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Geographic Information Systems, spatial analysis, and HIV in Africa: A scoping review

  • Danielle C. Boyda,

    Roles Formal analysis, Methodology, Writing – original draft

    Affiliation Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America

  • Samuel B. Holzman,

    Roles Data curation, Formal analysis, Methodology, Writing – review & editing

    Affiliation Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America

  • Amanda Berman,

    Roles Conceptualization, Data curation, Writing – review & editing

    Affiliations Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America, Johns Hopkins Bloomberg School of Public Health Center for Communication Programs, Baltimore, MD, United States of America

  • M. Kathyrn Grabowski,

    Roles Conceptualization, Methodology, Writing – review & editing

    Affiliation Department of Pathology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America

  • Larry W. Chang

    Roles Conceptualization, Formal analysis, Methodology, Writing – original draft

    Affiliations Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America, Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America



Geographic Information Systems (GIS) and spatial analysis are emerging tools for global health, but it is unclear to what extent they have been applied to HIV research in Africa. To help inform researchers and program implementers, this scoping review documents the range and depth of published HIV-related GIS and spatial analysis research studies conducted in Africa.


A systematic literature search for articles related to GIS and spatial analysis was conducted through PubMed, EMBASE, and Web of Science databases. Using pre-specified inclusion criteria, articles were screened and key data were abstracted. Grounded, inductive analysis was conducted to organize studies into meaningful thematic areas.

Results and discussion

The search returned 773 unique articles, of which 65 were included in the final review. 15 different countries were represented. Over half of the included studies were published after 2014. Articles were categorized into the following non-mutually exclusive themes: (a) HIV geography, (b) HIV risk factors, and (c) HIV service implementation. Studies demonstrated a broad range of GIS and spatial analysis applications including characterizing geographic distribution of HIV, evaluating risk factors for HIV, and assessing and improving access to HIV care services.


GIS and spatial analysis have been widely applied to HIV-related research in Africa. The current literature reveals a diversity of themes and methodologies and a relatively young, but rapidly growing, evidence base.


Geographic Information Systems (GIS) are systems designed to store, manage and display spatial data and aid in analysis and interpretation of spatial data [1, 2]. Spatial analysis are techniques applied to geographic data to measure, interpret, and explore characteristics and associations [14]. Geographic data can include defining the locations of a point in space (known as point pattern data), quantifying values of a characteristic at a given location (geostatistical data), or describing an entire geographical region (area-level data) [1, 57]. With the rise and increasing accessibility of computing power and relevant software, GIS and spatial analysis are finding wider audiences and novel applications [3, 810].

Notably, GIS and spatial analysis can be powerful tools to understand, prevent, and help treat diseases. For example, as a visualization aid, GIS software can be used to map the geographic distribution of disease, associated risk factors, and services available for prevention and treatment. Furthermore, spatial analysis of this data can analyze risks for disease, epidemic trends over space and time, and disease hotspots [9, 11]. Taken together, these tools can contribute to the design, planning, and allocation of global health resources for prevention and treatment services, as well as help to assess intervention impact.

HIV continues to be a major global health threat, particularly in Africa which accounts for 67% of the world’s infections, highlighting a need for additional approaches to understand and mitigate the epidemic [12]. GIS and spatial analysis techniques have begun to be applied to HIV-related research in sub-Saharan Africa. However, it is unclear to what extent and depth these tools have been utilized. In this scoping review focused on providing an overview of a broad topic [13], we sought to systematically find and inductively summarize the literature on applications of GIS and spatial analysis to HIV-related research in Africa. Our goal is to help inform researchers, implementers, and policy makers on how GIS and spatial analysis tools have been used, and to explore underlying themes and methodologies.


Eligibility criteria

Inclusion criteria.

This review included peer-reviewed articles that focused on HIV in Africa and involved GIS or spatial analysis. HIV-related topics included HIV prevalence or incidence, HIV-specific mortality, HIV risk factors, and implementation of HIV prevention or treatment services. Only articles explicitly based in a country or countries in Africa were included. Spatial analysis techniques and the GIS use were defined broadly for the purposes of this review in order to capture the diversity of emerging practices, i.e. we included articles that used any specialized GIS software or specifically incorporated any spatial analysis techniques. Articles were included regardless of participant characteristics, including age, race, sex, sexual orientation, HIV status or other factors. Articles were included regardless of sample size, scope or setting within Africa, type of program activities or interventions, or by study design.

Exclusion criteria.

We excluded studies concerning non-opportunistic infection health outcomes in HIV-positive individuals or for which HIV infection was not the main focus but treated as a risk factor. We did not include studies that used geographic coordinates solely for study enrollment purposes, e.g to generate sampling frames, or basic visualization, e.g. simple display of points on a map. We excluded HIV phylogenetic studies, unless they involved other spatial analyses, on the basis that such research is complex and specialized enough to warrant its own review. Abstracts, posters, reviews and commentary pieces were also excluded.

Information sources and search.

We developed a search strategy with terms relating to “HIV”, “geographic information systems”, and “spatial analysis” (see Appendix for full search strategy). This search was developed through an iterative process of incorporating new terms and refining those included based on results returned and identification of relevant citations. Reviewers conducted electronic searches of PubMed, EMBASE, and Web of Science on November 16, 2017, with no restriction on date or language of publication. Potentially eligible articles known to paper authors were also included in the initial search.

Study identification and data collection.

Two reviewers independently screened article titles and abstracts of all initial search results. Articles deemed eligible for inclusion by either reviewer underwent full text review by three authors to determine final inclusion with adjudication done by majority vote when necessary. Reviewers extracted data to a master table, capturing details about location, objectives, study design, data source and study population, spatial analysis methodologies, software used, and results.

Synthesis of results.

Following data extraction, we categorized studies based on the primary content of the paper in an inductive, thematic analysis. Articles were permitted to sort into more than one category as relevant. We did not assess study quality due to study heterogeneity.


Study selection

As shown in Fig 1, 773 references were identified, excluding 780 duplicates, of which 681 were screened out as not meeting eligibility requirements. Full text review of the remaining 92 studies identified 65 final articles for inclusion in the review. The reasons for exclusion were that the study was not based in Africa (1 reference), that the study was not primarily focused on HIV-related topics (10 references), and that the analysis did not involve spatial analysis (16 references).

Included studies spanned the continent in geographic focus. Eight papers were multi-country analyses, and 15 different countries were represented in single-country papers. Among single-country studies, 37 were based in Southern Africa, 16 in East Africa, and two each in West Africa and Central Africa. South Africa was the most common country of study with 17 papers. Dates of publication spanned from 2000 to 2017, with almost all (98%) published between 2006–2017 and over half published after 2014.

Study themes

Inductive thematic analysis found that GIS and spatial analysis were most commonly used to examine: (a) HIV geography, (b) HIV risk factors, and (c) HIV service implementation. We discuss methodological techniques in each theme and narratively and in tabular format highlight key findings below.

HIV spatial epidemiology

Twenty-seven articles sorted into this thematic category which focused on the spatial distribution of HIV. The following subcategories were identified and used to further characterize studies: (i) spatial cluster analysis, and (ii) smoothed mapping.

Spatial clustering analysis.

One of the most common spatial analytic techniques for evaluating the geographic distribution of HIV was local cluster detection (Table 1), or the identification of HIV disease “hotspots”. Clusters are areas with higher or lower numbers of events in a particular study region than expected if cases are randomly distributed [14].

Table 1. Studies using cluster detection and clustering analysis to characterize the spatial distribution of HIV.

Thirteen studies used the Kulldorff spatial scan statistic [15] to identify clustering of HIV infection, [1624], HIV-specific and all-cause mortality [2527], and sero-discordant partnerships [28]. In these studies, the spatial scan statistic identified statistically significant “hotspots” or “cold spots” by systematically scanning circular windows of varying sizes across the study area, comparing the number of cases within the window to those outside the window. The resulting data were used to identify areas of particular risk or concentrated need, and to compare characteristics inside and outside of clusters. SaTScan software was the most commonly used tool for identifying clusters (see S1 Table for software used in each study).

Clusters of high and/or low HIV prevalence were detected, independent of administrative boundaries, in 20 of 22 countries where the Kulldorff spatial scan statistic was applied, though the geographic size and relative risk strength of the clusters varied widely. Studies that compared clusters over time found that they tended to persist with little change in location [17, 22, 25]. In one study, areas contained within high-prevalence clusters had stable or increasing HIV prevalence over time, even when HIV significantly decreased in the rest of the country [17].

Less commonly-used spatial statistics included Local Moran’s I and the τ statistic [29]. Both of these methods are global clustering statistics which measure the tendency for points to occur closer together in space by chance across the entire study area. In contrast, the Kulldorff spatial scan statistic identifies local clusters in a particular region. Local clusters can exist in either the absence or presence of global clustering [30]. In HIV epidemiology, global clustering statistics have been used to determine if HIV-positive people tend to live closer to each other than would be expected by chance. For example, a study in Uganda conducted a global clustering analysis and [31] found that a participant living in the same household as an HIV-positive person was 3.2 times more likely to be HIV-positive than any study participant; however, the authors found no significant spatial clustering of cases outside the household.

Smoothed mapping of HIV.

Smoothed maps, or “heat maps” of HIV use spatial methods to create and display continuous gradations of HIV-related outcomes over space, using data gathered in a limited number of locations to predict values in unmeasured locations (Table 2). Eighteen studies in this review created smoothed maps of HIV [1719, 23, 24, 2729, 3241]. Methods for creating smoothed maps of HIV varied in complexity. Simpler methods were based on calculating weighted averages of existing data surrounding an unmeasured location. These included inverse distance weighted estimates (IDW) [17, 19, 28, 29, 34, 38] and kernel density smoothing with a fixed [18, 23] or varying radius [32, 33]. More complex methods, such as kriging or Bayesian hierarchical modeling with a spatial component, used statistical models to predict HIV prevalence [24, 27, 32, 3437, 3941]. Software use varied by method, with IDW and kriging maps typically created using ArcGIS [38], kernel density smoothing done in BayesX [18] or Idrisi [23], and Bayesian modeling done in R [35, 36], WinBUGS [39, 40], or OpenBUGS [27] (see S1 Table).

Table 2. Studies creating continuous surface maps of HIV.

Two studies compared different mapping techniques, and the most comprehensive study found that a Bayesian geostatistical modeling approach was slightly more accurate than five other methods [32, 34]. However, it was also noted that the choice of mapping method may have less impact on the accuracy of the HIV estimates than the underlying quality and sampling frame of the data used [3234]. HIV estimates derived from smoothed maps and aggregated to the country level were found to align closely with more complex UNAIDS model data, regardless of mapping methodology [34].

Smoothed mapping techniques generated maps which estimate HIV prevalence in areas where data is not available from surveys [17, 29, 3234, 42]. Studies that generated smoothed maps found high-risk areas that stretched across more than one administrative area [19, 29, 3234, 36, 37], or high prevalence pockets in provinces that would not necessarily have been identified as high priority settings [29, 32, 36]. As Kleinschmidt et al note, a high prevalence area that they detected across two South African provinces could benefit from inter-provincial coordination of treatment and prevention service provision [36].

Detailed smoothed maps also enabled visual comparisons of the spatial patterns of HIV over time periods [17, 29, 34, 35], or between various subgroups of the population, such as men and women or those living in sero-discordant partnerships and those not [19, 28, 36, 39]. GIS software also helped validate HIV prevalence estimates from antenatal care surveillance against population-based survey estimates, which are more comprehensive, but more resource-intensive, by comparing data between the two sources at similar locations [43, 44].

HIV risk factor analysis

Thirty-three studies sorted into this thematic subcategory which focused on the spatial epidemiology of HIV which is based on the concept that both HIV and the factors associated with infection vary spatially and have some degree of spatial dependence. Articles in this theme divided into three sub-topics based on underlying methods utilized: (i) spatial regression, (ii) joint spatial disease/risk factor modeling, and (iii) geography of risk factors.

Spatial regression.

Spatial analysis contributed to the evaluation of risk factors for HIV by testing the assumption that data is independent of geography (Table 3). Many studies noted that this independence assumption was violated if data displayed spatial dependence or correlation, meaning that data points in close proximity to each other were more alike than those further apart [18, 21, 27, 40, 4550]. To address this issue, thirteen studies included in their analysis spatial regressions with spatial random effects to represent the correlations within geographic areas and between geographically proximate areas [18, 21, 27, 39, 40, 4552]. These analyses used BayesX [18, 21, 45, 46, 4850], WinBUGS [27, 39, 47]and OpenBUGS Bayesian software [40]. Studies that compared non-spatial and spatial regressions found that the spatial models had better fit [4547]. Spatial models may have been more accurate in these studies because they accounted for underlying, unmeasured environmental, social, behavioral or other confounding factors relevant to HIV prevalence that were geographically determined.

Table 3. Studies performing spatial regressions, regressions with spatially varying coefficients, and joint spatial modeling.

Seven studies used the results of spatial regression to map adjusted relative HIV risk over geographic areas, controlling for other factors like age, sex and employment. Maps of adjusted HIV risk visualized spatial patterns and heterogeneity, displaying relative risk by administrative area [21, 4750] or over smoothed, continuous surfaces [45, 46]. In Zambia, spatial patterns of HIV changed after adjusting for age and spatial correlation so that the two highest-prevalence districts were no longer among the areas at highest risk [49]. These results, together with similar findings elsewhere [4850], highlight the extent to which geographic location can provide insights on complex or poorly understood risks for HIV that would not be captured in a standard statistical model.

Joint spatial disease/risk factor modeling.

Four studies examined how the relationship between HIV and a risk factor or other sexually transmitted infection (STI) changed over geographic space (Table 3) [5356]. Such spatial variation may have been due to unmeasured, underlying modifying or cofounding factors that varied across space, such as attitudes, cultures and preferences, or due to sampling variation [53]. Spatial analysis was used to evaluate and display how different risk factors [53, 56] or other STIs [5355] were more or less correlated with HIV across different geographic areas. Joint spatial modeling was conducted with WinBUGS [54, 55], R [53]or a combination of ArcGIS and STATA software [56].

These studies aimed to assist resource allocation decisions by identifying opportunities for geographic targeting of HIV prevention interventions [53, 56]. They searched for geographic areas where particular risk factors were more relevant to HIV outcomes, for instance finding regions where age at first sex had a stronger association with HIV prevalence [53]. However, models that allowed the effect of each risk factor to differ by spatial location were not consistently better or worse fitting than models that held relationships constant over space [53, 56]. There was some evidence that the impact of social risk factors relating to sexual habits varied more over space than the impact of demographic risk factors in a South African analysis [56].

Three studies compared spatial patterns of HIV and another STI, either HSV-2 [53, 55] or syphilis [54], in order to address questions of population-level ecological associations between STIs. These studies examined the extent to which these STIs were spatially correlated, whether because the infections were mutual risk factors or because they had similar underlying, potentially unmeasured causes in Africa [5355]. Joint spatial modeling revealed that syphilis prevalence did not predict HIV prevalence in South Africa, given their low degree of spatial correlation, and as such would not be suitable as a proxy measure to examine ecological differences in risky sexual behavior resulting in HIV infection [54].

Geography of HIV risk factors.

GIS enabled the systematic measurement of spatial risk factors (Table 4). For example, GIS technology allowed for the characterization of a defined area around a study participant’s community, such as the risk of schistosomiasis [57] or the density of refugee camps within a 25km radius [19]. Similarly, GIS tools were used to measure distances between a household’s location and the nearest city [19, 35], major road [19, 58], body of water or refugee camp [19]. One study used geo-referenced mobile phone data from a cell service provider in Côte d’Ivoire to develop a predictive model of area HIV prevalence based on residents' travel and communication patterns [42].

Local cluster detection methods were used to identify clusters of HIV risk factors, for instance of condom non-use [59]. These risk factor clusters anticipated areas that might benefit from prevention services, and were compared to mapped clusters of HIV to display the overlap between risk and outcome [29, 37]. Two studies compared incidence of new HIV infections inside and outside of risk factor clusters [28, 37]. Three of the studies that identified clusters of high HIV prevalence [23, 28] or mortality [27] also compared characteristics inside and outside of these clusters to identify associated risk factors.

Smoothed maps of risk factors showed geographic variation independent of administrative boundaries enabling new insights [28, 37, 6062]. Smoothed mapping of malaria endemicity enabled the estimation of malaria prevalence at every point where HIV prevalence had been measured [63]. Studies compared the spatial distribution of a risk factor to the spatial distribution of HIV to assess evidence of geographic concurrence [28, 37, 57, 61]. Geographic overlap of particular risk factors and HIV enabled a community- or population-level analysis of their relationship, rather than individual-level analysis, and highlighted the extent to which location influences the HIV epidemic.

Risk factors were also examined with spatial regression methods, such as early sexual initiation [64] or HIV stigma [65]. In Nigeria, for example, controlling for spatial correlations, demographic and knowledge covariates, and the non-linear effect of age revealed a clear North-South dichotomy in early sexual initiation [64]. Studies analyzing the geography of risk factors often used GIS software like ArcGIS [19, 28, 29, 63], Idrisi [58], and SaTScan [23, 59] to extract spatial data about the risk factor that was then incorporated into an analysis in statistical software like R, STATA, SPSS, and others. Alternatively, some analyses were conducted entirely in R [35, 6062, 65], BayesX [64] or STATA, using thefunctions in these software applications to handle both spatial and statistical analyses, such as the geonear function in STATA [57].

HIV services implementation

Twenty-three articles contributed to understanding relationships between geography and implementation of HIV services. Two subcategories were the (i) effect of distance to HIV services on health outcomes, and (ii) planning and evaluating HIV service provision.

Effect of distance.

GIS technology allowed for measurement of geographic distances and distribution of services and enabled analysis of the effect of distance on health outcomes and service uptake (Table 5). For example, GIS tools enabled the calculation of distances between points, including both Euclidean straight-line distance [29, 40, 58, 6672] and road distance [7376] between households and clinics, providing objective measures of geographic burdens to health care access. Evidence was mixed on whether these measures were [77] or were not [66, 75, 78] correlated with self-reported measures of health care barriers. Studies also examined the impact of proximity to health care on presence of HIV risk hotspots [45, 69] and HIV clusters [24, 70, 72, 79].

Table 5. Studies regarding the effect of distance to health care.

Studies identified in this review analyzed the effect of distance to care on nearly all stages of the HIV treatment cascade, including knowledge about HIV [72], follow-up visits for male circumcision prevention services [67], HIV testing and counseling [24, 70, 73, 79], healthcare attendance [66, 74, 78], ARV uptake [68, 71, 75, 76] and adherence [77, 80], PMTCT uptake [69], and HIV mortality [29, 40, 45]. The majority of studies found that proximity to health care was associated with positive health outcomes at each step of the cascade [45, 6672, 76, 78, 79], though these findings were not universal [24, 40, 71, 74, 75, 77].

In some studies, access for HIV patients appeared to be mediated through the public transportation network more than through road access [29, 71, 75], particularly for patients who were further away from the clinic [71]. This relationship may reflect the positive correlation between road transportation and HIV transmission observed in some settings [29, 58]. There was furthermore evidence that the effect of distance on service access differed between those accessing mobile care compared with those attending static clinics [67, 73]. HIV-positive patients did not necessarily transfer to a closer clinic when given the chance [66, 76], and postpartum women who switched clinics returned to care faster and with better outcomes when they transferred to distant clinics than when they re-entered nearby clinics [80].

Distance measures were extracted with GIS software like ArcGIS [26, 29, 60, 67, 70, 7274, 76, 77, 79, 81], ArcMap [80], MapInfo [68], Google Earth [71], Idrisi [23] and associations were analyzed in a standard statistical software such as STATA, R and SPSS (see S1 Table for more details).

Service provision.

GIS technology was used to examine the extent to which health care service expansion [60, 70, 82] and outreach to communities [68, 73, 76] increased access and uptake (Table 6). Decentralization of HIV services was found to dramatically decrease the average distance traveled for ART among people living with HIV [68, 76] and to diminish the variation in service uptake over space [60, 70]. For example, as HIV testing became more widely available in Mozambique, clusters of high or low rates of testing disappeared, indicating that the geographic barriers to testing became less relevant [70].

Two studies used GIS tools to target HIV services and improve their efficiency [38, 82]. One study mapped the density of HIV infection in Lesotho in order to calculate the geographic variation of coverage if the country was to optimize efficiency in treating 70% of its HIV-positive population [38]. In rural areas where most of the HIV-positive population lives, the authors found, coverage would be as low as 4%, whereas the densely populated urban areas would receive up to 94% coverage. Yao and Murray [82] used GIS technology along with optimization software Gurobi to optimize the location of HIV testing and counseling sites, so that the overall distance traveled by the entire population to their nearest testing site would be as short as possible. Software like ArcGIS [38, 70, 73, 76], ArcView [60]and MapInfo [68]was used for visualization and extraction of distance data to be analyzed in statistical software like STATA and R.


The review demonstrates the wide applications of GIS and spatial analysis to understanding HIV and supporting HIV care and prevention services in Africa. Most of the literature was published recently and rapid growth in the evidence-base was clear. We expect that GIS use and spatial analysis methodologies will continue to expand as more researchers and implementers develop knowledge and capacity in this growing field and increasingly recognize its usefulness.

For example, in this review GIS tools were regularly used to display HIV prevalence, incidence or mortality data in order to communicate complex information about the epidemic in a clear and accessible way. The ability to identify spatial clusters and generate smoothed maps revealed variations within and across administrative regions, the boundaries of which often have little bearing on disease prevalence.

In the context of HIV risk factor analysis, spatial analysis reflected the likelihood that people living close to each other share common exposures and disease outcomes and that location was critical in including in HIV risk factor analyses. The findings that spatial regressions of HIV which accounted for geographic correlation in the data had better fit than non-spatial regressions highlight the benefit of adding spatial data to certain analyses.

Another recurring theme was the critical role of geography in informing access to HIV-related health services in low-income settings with poor existing health care infrastructure. GIS and spatial analysis may allow for more efficient allocation of resources and appropriate response targeting in many African settings.

Of note, while this review was focused on more technologically and methodologically sophisticated applications, several articles not included in this review demonstrated the utility of simply visually displaying mapped data points, such as for plotting the locations of HIV services [83] and risky sexual behaviors [8488]. Mapping the overlap of service provision and demand was useful for selecting priority areas for programmatic expansion [81, 89, 90]. Program implementation could benefit from dynamic, interactive and iteratively updated maps with verified health facility coordinates and infrastructure data [91] and simple mapping may be good entry point for more advanced geospatial techniques.

A number of gaps in the literature were also found by this review. West African countries were less represented, perhaps because they have lower HIV prevalence rates and less HIV research capacity. Central African and Horn countries were similarly less represented. Countries that did not include both HIV biomarkers and geographical coordinates in their DHS surveys also may be less likely to feature in spatial analysis studies because these were characteristics that determined inclusion in certain studies. Some important HIV related outcomes had minimal or no found spatial studies such as HIV medication shortages or stock-outs. Adherence and retention outcomes were also not substantially considered in relation to spatial factors. Finally, there was limited literature on HIV resistance and spatial analysis, potentially because these data are too sparse.

Interesting directions and issues in spatial research were also found. The novel use of cell phone technology in some studies points to an important future area of spatial research. Data independent of national boundaries/national data collection practices to facilitate cross-country comparisons and multi-country analyses would be a helpful future direction given the mobility of persons and complexity of social networks. Privacy concerns continue to be an important area of consideration as GIS tools become easier to use and more broadly implemented.


Our search strategy may have missed studies relating to GIS or spatial analysis and HIV, particularly as the vocabulary for GIS is dispersed and evolving. There is some risk of publication bias, potentially favoring articles that detect spatial heterogeneity over those that do not, or favoring particular spatial analysis methods over others. This review was not intended to assess the quality, methodological rigor or risk of bias within studies included for review, nor did we attempt to conduct a meta-analysis. In part, this decision reflects the wide diversity of contexts, approaches, and methodologies that emerged through the review, which were difficult to synthesize. There are not currently any best practice or guidelines for reporting of geospatial studies, and this may be an area for future development.


This systematic review searched for and summarized evidence on the use of GIS and spatial analysis techniques for HIV in Africa. Our findings demonstrate the wide array of spatial approaches to HIV-related data. These applications include characterizing geographic distribution of HIV, evaluating HIV epidemiologic risk factors, and assessing and improving implementation of HIV services. The rapid growth and diversity of applications of GIS and spatial analysis to the field of HIV yields great potential for future insights and progress.


The authors thank Peggy Gross, public health informationist, for her advice on the search strategy.


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