WITHDRAWN: Multi-Variate Mapping Techniques to Identify Positive Deviance: Using GIS Mapping to Contextualize Food Insecurity, Poverty, and Obesity in Florida

Geographic Information Systems (GIS) is a field of analyses using computer programs to capture, analyze, and present spatial and other types of geographic data. GIS mapping clarifies relationships and patterns between individuals and their environments. Similar to the traditional ecomap, GIS mapping enables social workers to view, understand, interpret, and organize client systems in ways that reveal important relationships, patterns, and trends. This presentation utilizes GIS mapping techniques to examine the problem of community level food insecurity, poverty, and obesity, and identify communities demonstrating potentially replicable positive deviance.

Living in low-income communities and having limited access to healthy foods are associated with higher rates of obesity. Researchers used ArcGIS multivariate mapping techniques to examine the relationship between the Food Environment Index (FEI) (a composite score of limited access to healthy foods and food insecurity estimates), adult obesity rates, poverty rates, and other geographic and demographic factors. Florida adult obesity ranged from a low of 14.4% to a high of 46.4%. County FEIs ranged from 5.1 - 8.3, with 0 (worst) to 10 (best). As expected, obesity rates are higher in areas with poorer FEI scores. However, as identified through bivariate mapping, several counties did not maintain the hypothesized patterns, with one county demonstrating measures of positive deviance. Positive deviance identifies uncommon but successful patterns despite facing similar challenges and/or limited resources and knowledge.

Multivariate mapping can serve as a useful tool to visualize deviations in expected patterns. Bivariate maps may also help identify areas to be studied at a sub-county level to determine why outcomes are better or worse than other counties with the same demographics. Furthermore, multivariate mapping can be useful in framing studies investigating positive deviance.

This presentation will include a significant number of high-quality, multivariate maps to demonstrate the methodology and results.