Identifying High Risk Areas for Childhood Obesity in Houston, Texas
1.
I will be researching risk areas for childhood obesity in Houston, Texas, a city
frequently labeled the fattest in the United States. Although there are many, often
controversial, variables included in the childhood obesity model, this method will
focus on the demographic, socioeconomic, built environment, and access to food
variables. The demographic variables include African Americans and Hispanics,
while the socioeconomic variable will be those of low-income, as these are
identified as primary risk factors in childhood obesity. Built environment
variables include limited access to green spaces. Access to food variables include
limited access to grocery stores with excessive access to fast food restaurants and
convenience stores. These are the secondary variables, as they aren’t as definitive
and researched as the socioeconomic and demographic variables. Suggestions will
be provided for the areas identified as high risk, but they will most likely address
issues other than the previously mentioned variables, as restricting commerce and
the built environment aren’t a common precedent in Texas.
2.
Food Environment Atlas by USDA
The USDA recently came out with a series of maps regarding food in the United
States. There are too many variables to list, but the applicable maps that I found
useful are the demographics maps, the pre-school childhood obesity maps, and
access to grocery stores, fast food restaurants, and convenience stores. The maps
are intended to be informative without drawing direct conclusions. However,
because of their simplicity, they do provide citizens with the ability to be more
knowledgeable of the food environment and perhaps to even inspire action. Due
to the fact that their data is on the county level I will be unable to use it, but the
sources of their data is important to note. For example, their demographic maps
come from the census, as well as the information regarding grocery stores, fast
food restaurants, and convenience stores (these being from the economic census).
The pre-school childhood obesity data is from the CDC.
Mapping Data Shape Community Responses To Childhood Obesity by Sage
A group at Children’s Optimal Health in Austin, Texas used GIS to identify areas
with high rates of childhood obesity. In 2008, 40.3% of fourth-grade children
were overweight, while 23% were obese. They were able to go beyond general
census data and utilize fitness tests and school demographics to identify where
obesity was most prevalent. They utilized American Community Survey
Demographics and InfoUSA to access information on fast food restaurants,
convenience stores, and grocery stores. They found that obesity was common
among schools along I-35 near downtown Austin with high proportions of
minorities. Two neighborhoods were identified as having large populations of
obese children, but one neighborhood failed school fitness tests at dramatically
higher rates, which was correlated to their access to food.
Mapping the Childhood Obesity Epidemic: A Geographic Profile of the
Predicted Risk for Childhood Obesity in Communities Across the United
States by Hendey
This group used the National Health and Examination Survey (NHANES) and the
2000-04 Medical Expenditures Panel Survey (MEPS) data identified to census
tracts. Their variables included demographics, household structure, employment,
education, income, housing, health environment, food environment, and physical
environment. The variables most linked to childhood obesity included the
demographic, income, and education variables. They were unable to access more
detailed data, such as the built environment, but they suggest that this data would
be available by many large metropolitan agencies. They then provide around 100
maps that they created of the United States as a whole and states individually.
Mapping the evolution of 'food deserts' in a Canadian city: Supermarket
accessibility in London, Ontario, 1961-2005 by Larsen
Looking at London, Ontario, this paper examines the flight of supermarkets away
from urban centers and towards suburbs. The change in super market access from
1961 to 2005 was dramatic, as suburbs became more popular, and city centers
declined. This has marked individuals that resided in urban areas, often times of
lower income, to have fewer grocery stores and less access to healthy foods. They
analyzed census tracts and the distance from the tract’s centroid to a supermarket
over time. They cross-referenced this with access to public transportation, as it
was unrealistic to assume that all supermarkets must be within walking distance.
With public transportation taken into account, super markets were considerably
more accessible for low-income city centre residents with the exception of East
London. Larsen recommends that the government address these growing grocery
store inequities, as they are particularly common of those with low income.
3.
I will be using the select by attribute for the census data in order to narrow the
tracts. The hierarchy for the select by attribute will run: areas with low-income,
race, and the number of children. Schools, parks, grocery stores, convenience
stores, and fast-food restaurants will be identified within and near the tracts using
select by location. I would also like to do a contrast of an area with low-income
and high percentage of minorities with an affluent area with a high percentage of
Caucasians. This is a preliminary sketch of my methods, and they are subject to
change.
4.
Data Layer
Census Tracts
Source
Census
Margin of Error (allowed)
50
African American Pop.
Hispanic Pop.
White Pop.
Number of Children
Household Income
Green Spaces
Grocery Stores
Convenience Stores
Schools
Primary Roads
Census
Census
Census
Census
Census
S-Drive
StreetMap USA
StreetMap USA
S-Drive
S-Drive
50
50
50
50
50
50
50
50
50
50
50 feet is appropriate, considering schools located within a tenth of a mile of fastfood restaurants are 5% more likely to be obese. At a quarter of a mile there is no
correlation. Consequently, a hundredth of a mile, approximately 50 feet, would be
the most appropriate.
References
Food Environment Atlas. USDA. February 2010. http://www.ers.usda.gov/foodatlas/
Mapping Data Shape Community Responses To Childhood Obesity. William M Sage,
Matthew Balthazar, Steven Kelder, Susan Millea, et al. Health Affairs. Chevy Chase:
Mar 2010. Vol. 29, Iss. 3; pg. 498, 5 pgs.
Mapping the Childhood Obesity Epidemic: A Geographic Profile of the Predicted Risk
for Childhood Obesity in Communities Across the United States. Sharon K Long, Leah
Hendey, Kathryn Pettit. December 2007. The Urban Institute.
Mapping the evolution of 'food deserts' in a Canadian city: Supermarket accessibility in
London, Ontario, 1961-2005. Kristian Larsen, Jason Gilliland. International Journal of
Health Geographics 7.16 (April 18, 2008): p16.