A Geographic Information System (GIS) is a computer-based tool for integrating and analyzing geographic or spatial data. GIS allows users to combine important geographic attributes (for example, streets, zip codes, and program locations) with other types of data (for example, neighborhood demographic data, program participant characteristics, and program outcome measures). In turn, users can visualize this information in map form to identify patterns or relationships. Users can also manipulate GIS’s statistical tools to detect significant geographic trends and patterns. In sum, geospatial analysis allows users to visually and statistically detect relevant geographic and spatial phenomena that may remain unnoticed through more traditional analytic approaches.
The emergence of geospatial analysis has given support to an idea that real estate developers have long since accepted: When it comes to success, it’s all about location, location, location! Space, like many other resources, is finite. When space and location have been utilized responsibly and creatively to support increased access to fresh food, health care, green spaces, recreational spaces, employment, and places to gather, communities and individuals benefit through improved quality of life and well-being. Alternatively, when space and location have been used as tools to isolate people and create disproportionate access to resources (i.e., redlining), it has created economic inequity that has contributed to vicious cycles of poverty and poor health outcomes for minority populations (Hillier, 2002; Zhang & Ghosh, 2016). This article provides an introduction to how geospatial analysis can be used to examine the spatial distribution of program effects and community resources for evaluation and program design.
GIS provides several benefits to those designing, implementing, and evaluating programs. During the program design phase, GIS can be used to select program sites. For example, users can map locations of target populations and the geographic distribution of assets and needs. This information can, in turn, be used to select optimal program sites that are accessible to target populations as well as to develop a set of services that meet the varying needs of these populations. For example, transportation planning may utilize GIS mapping to determine areas with limited access to public transportation (i.e., bus routes, metro/subway access) or redirect routes into low-income communities that would benefit from increased options for transportation (Miller & Wu, 2000).
GIS also has the potential to reveal underlying social, economic, educational, and political characteristics of a community. By examining these structures, users can obtain a better understanding of contextual factors that facilitate or hinder program success. As noted earlier, users can develop maps that display the distribution of assets and needs within a community such as job opportunities, educational services, health resources, and similar indicators. These indicators can also be mapped across time to show how community context has changed, such as shifts in community demographics and resources. Users can take this information and respond to these changes as well as forecast others. For example, users that detect cultural shifts in a community might adapt program services to be more culturally responsive, such as hiring individuals who speak a specific language or adjusting a program’s curriculum to be more culturally relevant.
From an analysis standpoint, these contextual data points contribute to a more ecological approach to understanding circumstances before and after the introduction of a new program or intervention. GIS allows users to overlay data collected from different program implementation sites with actual geographic locations. This provides users with the ability to explore how a program is situated within and interacts with its environment for program implementation and evaluation. For instance, such an analysis might reveal that a program location that is closer to a major public transportation hub serves more people, is attracting participants from certain target neighborhoods but not others, or that program impacts are occurring among participants from certain areas of a city but not others.
Using GIS in this manner can help evaluators and program decision makers examine how a program can differentially benefit program recipients and promote equity, linking the benefits of programs and the distribution of those benefits across different locations to inform recommendations for programs intended to decrease existing disparities. For example, research conducted on food security often identifies higher availability of markets and grocery stores in areas with higher incomes (Walker, Keane, & Burke, 2010). At the same time, areas that lack access to fresh fruits and vegetables are often overpopulated by quick-service restaurants that capitalize on low-income areas by providing cheap food with high caloric content (Block, Scribner, & DeSalvo, 2004). Given these findings, if a program designed to improve access to fresh food was implemented in a mixed income area, the effect of the program could have mixed, or null results. However, program effects might exist if the results were segmented by the pre-existing food environments of program recipients. When evaluating these types of programs, using geospatial analysis to map the spread of effects provides a richer picture of how the program functions and for whom it benefits. Geospatial analysis and considerations of existing location features can inform the development, planning, and evaluation of programs to target communities most in need of program benefits as well as those that may already have adequate or excess resources. Adding spatial elements to analyses provides an avenue for exploring disparities resulting from systemic and historic practices, and evaluating programs or grants designed to address these inequities.
References
Block, J. P., Scribner, R. A., & DeSalvo, K. B. (2004). Fast food, race/ethnicity, and income: A geographic analysis. American Journal of Preventive Medicine, 27(3), 211–217.
Hillier. A. (2002) Redlining in Philadelphia. In A. K. Knowles (Ed.), Past Time, Past place: GIS for history. Redlands, CA: Environmental Systems Research Institute.
Miller, H. J., & Wu, Y. H. (2000). GIS software for measuring space-time accessibility in transportation planning and analysis. GeoInformatica, 4(2), 141–159.
Walker, R. E., Keane, C. R., & Burke, J. G. (2010). Disparities and access to healthy food in the United States: A review of food deserts literature. Health & Place, 16(5), 876–884.
Zhang, M., & Ghosh, D. (2016). Spatial supermarket redlining and neighborhood vulnerability: A case study of Hartford, Connecticut. Transactions in GIS, 20(1), 79–100.