Neighborhood Characteristics of Fast Food Restaurant Locations Jennifer R. Bonds and Dominic Farris Harvard School of Public Health June 2005 Brisa N. Sanchez M.Sc. And Steven Gortmaker, PhD

The Obesogenic Environment Obesogenic environments encourage consumption of food and/or discourage physical activity (Chisolm et al., 1998, Price and Gottesman, 1991; Stunkard, 1991; Weinsier et al., 1998) Studies have shown associations between fast food intake and increased BMI and weight gain (French, Harnack, and Jeffery, 2000;French and Jeffery, 1998) Cost is the most significant predictor of dietary choices (Sooman, Macintyre, Anderson, 1993; Foley, Pollard, 1998; Mackerras, 1997) Fast food consumption is related to obesity and this relationship is strongest among low-income individuals (French, Harnack, Jeffrey, 2000; Jeffrey, French, 1998)

Fast Food Restaurant Placement Easy access to restaurants is a concern for customers and businesses strive to meet this demand Business planners consider neighborhood demographics when determining the placement of new restaurants Business planners may also consider a wide variety of factors including property taxes, zoning, and business permits Neighborhood racial segregation in Chicago

Research Inquiries There are more fast food restaurants per person in low income census tracts (excluding census tracts in the lowest 10 percentile) than middle/high income census tracts Ethnic specific restaurants targeted at Blacks and Hispanics in majority Black and Hispanic census tracts than census tracts of predominately other racial/ethnic groups

Operational Definitions Fast food restaurant – eating places where customers order items and pay before eating and where food can be eaten on the premises or taken out Census tract – the unit of measure for the US census; it is small and its boundaries are drawn along visible features such as roads and are always nested within counties Poverty status – income thresholds determined by the census bureau based on family size and total family income in the last 12 months; this value changes each year based on inflation

Operational Definitions Housing value – an estimate of how much a house and lot would sell for if it were for sale (excluding properties that were renter occupied) Shopping area – one-half square mile boundaries around each census tract Community area –defined by sociologists at the University of Chicago during the 1920s, and at that time corresponded to neighborhoods; there are 77 and they are used for political purposes by the city of Chicago

Data Characteristics Wealth and Ethnicity in Chicago Community Areas

Population Density

Wealth as Median Income**

Wealth as Median Housing Value

Community Areas by Major Ethnic Group

Census Tract Characteristics by Ethnicity

**Data Analysis Census tract data based on 2000 Census and 2004 American Housing Survey Restaurants were categorized by ethnic group: Black, Hispanic, and other Used two determinants of wealth: median income and median housing value Did not consider census tracts with median income and median housing value in the lowest 10 percentile Shopping areas where developed Used Poisson regression

Results: Restaurants and Wealth Income: Adjusted for commercialization and the percentage of renters The number of restaurants increases as income and commercialization increases The percentage of renters decreases as income and commercialization increases Conclusion: Our hypothesis was not supported

Median Income

Results: Restaurants and Wealth Housing Value: Adjusted for commercialization and the percentage of renters The number of restaurants increases as housing value and commercialization increases The percentage of renters decreases as housing value and commercialization increases Conclusion: our hypothesis was not supported

Housing Value

Results: Black American - targeted Restaurants Adjusted for median income, median housing value, commercialization, population density, and total number of restaurants There are more Black American - targeted restaurants in majority Black census tracts compared to census tracts that are majority Hispanic and other ethnic groups Commercialization, housing value, and the total number of restaurants in the census tracts have a positive association to the number of Black American – targeted restaurants Conclusion: Our hypothesis was supported

Black American targeted Restaurants and their Location

Results: Hispanic - targeted Restaurants Adjusted for median income, median housing value, commercialization, population density, and total number of restaurants There are less Hispanic – targeted restaurants in majority Hispanic census tracts compared to census tracts that are majority Black and other ethnic groups Commercialization, total number of restaurants, housing value and income have negative association to the number of Hispanic – targeted restaurants in census tracts Conclusion: Our hypothesis was not supported

Hispanic-targeted Restaurants and their Location

**Limitations Limited information on housing value, income, and poverty status for some census tracts Overlap in census tracts??? Incomplete list of Hispanic and Asian targeted restaurants Omitted information from census tracts with very small populations (less than 200 people)

**Implications Can be generalized to other large, diverse metropolitan areas Public health interventions in majority Black neighborhoods can reflect knowledge of fast food restaurant placement Hispanic targeted fast food restaurant chains are not popular among Hispanics Population within Chicago city limits does not differ greatly based on wealth as compared to census tracts in the greater Chicago area*

**Future Research Census tracts within the city limits should be compared to census tracts in the greater metropolitan area outside the city limits Asian targeted restaurants should be studied Other factors of fast food restaurant placement should be studied Why are more/less in certain areas* Is the appearance of restaurants in lower income areas in response to local demand or does their appearance drive demand?* Access*

Acknowledgements We would like to thank the following people: Bryn Austin Steve Melly Dr. Steven Gortmaker Brisa Sanchez, M. Sc. Dr. Louise Ryan Isabelle Angelouski

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