff et al 1995Gatrell 1983) MDS is a family of statistical methods by which the informationcontained in a set of data is represented by points on a multidimensionalspace These points are mapped onto a two-dimensional space in such a waythat their mutual geometrical relationships reflect empirical relationshipsin the data

The mathematical framework used in MDS was initially developed aspart of the general development of principal component analysis andfactor analysis (Cliff and Haggett 1996) Mathematically MDS is a matterof statistical fitting (Kruskal and Wish 1978) Essentially the process ofMDS is to find a configuration of

n

points in a multidimensional spacesuch that inter-point distances in the configuration match the experimentaldissimilarities of their objects as accurately as possible A map is constructedin which the location of the points does not correspond to their (scaled)geographic locations but to their degree of similarity based on a groupof variables In general the greater the degree of similarity between placesby the variable measure the closer the places will be in the MDS spaceConversely points that are dissimilar on the variable will be widelyseparated in the MDS space irrespective of their geographic location inthe real world

Euclidean space is the basis for geographic representation in the currentgeneration of GIS The spatial framework in a GIS is often defined by theexisting raster or vector data structures ndash what Cliff and Haggett (1998)called lsquoboxed spacesrsquo However most diseases often diffuse through lsquoforcedspacesrsquo strikingly non-linear and new insights on the process may beobtained if they are mapped using non-linear metrics Ideally GIS shouldencompass different geometries for both analysis and display (Miller andWentz 2003) At present there is no GIS using non-Euclidean geometry(like hyperbolic or elliptic) so the current route to non-linear mappingusing GIS has to proceed via a loose coupling to MDS

Medical geographers have been keenly concerned with the paradoxicalside of a map it can obscure evidence suggest bogus concentrations andfalse trails while revealing distinctive patterns for a particular disease Wecan visualize patterns that may be hidden in conventional maps bymapping disease patterns in non-Euclidean spaces using methods likeMDS as it was demonstrated by Cliff and Haggett (1996 1998) for thediffusion of measles

4 GIS and Medical Geography Toward a New Synergy

The interactions between GIS and medical geography are really a two-way street and both fields are poised for a more synergistic developmentDespite calls made to have GIS dancing and singing to an epidemiological


Geography Compass



tune a decade ago (Gatrell and Bailey 1995) only in recent years have weseen some real synergistic efforts to link the two fields It is time to havethe GIS and medical geography communities working together for anew synergy between these two dynamic and exciting fields Both GISand medical geography are currently poised for a mutually beneficialrelationship And relevant synergies can be achieved at the methodologicalontological and epistemological levels

41

methodological synergy stis and health geomatics

A few years ago Goodchild (2004) observed quite rightly that the staticview espoused by GIS led to an emphasis more on forms and less onprocesses a trend that has made GIS inappropriate for many health-relatedapplications How to incorporate time in data representation and analysisthus making GIS more relevant for disease surveillance has been one ofthe top priorities for researchers Some synergistic works are alreadyappearing as exemplified by the works in the development of healthgeomatics (Boulos et al 2001)

Jacquez (2004) call to develop integrated lsquoSpace-Time IntelligenceSystem (STIS) for disease surveillancersquo represents one of the leadingsynergistic efforts at the methodological level (wwwterraseercomproductsstisstis_demohtml) To health researchers modeling chronicand infectious diseases often entails tracking and describing individuals andtheir attributes (such as disease status date of diagnosis risk factorsand so on) as they move and change through both space and time Yetthe capabilities of the current generation of GIS to handle the temporaldimension have been rather limited due to the inherent lack of temporalcoding in the underlying data structures The goal of STIS according toAvRuskin et al (2004) is to synergistically integrate medical geographywith GIS by designing data structures indexing and queries for spatio-temporal data within the context of health surveillance Jacquez (2004)describes a space-time object model that treats modeled individuals as achain of linked observations comprised of space-time coordinates andtime-referenced attributes STIS is capable of modeling movement forthese individuals under surveillance via either simple (eg linear usingvector representation) or more complex functions The prototype STIS isequipped with several spatial temporal spatio-temporal and epidemio-logical queries emergent from the data model Jacquez (2004) reported apilot study on the applications of STIS to simulate the spread of influenzain a hospital ward

Although lsquohealth geomaticsrsquo is less frequently used in North Americait nonetheless another major synergistic effort to more seamlessly integrateGIS and major health issues As reported by Boulos (2004) evidence-basedGIS-driven is a major investment in the UK for a new national healthservices The new spatial infrastructure based the ideals of a health



Geography Compass


geomatics is designed for early problem detection and solving related topublic health Health geomatics focuses on developing a comprehensivespatio-temporal health information system that can be used across multipleagencies for planning and monitoring clinically cost-effective actions anddeveloping proactive real-time health surveillance services However itremains to be seen whether they can achieve their very ambitious goalsto inform and educate both the public and professionals and empowerdecision-makers at all levels

42

ontological and epistemological synergy critical gis and medical geography

In addition to synergistic activities developed from methodologicalperspectives I believe that synergistic efforts should also be made alongtheoretical fronts This has been the case of some of the most recentdevelopments in both GIScience and medical geography sharing commonentomological as well as epistemological premises

The development of GIScience during the past decade in general andthe studies related to lsquoGIS and societyrsquo in particular have both led to theflourishing of a critical approach to GIS (Pickles 1995 1998 Schuurman2000 Sheppard et al 1999) Unlike earlier concerns on predominantlytechnical issues the critical approach probes how environment and peopleare represented in GIS what social and institutional setting contributedto representations and which were the ethical consequences of applyingthis powerful technology to hot-button social issues Taking a criticalapproach to GIS in public health is increasingly important as moreresearchers are realizing that lsquoa well intentioned tool can cause more harmthan good when used inappropriatelyrsquo (Krieger 2003)

Almost in parallel to the development of critical GIS medical geographyhas increasingly shifted from a biomedical focus using spatial analyticalapproaches to a more eco-social paradigm that embraces diverse alternativemethods (Kearns 1993 Mayer 1992) This is especially crucial whendifference has been established between disease (a physical conditionabnormality) and illness (a culturally defined role and label of having adisease) Many important and relevant issues related to illness defy quantitativeanalysis which often necessitates researchers to explore the issues beyondthose defined by the toolbox

Basic arguments for a social theoretic approach to health geographyresonates well with critical GIS (Harvey et al 2005 Litva and Eyles1995) In particular both critical GIS and medical geography are intenselyconcerned with how to measure and conceptualize place instead of space(Curry 1998 Macintyre et al 2002) To health researchers and epidemio-logists to identify the spider on a complex web of causation for ideas isnot an easy task (Krieger 1994 2001) As Mayer (1983) so perceptiveobserved that lsquoto proceed from the establishment of statistical association


Geography Compass



to possible causative links is an enormous qualitative leap ndash a transformationwhich is fraught with epistemological theoretical and methodologicaldifficultiesrsquo (p 1213) These difficulties in epidemiological research obviouslyrequire researchers to take a more holistic approach to studying health-related issues

Both GIScience and medical geography are moving away from thedominant positivistic paradigm to a more inclusive outlook Such a shifthas resulted in a common ontology and epistemology which may possiblyserve as the base for a new conceptual spring board toward more synergisticefforts The critical approach to both GIS and medical geography isrelentlessly reflexive (Sheppard 2005) To practice critical GIS it is importantto supplement extensive and quantitative methods with intensive andqualitative investigations As McLafferty (2002 2005) has demonstrated inher work relating GIS and womenrsquos empowerment in the context ofbreast cancer that there is plenty of room to integrate a critical GISapproach with complex multidimensional issues in medical geographyFuture synergistic efforts like the work of McLafferty (2005) at the onto-logical and epistemological level to link GIS and medical geography willmake further contributions to what Kwan (2004) termed as the newhybrid geography in which the dividing opposition between qualitativeversus quantitative critical versus technical and spatial analytical versussocial cultural approaches to both GIS and medical geography can bebridged (Kwan and Aitken 2007 Kwan and Knigge 2006)

5 Summary and Conclusion

The goal of this compass article is to present a synoptic overview of thetwo-way interaction between GIS and medical geography and discuss theneed for a better synergy between these two dynamic fields Despite allthe legal confidentiality constraints on health data and unresolvedproblems in geocoding not to mention the stubborn MAUP problems GISoffer medical geographers a very powerful set of analytical and visualizationtools to explore the multiple dimensions of diseases and health servicesWhile GIS applications are important and have contributed to the rapidgrowth of medical geography in recent years this article has also shownthat advances in medical geography can also have significant implicationson the future development of GIScience Medical geography can provideGIScientists with new ontological and methodological frameworks toaddress many fundamental issues in GIScience So the potential for a moresynergistic development of GIS and medical geography is really greatThe critical perspective that is emerging in both GIScience and medicalgeography may serve as an excellent starting point toward a new synergybetween GIS and medical geography in the spirit of a new hybridgeography that can seamlessly integrate the spatial analytical with thesocial cultural traditions Critical GIS shares a compatible (if not identical)



Geography Compass


ontology epistemology and ethics with the new place-based medicalgeography Both GIS and medical geography are poised for a new roundof synergy in both research and education efforts which will be mutuallybeneficial for both fields

Geospatial technologies along with bio- and nano-technologies havebeen regarded as three defining technologies in the 21st century A moreexciting (and in some aspects also disturbing

2

) recent development hasbeen the rapid convergence and integration of these three technologiesI foresee in a not-too-distant future that GIS could even evolve into anextension of the medical imaging technology (Squier 2004 Treichleret al 1998 Wolbarst 1999) so that health-related issues will be analyzedand visualized seamlessly from the genetic all the way to the global levelThis potential can only be realized if growing synergies between GIS andmedical geography are actively pursued I concur with Goodchild (2000)that the relationship between GIS and the world is somewhat analogousto the relationship between medical imaging and the human bodyAdvances in medical imaging have contributed to the development of theradiology profession Now it is time to educate and train medical spatialanalysts who are essentially radiologists but examining issues at the scalescommonly used in geography

Short Biography

Daniel Z Sui is currently a professor of geography and holder of the RetaA Haynes endowed chair in geosciences at Texas AampM University Hismain interests include theoretical issues in GIScience and applications ofGIS in public health and homeland security He is the current editor-in-chief for

GeoJournal

Acknowledgement

The author would like to thank Dr Jim Holt at the US Centers for DiseaseControl and Prevention for the stimulating collaboration in projectsrelated to this article Comments by Lisa Jordan Jose Gavinha and threeanonymous reviewers on an earlier draft are gratefully acknowledged

Notes

Correspondence address Daniel Z Sui Department of Geography Texas AampM UniversityCollege Station TX 77843-3147 USA E-mail suigeogtamuedu

1

The Panopticon is a type of prison building designed by English philosopher Jeremy Benthamin the late 18th century The concept of the design is to allow an observer to observe (

-opticon

)all (

pan-

) prisoners without the prisoners being able to tell if they are being observed or not(wwwwikipediacom accessed on December 19 2006)

2

Due to the paradoxical nature of all technological innovations see Sui and Goodchild (2003)for details


Geography Compass



References

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International Journal of Health Geographics

5 pp 15ndash26Albert D P Gesler W M and Levergood B (eds) (2000)

Spatial analysis

GIS

and remotesensing applications in the health sciences Chelsea MA Ann Arbor Press

Albrecht K and McIntyre L (2005) Spychips how major corporations and government plan totrack your every move with RFID Nashville TN Nelson Current

Ali M Emch M and Donnay J P (2002) Spatial filtering using a raster geographic informationsystem methods for scaling health and environmental data Health amp Place 8 pp 85ndash92

Ali M et al (2002) Identifying environmental risk factors for endemic cholera a raster GISapproach Health amp Place 8 201ndash210

Aratoacute M N Dryden I L and Taylor C C (2006) Hierarchical Bayesian modelling ofspatial age-dependent mortality Computational Statistics amp Data Analysis 51 pp 1347ndash1363

Armstrong M P Rushton G and Zimmerman D L (1999) Geographically masking healthdata to preserve confidentiality Statistics in Medicine 18 pp 497ndash525

AvRuskin G A et al (2004) Visualization and exploratory analysis of epidemiologic datausing a novel space time information system International Journal of Health Geographics 3p 26

Badrinath P Day N E and Stockton D (1999) Geographical clustering of acute adultleukaemia in the East Anglian region of the United Kingdom a registry-based analysisJournal of Epidemiology and Community Health 53 (5) pp 317ndash318

Barabasi A L (2002) Linked the new science of networks Cambridge MA Perseus Book GroupBarry J and Breen N (2004) The importance of place of residence in predicting late-stage

diagnosis of breast or cervical cancer Health amp Place 11 (1) pp 15ndash29Bell B S (2002) Spatial analysis of disease-applications In Beam C (ed) Biostatistical applications

in cancer research Boston MA Kluwer Academic Publishers pp 151ndash182Bell B S and Broemeling L D (2000) A Bayesian analysis for spatial processes with application

to disease mapping Statistics in Medicine 19 pp 957ndash974Berke O (2004) Exploratory disease mapping kriging the spatial risk function from regional

count data International Journal of Health Geographics 3 p 18Besag J and Newell J (1991) The detection of clusters in rare diseases Journal of the Royal

Statistical Society 154 pp 143ndash155Best N G and Wakefield J C (1999) Accounting for inaccuracies in population counts and

case registration in cancer mapping studies Journal of the Royal Statistical Society Series A 162(Part 3) pp 363ndash382

Bian L (2004) A conceptual framework for an individual-based spatially explicit epidemio-logical model Environment and Planning B 31 pp 381ndash395

mdashmdash (2005) Simulating spatially explicit networks for dispersion of infectious diseases InMaguire D J Batty M and Goodchild M F (eds) GIS spatial analysis and modelingRedlands CA ESRI Press pp 245ndash264

Bolstad W M (2004) Introduction to bayesian statistics New York John Wiley amp SonsBoulos M N K (2004) Toward evidence-based GIS-driven national spatial health informa-

tion infrastructure and surveillance services in the United Kingdom International Journal ofHealth Geographics 3 pp 1ndash21

Boulos M N K Roudsari A V and Carson E R (2001) Health geomatics an enablingsuite of technologies in health and healthcare Journal of Biomedical Informatics 34 pp 195ndash219

Boulos M N K et al (2005) Using software agents to preserve individual health dataconfidentiality in micro-scale geographical analyses Journal of Biomedical Informatics 15pp 160ndash170

Carlin B P and Louis T A (1996) Bayes and empirical Bayes methods for data analysis NewYork NY Chapman amp HallCRC

Cartwright L (1995) Screening the body tracing medicinersquos visual culture Twin Cities MNUniversity of Minnesota Press

Cheek J and Rudge T (1994) The Panopticon re-visited An exploration of the social andpolitical dimensions of contemporary health care and nursing practice International Journal ofNursing Studies 6 pp 583ndash591


copy 2007 The Author Geography Compass 13 (2007) 556ndash582 101111j1749-8198200700027xJournal Compilation copy 2007 Blackwell Publishing Ltd

Clarke K C McLafferty S L and Tempalski B J (1996) On epidemiology and geographicinformation systems a review and discussion of future directions Emerging Infectious Diseases2 pp 85ndash92

Classen J et al (1998) Multimodal output mapping of human central motor representationon different spatial scales The Journal of Physiology 512 pp 163ndash179

Cliff A D and Haggett P (1996) The impact of GIS on epidemiological mapping andmodelling In Longley P and Batty M (eds) Spatial analysis modelling in a GIS environmentLondon GeoInformation International pp 321ndash343

mdashmdash (1998) On complex geographical space computing frameworks for spatial diffusionprocesses In Longley P A et al (eds) Geocomputation a primer Chichester UK Wileypp 231ndash256

mdashmdash (2003) The geography of disease distributions In Johnston R J and Williams M (eds)A century of british geography Oxford UK Oxford University Press pp 521ndash543

Cliff A D et al (1995) The application of multidimensional scaling methods to epidemiologicaldata Statistical Methods in Medical Research 4 (2) pp 102ndash123

Congdon P (2001) Bayesian statistical modelling Chichester UK John Wiley amp SonsCrampton J W (2003) Are choropleth maps good for geography GeoWorld January p 58Cromley E K and McLafferty S (2002) GIS and public health New York Guilford PressCromley E K Cromley R G and Ye Y L (2004) On-line reporting and mapping of

spatially aggregated individual records selected by user queries Cartographica 39 pp 5ndash13Croner C M Sperling J and Broome F R (1996) Geographic information systems (GIS)

new perspectives in understanding human health and environmental relationships Statistics inMedicine 15 pp 1961ndash1977

Curry M R (1998) Digital places living with geographic information technologies London BlackwellCutter S L Richardson D B and Wilbanks T J (eds) (2003) The geographical dimensions of

terrorism New York RoutledgeDensham P J (1991) Spatial decisions support systems In Maguire D J Goodchild M F

and Rhind D W (eds) Geographical information systems principles and applications LondonLongman pp 403ndash412

Diez-Roux A V Link B G and Northridge M E (2000) A multilevel analysis of incomeinequality and cardiovascular disease risk factors Social Science and Medicine 50 pp 673ndash687

Diez-Roux A V (2001) Investigating neighborhood and area effects on health AmericanJournal of Public Health 91 pp 1783ndash1789

Dijck J V (2005) The transparent body a cultural analysis of medical imaging ( in vivo the culturalmediations of biomedical science) Seattle WA University of Washington Press

Dorling D (1996) Area cartograms their use and creation Norwich UK University of EastAnglia Environmental Publications

Duncan C Jones K and Moon G (1996) Health-related behavior in context a multilevelmodeling approach Social Science and Medicine 42 pp 817ndash830

Eicher C L and Brewer C A (2001) Dasymetric mapping and areal interpolation imple-mentation and evaluation Cartography and Geographic Information Science 28 (2) pp 125ndash138

Foldiak P (1993) The lsquoideal homunculusrsquo statistical inference from neural populationresponses In Eeckman F and Bower J (eds) Computation and neural systems Norwell MAKluwe pp 39ndash48

Gastner M and Newman M (2004) Diffusion-based method for producing density-equalizing maps Proceedings of the National Academy of Sciences 101 (20) pp 7499ndash7504

Gatrell A C (1983) Distance and space a geographical perspective Oxford UK Clarendon Pressmdashmdash (2002) Geographies of health Oxford UK Blackwell PublishingGatrell A C and Bailey T (1995) Can GIS be made to sing and dance to an epidemiological tune

Presented at the International Symposium on Computer Mapping and EnvironmentalHealth Tampa FL February 13ndash15

Gatrell A C and Rigby J E (2003) Spatial perspectives in public health In Janelle D andGoodchild M (eds) Spatially integrated social science Oxford UK Oxford University Presspp 366ndash380

Gesler W M (2006) Geography of health and healthcare In Warf B (ed) Encyclopedia ofhuman geography Thousand Oaks CA Sage Publications pp 205ndash206



Gesler W M et al (2004) Use of mapping technology in health intervention research NursingOutlook 52 pp 142ndash146

Gladwell M (2000) The tipping point how little things can make a big difference Boston MALittle Brown and Co

Gong P Xu B and Liang S (2006) Remote sensing and geographic information systemsin the spatial temporal dynamics modeling of infectious diseases Science in China Series CLife Sciences 36 (2) pp 184ndash192

Goodchild M F (2000) Strategies for GIS and public health Geographic Information Systems inPublic Health Proceedings of the Third National Conference [online] Retrieved on 22 May 2006from httpwwwatsdrcdcgovGISconference98proceedingsproceedingshtml

mdashmdash (2004) GIScience geography form and process Annals of the Association of AmericanGeographers 94 pp 709ndash714

Gordis L and Gold E (1980) Privacy confidentiality and the use of medical records inresearch Science 207 pp 153ndash156

Gostin L O et al (1996) The public health information infrastructure a national review ofthe law on health information privacy Journal of the American Medical Association 275pp 1921ndash1927

Gotway C A and Young L J (2002) Combining incompatible spatial data Journal of theAmerican Statistical Association 97 (458) pp 632ndash648

Gregorio D I et al (2005) Lumping or splitting seeking the preferred areal unit for healthgeography studies International Journal of Health Geographics 4 pp 6ndash16

mdashmdash (2006) Effects of study area size on geographic characterizations of health events prostatecancer incidence in Southern New England USA 1994ndash1998 International Journal of HealthGeographics 5 pp 8ndash16

Gregory I N and Ell P S (2005) Breaking the boundaries geographical approaches tointegrating 200 years of the census Journal of the Royal Statistical Society Series A Statistics inSociety 168 pp 419ndash437

Guidry V T and Margolis L H (2005) Unequal respiratory health risk using GIS to explorehurricane-related flooding of schools in Eastern North Carolina Environmental Research 98pp 383ndash389

Hall E (2003) Reading maps of the genes interpreting the spatiality of genetic knowledgeHealth amp Place 9 pp 151ndash161

Hao Y et al (2006) US congressional district cancer death rates International Journal of HealthGeographics 5 pp 28ndash39

Harvey F (2003) Knowledges and geographyrsquos technology ndash politics ontologies representationsin the changing ways we know In Anderson K et al (eds) Handbook of cultural geographyLondon Sage Publications pp 532ndash543

Harvey F Kwan M P and Pavlovskaya M (2005) Critical GIS Cartographica 40 pp 1ndash4Hay S I et al (2005) The accuracy of human population maps for public health application

Tropical Medicine and International Health 10 pp 1073ndash1086Holt J B Lo C P and Hodler T W (2004) Dasymetric estimation of population density and

areal interpretation of census data Cartography and Geographic Information Science 31 (2) pp 103ndash121

Ibaugh A and Rushton G (2003) A spatial decision support system for improving the coordinationand delivery of health care services [online] Retrieved on 17 May 2006 from httpwwwuiowaedu~geoghealthindex3html

Jacquez G M (2004) Spatial analysis in epidemiology nascent science or a failure of GISJournal of Geographic Systems 2 pp 91ndash97

Jacquez G M Maruca S and Fortin M J (2000) From fields to objects a review ofgeographic boundary analysis Journal of Geographic Systems 2 (3) pp 221ndash241

Jankowski P and Ewart G (1996) Spatial decision support system for health practitionersselecting a location for rural health practice Geographical Systems 3 pp 279ndash99

Jones K and Duncan C (1995) Individuals and their ecologies analyzing the geography ofchronic illness within a multi-level modeling framework Health amp Place 1 pp 27ndash40

Jun H J et al (2004) A multilevel analysis of womenrsquos status and self-rated health in theUnited States Journal of the American Medical Womenrsquos Association 59 pp 172ndash180



Kearns R A (1993) Place and health toward a reformed medical geography The ProfessionalGeographer 45 pp 139ndash147

Kendall S (2005) RFID tagging for hospital patients CIO Magazine March [online]Retrieved on 5 August 2006 from httpwwwciocomarchive030105tl_trackinghtml

Kevles B (1998) Naked to the bone medical imaging in the twentieth century New BrunswickNJ Rutgers University Press

Khalakdina A et al (2003) Analysis of the spatial distribution of cryptosporidiosis in AIDSpatients in San Francisco using density equalizing map projections (DEMP) InternationalJournal of Hygiene and Environmental Health 206 (6) pp 553ndash561

Kimes D et al (2004) Relationships between pediatric asthma and socioeconomicurbanvariables in Baltimore Maryland Health amp Place 10 (2) pp 141ndash152

Kistemann T Dangendorfa F and Schweikartb J (2002) New perspectives on the use ofgeographical information systems (GIS) in environmental health sciences International Journalof Hygiene and Environmental Health 205 pp 169ndash181

Koch T (2005) Cartogaphies of disease maps mapping and medicine Redlands CA ESRI PressKoch T and Denike K (2004) Medical mapping the revolution in teaching and using-maps

for the analysis of medical issues Journal of Geography 103 (2) pp 76ndash85Krieger N (1994) Epidemiology and the web of causation has anyone seen the spider Social

Science and Medicine 39 pp 887ndash903mdashmdash (2001) Theories for social epidemiology in the 21st century an ecosocial perspective

International Journal Of Epidemiology 30 pp 668ndash677mdashmdash (2003) Place space and health GIS and epidemiology Epidemiology 15 pp 384ndash385Krieger N et al (2005) Painting a truer picture of US socioeconomic and racialethnic

health inequalities the Public Health Disparities Geocoding Project American Journal of PublicHealth 95 pp 312ndash323

mdashmdash (2001) On the wrong side of the tracts Evaluating the accuracy of geocoding in publichealth research American Journal of Public Health 91 (7) pp 1114ndash1116

Kruskal J B and Wish M (1978) Multidimensional scaling Beverly Hills CA Sage PublicationsKuhn W (2001) Ontologies in support of activities in geographical space International Journal

of Geographic Information Science 15 pp 613ndash631Kulldorff M (1997) A spatial scan statistic Communications in Statistics Theory and Methods 26

pp 1481ndash1496mdashmdash (1999) Geographic information systems (GIS) and community health some statistical

issues Journal of Public Health Management and Practice 5 (2) pp 100ndash106Kwan M P (2004) Beyond difference from canonical geography to hybrid geographies

Annals of the Association of American Geographers 94 pp 756ndash763Kwan M P and Aitken S (2007) GIS and qualitative research geographical knowledge

participatory politics and cartographies of affect In DeLyser D et al (eds) Handbook forqualitative methods in geography London Sage forthcoming

Kwan M P and Knigge L (2006) Doing qualitative research using GIS an oxymoronicendeavor Environment and Planning A 38 (11) pp 1999ndash2002

Kwan M P and Schuurman N (2004) Issues of privacy protection and analysis of publichealth data Cartographica 39 pp 1ndash4

Kwan M P Casas I and Schmitz B C (2004) Protection of geoprivacy and accuracy ofspatial information how effective are geographical masks Cartographica 39 pp 15ndash28

Lai P C et al (2004) Understanding the spatial clustering of severe acute respiratorysyndrome (SARS) in Hong Kong Environ Health Perspectives 112 pp 1550ndash1556

Langford I H et al (1999) Multilevel modeling of the geographical distributions of diseasesJournal of the Royal Statistical Society Series C 48 pp 253ndash268

Lawson A B (2001) Statistical methods in spatial epidemiology London John Wiley amp SonsLitva A and Eyles J (1995) Coming out exposing social theory in medical geography

Health amp Place 1 pp 5ndash14Longley P A et al (2001) Geographic information systems and science New York John Wiley amp SonsMaantay J (2007) Asthma and air pollution in the Bronx methodological and data

considerations in using GIS for environmental justice and health research Health amp Place 13(1) pp 32ndash56



Macintyre S Ellaway A and Cummins S (2002) Place effects on health how can weconceptualize and operationalise and measure them Social Science and Medicine 55 pp 125ndash139

Maheswaran R and Craglia M (eds) (2004) GIS in public health practice Boca Raton FLCRC Press

Martin S (2005) Cartography discourse and disease how maps shape scientific knowledge aboutdisease Unpublished Master of Arts thesis Department of Anthropology and GeographyGeorgia State University

Mather F J et al (2006) Hierarchical modeling and other spatial analyses in prostate cancerincidence data American Journal of Preventive Medicine 30 (2) pp S88ndashS100

Mayer J D (1982) Relations between two traditions of medical geography health systemsplanning and geographical epidemiology Progress in Human Geography 6 pp 216ndash230

mdashmdash (1983) The role of spatial analysis and geographic data in the detection of diseasecausation Social Science and Medicine 17 pp 1213ndash1221

mdashmdash (1990) The centrality of medical geography to human geography the traditions ofgeographical and medical geographic thought Norsk Geografik Tidsskrift (Norwegian Journalof Geography) 44 pp 175ndash187

mdashmdash (1992) Challenges to understanding spatial patterns of disease philosophical alternativesto logical positivism Social Science and Medicine 35 pp 579ndash588

mdashmdash (2000) Health geography In Johnston R J et al (eds) The dictionary of human geographyMalden MA Blackwell

mdashmdash (2005) Changing vector ecologies political geographic perspectives In NationalResearch Council (eds) The impact of globalization on infectious disease emergence and controlWashington DC National Academy Press pp 197ndash205

McLafferty S (2002) Mapping womenrsquos worlds knowledge power and the bounds of GISGender Place and Culture 9 pp 263ndash269

mdashmdash (2005) Geographic information and womenrsquos empowerment a breast cancer exampleIn Nelson L and Seager J (eds) A companion to feminist geography Malden MA Blackwellpp 486ndash495

McLaughlin C C (2002) Confidentiality protection in publicly released central cancer registrydata Journal of Registry Management 29 pp 84ndash88

McMaster R B and Usery E L (eds) (2005) A research agenda for geographic information scienceBoca Raton FL CRC Press

Melnick A L (2002) Introduction to geographic information systems in public health GaitherburgMD Aspen Pusblishers

Milgram S (1967) The small world problem Psychology Today 2 pp 60ndash67Miller H J and Wentz E A (2003) Representation and spatial analysis in geographic

information systems Annals of the Association of American Geographers 93 (3) pp 574ndash594Mitchella R Dorling D and Shaw M (2002) Population production and modelling

mortality ndash an application of geographic information systems in health inequalities researchHealth amp Place 8 pp 15ndash24

Monmonier M (1996) How to lie with maps Chicago IL University of Chicago PressMoore D A and Carpenter T E (1999) Spatial analytical methods and geographic information

systems use in health research and epidemiology Epidemiologic Reviews 21 pp 143ndash161National Research Council (NRC) (1997) Rediscovering geography Washington DC National

Academy Pressmdashmdash (2005) Network science report by committee on network science for future army applications

Washington DC National Academies Pressmdashmdash (2006a) The impact of globalization on infectious disease emergence and control exploring the

consequences and opportunities Washington DC National Academy Pressmdashmdash (2006b) Learn to think spatially GIS as a support system in the K-12 curriculum Washington

DC National Academy PressNewman M E J (2002) Spread of epidemic disease on networks Physical Review E 66

016128Newman M E J Barabasi A L and Watts D J (2006) The structure and dynamics of networks

Princeton NJ Princeton University Press



Olvingson C et al (2003) Ethical issues in public health informatics implications forsystem design when sharing geographic information Journal of Biomedical Informatics 35pp 178ndash185

Pickle L W (2002) Spatial analysis of disease Cancer Treatment and Research 113 pp 113ndash150Pickle L W and Mungiole M (1999) Exploring spatial patterns of mortality the new atlas

of United States mortality Statistics in Medicine 18 pp 3211ndash3220Pickles J (1995) Ground truth social implications of geographic information systems New York

Guilford Pressmdashmdash (1998) Arguments debates and dialogues the GIS-social theory debate and the concern

for alternatives In Longley P A et al (eds) Geographical information systems principles tech-niques management and applications New York John Wiley amp Sons pp 49ndash60

Public Health Agency of Canada (2005) Disease surveillance [online] Retrieved on 18 August2006 from httpwwwphac-aspcgccadsol-smed

Quinn M J (1992) Confidentiality In Elliott P et al (eds) Geographical and environmentalepidemiology methods for small-area studies Oxford UK Oxford University Press pp 132ndash140

Riccio A et al (2006) A hierarchical Bayesian approach to the spatio-temporal modeling ofair quality data Atmospheric Environment 40 pp 554ndash566

Richards T B and Croner C M (1999a) Geographic information systems in public healthPart 1 Journal of Public Health Management and Practice 5 pp 1ndash106

mdashmdash (1999b) Geographic information systems in public health Part 2 Journal of Public HealthManagement and Practice 5 pp 1ndash82

Richards T B et al (1999) Geographic information systems and public health mapping thefuture Public Health Reports 114 pp 359ndash373

Romano-Critchley G and Sommerville A (1999) Confidentiality and disclosure of health infor-mation London British Medical Association

Rushton G (2003) Public health GIS and spatial analytic tools Annual Review of Public Health24 pp 1ndash14

mdashmdash (2004) Spatial decision support systems In Smelser N J and Baltes P B (eds)International encyclopedia of the social amp behavioral sciences Oxford UK Pergamon Presspp 14785ndash14788

Rushton G Elmes G and McMaster R (2000) Considerations for improving geographicinformation research in public health URISA Journal 12 pp 31ndash49

Rushton G et al (2006) Geocoding in cancer research a review American Journal of PreventiveMedicine 30 (Suppl 2) pp S16ndashS24

Schroder W (2006) GIS geostatistics metadata banking and tree-based models for dataanalysis and mapping in environmental monitoring and epidemiology International Journal ofMedical Microbiology 296 pp 23ndash36

Schuurman N (2000) Trouble in the heartland GIS and its critics in the 1990s ProgressHuman Geography 24 pp 569ndash590

Selvin S et al (1998) Breast cancer detection maps of two San Francisco Bay Area countiesAmerican Journal of Public Health 88 pp 1186ndash1192

Sheppard E (2005) Knowledge production through critical GIS genealogy and prospectsCartographica 40 pp 5ndash21

Sheppard E et al (1999) Geographies of the information society International Journal ofGeographical Information Science 13 pp 797ndash824

Shin M (2004) Globalization and health In OrsquoLoughlin J Staeheli L and Greenberg E(eds) Globalization and its outcomes New York Guilford Publications pp 193ndash208

Shirley M D F and Rushton S P (2005) The impacts of network topology on diseasespread Ecological Complexity 2 pp 287ndash299

Shuai J et al (2006) A GIS-driven integrated real-time surveillance pilot system for nationalWest Nile virus dead bird surveillance in Canada International Journal of Health Geographics 5pp 26ndash40

Sonesson C and Bock D (2003) A review and discussion of prospective statistical surveil-lance in public health Journal of the Royal Statistical Society Series A 166 pp 5ndash12

Spiegelhalter D J et al (2004) WinBUGS user manual Version 141 Cambridge MedicalResearch Council Biostatistics Unit



Squier S M (2004) Liminal lives imagining the human at the frontiers of biomedicine DurhamNC Duke University Press

Strogatz S H (2001) Exploring complex networks Nature 410 pp 268ndash276Subramanian S V Kawachi I and Kennedy B P (2001) Does the state you live in make a

difference Multilevel analysis of self-rated health in the United States Social Science andMedicine 53 pp 9ndash19

Sui D Z (1999) GIS environmental equity and the modifiable areal unit problem (MAUP)In Craglia M and Onsrud H (eds) Geographic information research trans-Atlantic perspectivesLondon Taylor amp Francis pp 41ndash54

mdashmdash (2006) Geography and the small world theory GeoWorld June pp 24ndash26Sui D Z and Goodchild M F (2003) A tetradic analysis of GIS and society using McLuhanrsquos

law of media Canadian Geographers 47 (1) pp 5ndash17Sui D Z and Holt J B (forthcoming) Analyzing and visualizing public health information

using value-by-area cartograms CartographicaTanser F and Wilkinson D (1999) Spatial implications of the tuberculosis DOTS strategy in

rural South Africa a novel application of geographical information system and globalpositioning system technologies Tropical Medicine amp International Health 4 pp 634ndash638

Thomas A et al (2004) GeoBUGS user manual Version 12 Cambridge Medical ResearchCouncil Biostatistics Unit

Tobler W (2004) Thirty-five years of computer cartograms Annals of the Association of AmericanGeographers 94 (1) pp 58ndash73

Treichler P Penley C and Cartwright L (1998) The visible woman imaging technologiesgender and science New York New York University Press

Trooskin S B et al (2005) Geospatial analysis of hepatitis C in Connecticut a novelapplication of a public health tool Public Health 119 pp 1042ndash1047

Wakefield J C et al (2000) Bayesian approaches to disease mapping In Elliott P et al (eds)Spatial epidemiology methods and applications Oxford UK Oxford University Press pp 104ndash127

Waller L A and Gotway C A (2004) Applied spatial statistics for public health data HobokenNJ John Wiley amp Sons

Waller L et al (1997) Hierarchical spatio-temporal mapping of disease rates Journal of theAmerican Statistical Association 92 pp 607ndash617

Ward M P (2005) Epidemic West Nile virus encephalomyelitis a temperature-dependentspatial model of disease dynamics Preventive Veterinary Medicine 71 pp 253ndash264

Waring S et al (2005) The utility of geographic information systems (GIS) in rapid epide-miological assessments following weather-related disasters methodological issues based on theTropical Storm Allison experience International Journal of Hygiene and Environmental Health208 (1ndash2) pp 109ndash116

Warren C P Sander L M and Sokolov I M (2002) Geography in a scale-free networkmodel Physical Review E 66 056105

Watts D J and Strogatz S H (1998) Collective dynamics of lsquosmall-worldrsquo networks Nature393 pp 440ndash442

Wikipedia (2005) Homunculus [online] Retrieved on 18 August 2006 from httpenwikipediaorgwikiHomunculus

Withers S D (2002) Quantitative methods Bayesian inference Bayesian thinking Progress inHuman Geography 26 pp 553ndash566

Wolbarst A B (1999) Looking within how X-ray CT MRI ultrasound and other medicalimages are created and how they help physicians save lives Berkeley CA University of California Press

World Health Organization (2005a) A geographic information system for leprosy elimination [online]Retrieved on 18 August 2006 from httpwwwwhointlepMonitoring_and_Evaluationgishtm

mdashmdash (2005b) Public health mapping and GIS [online] Retrieved on 18 August 2006 fromhttpwwwwhointhealth_mappingen

Xu B et al (2006) A spatial temporal model for assessing the effects of inter-villageconnectivity in schistosomiasis transmission Annals of the Association of American Geographers96 (1) pp 31ndash46



Xu Z W and Sui D Z (forthcoming) The effect of small-world networks on epidemicdiffusion Ecological Complexity

Yang D H et al (2004) Improving geocoding practices evaluation of geocoding tools Journalof Medical Systems 28 (4) pp 361ndash370

Yang G J et al (2005) A review of geographic information system and remote sensing withapplications to the epidemiology and control of schistosomiasis in China Acta Tropica 96pp 117ndash129


Geography Compass



be reciprocal Advances in medical geography can also have significantimplications on the further development of GIS as science The goal ofthis paper is threefold

1 to present an overview of recent GIS applications in medical geography2 to examine the implications of recent advances of medical geography

for GIS and3 to stimulate further discussions on how GIS and medical geography

can be more synergistically integrated for their mutual enhancement

This article is organized into four sections After this brief introductionan overview of recent GIS applications is presented in section 1 Section2 discusses how advances in medical geography can be incorporated inthe future development of GIS Section 3 outlines how a new synergybetween GIS and medical geography can be sought at the methodogicalas well as ontological and epistemological levels The last section containsa summary and conclusion

Before we proceed two caveats are needed in order to avoid unnecessaryconfusions among some readers First there is a long and rich historyrelated to the mapping of diseases as Tom Koch (2005) demonstrated sovividly in his

Cartographies of Disease

In addition to the almost mythologizedstudy on the 1845 London cholera epidemics by John Snow there werenumerous studies on various disease during the last 300 years such as the1694 plague map of Bari Italy Finkersquos 1792 disease map of the world orthe 1798 yellow fever map of New York by Seaman The maps producedby Peterman (1852) and Haviland (1875) (cited in Pickle 2002) alsodeserve attention Although we have better methods for map productionand dissemination the conceptual issues we are struggling with today arestill strikingly similar to those contemplated by our predecessors Indeedwe still have a lot to learn from the rich history of medical mapping

Second this article adopts a broad inclusive definition for both lsquoGISrsquoand lsquomedical geographyrsquo In particular GIS refers to the science system andservices related to the handling of geospatial information across multiplespatial and temporal scales (Longley et al 2001) According to Gatrell (2002)medical geography is closely related to public health and epidemiology Itseeks an understanding of spatial epidemiology or incidence of diseaseMedical geography is also concerned with operations of the healthcaresystem in a spatial setting In this article lsquomedical geographyrsquo is usedloosely as a big-tent concept that includes all geographic aspects of healthand disease that have been studied by medical geographers epidemiologistsand public health researchers In this article I treat medical geography aspart of health geography In the literature however distinctions are oftenmade between medical and health geography with medical geographydominated by biomedical models favoring intensive quantitative methodswhereas health geography relies more on socio-ecological models andoften employs more extensive qualitative approaches such as participant



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geographic information systems and medical geography: toward a new synergy

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