Conducted Poisson e count regression model assessing the relationship between racial composition and three food store varieties: healthy, unhealthy and provisional locations. I use ArcGIS to count the number of food locations per census tract and then regressed the numbers with the racial composition of the neighborhoods in Milwaukee.

1. Finding the Hidden Wells within Food Deserts: Accessing and Measuring Racial Disparities according to Food Store Types in the Milwaukee Metro Area By Mark Caldwell

3. Importance of Study

4. Data and Methods

5. Preliminary Analysis

6. Negative Binomial Regression: White, Black and Latino

7. Results and Predicted Count Probabilities

8. Limitations

10. Spatial Distribution of Healthy Food Locations

11. Spatial Distribution of Unhealthy Food Locations

12. Spatial Distribution of Provisional Food Locations

13. Importance of Study “Food deserts” are considered those places in the city where access/availability of nutritional food is non-existent Non-profit organizations seeks to fill the void in these food deserts through community outreach programs Focal Questions: How does race associate with the locations of these provisional food locations? And additionally: How do healthy and unhealthy food store types associate with race?

14. Focal Relationship: Race/Class and Three Food Store Types Education: Bachelors Degree of Better Healthy Food Stores Race: White Population Percentage Median Income ($) Unhealthy Food Stores Total Population Provisional Food Stores Total Square Miles of Census Tract Main Dependent Variables Main Independent Variable Control Variables

15. Definitions of Food Store Types Healthy: Grocery Stores and Supermarkets. These are locations where healthy food options are available to purchase Unhealthy: Convenience Stores, Fast Food, and Food Markets. Locations that lack healthy food options such as fresh produce and meats. Provisional: are constituted by emergency food locations, food buying club drop-off sites, farmer’s markets and community supported agriculture (CSA) drop sites.* *These sites are being used together because they fall within the realm of businesses and non-profit organizations that are decided by the consumer base which they serve with a common mission goal to provide food accessibility.

16. Data and Methods Unit of Analysis: 304 Census tracts delineated by the Milwaukee City Limits Data: I utilized my own compilation of data that has two components: geocoded food store locations and census tract information regarding socioeconomic indicators for Milwaukee city limits. First the three types of food locations: unhealthy, healthy, and provisional where acquired using two data streams. The first stream utilized the Reference USA: US Businesses database to obtain these food destinations. The second stream consisted of my personal social network with non-profit organizations, farmers markets and actors within the food justice movement who provided the locations of my “provisional locations.” These locations were obtained through data sent me for research purposes or were readily available of company websites or online community forums. Census tract information: Median Income, Total Population, Total Square Miles, Racial Composition and Educational Outcomes Methods: After running a countfit command, a Negative Binomial model was run for all three food store varieties.

18. NBREG: White, Black and Latino White Population Percentage Black and Latino Population Percentage Standard errors in parentheses *** p<0.001, ** p<0.05, * p<0.01

19. Results: Income, Education and Population Income: 1. Healthy=100*exp^(-1.84e-05-1)= -36.78% 2. Unhealthy = 100*exp^(-1.63e-05-1)=-36.78% 3.Provision = 100*exp^(-1.80e-05-1)= -36.78% Population: Not statistically significant for unhealthy and provisional sites 1. Healthy= 100*exp^(0.000271-1)=36.75% Education: 1.Healthy = 100*e^(-1.539-1)=-21.45% 2.Unhealthy= 100*e^(1.339-1)=381.52% 3. Provision = 100*e^(1.569-1)=480.18%

20. Results: Interpreting Racial Percentage White Population Percentage: Healthy = 100*e^(-.00308-1)=-36.67% “The model predicts that the count number of healthy food types will be approximately 37% lower for every percent increase in the White population within census tracts.” 2. Unhealthy = 100*e^(.00368-1)=36.92% “The model predicts that the count number of unhealthy food types will increase by a factor of 1/3 (37%) for every percent increase in the White population within census tracts.” 3. Provision= 100*e^(0.000423-1)=36.80% “The model predicts that the count number of provisional food types will increase by a factor of 1/3 (37%) for every percent increase in the White population within census tracts.”

21. Limitations The model predictions run counter to my original hypothesis, showing that white population percentage is positively associated with the number of unhealthy and provisional types, while negatively associated with healthy food locations. A potential explanation may lie in the notion that healthy food companies attempt to locate there operations in areas where there isn’t market saturation but still have high income and educational achievement demographics The provisional food types is limited based on the researchers data set. Since this analysis has begun, an additional 55 new locations have been identified which fit into this food type category. This would change the results significantly. The definition of “healthy” and “unhealthy” are broad concepts that allows for supermarkets to sell unhealthy products and fast food restaurants to offer “healthy meal options”

22. Areas of Future Inquiry I would like to create some predicted probability graphs to show how the food counts are affected by substantial racial percent changes (20% intervals) I thought about conducting a location quotient analysis utilizing community centers and churches as points which food locations could then be ascribed a set buffer ring. These new polygon units could then be overlaid with Census tract information I need to expand my literature review and theory sections to try to account for the results. Additional theories might include Jargowsky’s“social isolation,” and Massey and Denton’s “racial stratification,” and Brooks-Gunn “neighborhood poverty.”

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