This document summarizes a study that analyzed census tract data, supermarket locations, rates of diet-related disease diagnoses, and transportation options to identify vulnerable populations at risk for diet-related diseases. The study created indices for transportation access, supermarket proximity, and disease diagnosis rates. It then combined these indices into a master index to reveal relationships between location, mobility, and health. The study found that 61% of the Bronx population lives within 0.8 km of a subway stop and has access to transportation, but diet-related disease rates remain high in some areas.

1. Diet-Related Disease, Supermarket
Location, and Access to
Transportation Options:
Identifying food-critical areas and
vulnerable populations
Christopher Bride
GEP690 – Capstone project
Dr. Andrew Maroko
Spring 2012
Diet-Related Disease, Supermarket Location, and Access to Transportation Options:
Identifying food-critical areas and vulnerable populations by Christopher P. Bride is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
© 2012 C.Bride

2. Abstract
• This study is focused on identifying at risk populations
for diet related disease through the analysis of census
tract data, supermarket locations, and area hospital
diagnostic (ICD-9) codes.
• The project is about population, not food deserts. An
individual can live in a food desert and still have access
to healthy food choices (specifically, supermarkets)
throughout the day by their travel habits and various
modes of transportation.
• Transportation options will be analyzed to evaluate
their influence on the health and decisions made
regarding food.

3. Objectives:
• Identify a relationship between diet-
related disease and location of
supermarkets with respect to the
population
• Determine at-risk census tracts
• Incorporate transit options into
findings

4. Methodology
• Create 3 classes of maps:
– Disease Diagnosis
– Transit Options
– Supermarket Proximity to various transit options
• Create three indices
– Transportation (subway and car)
– Supermarket proximity
– Diet-Related Disease Diagnosis (total DRD rates)
Combine indices into one Master Index to reveal
relationships between location, mobility and health

5. 2. Calculate
1. Create .8km 1. Obtain diagnosis
Flow Chart
population w/in 2. Join with Census
Polygons with data from
and outside
Network Dataset tract table
polygons infoshare.org
3. Convert to 4.Map according to 3. Calculate
4. Sum
percentages per accessibility (not percentage of
percentages of
population w/ICD9
census tract lack of access)
Mobility code
relevant diagnosis
Subway Index 5. Map according
to Diagnosis
Access
Index
Diet-Related
2.Convert to Disease
1. Obtain car data
from census
number of cars
(from households Index
with cars)
Car
3. Calculate
percentage of
4. Map according to
car access per
Access
population w/car
(one car per person)
census tract Index
2.Calculate
1.Create .8km
population w/in
Polygons with
and outside
Network Dataset
polygons Supermarket Master Index and
Access Index final evaluation
3. Convert to 4.Map according to
percentages per accessibility (not
census tract lack of access)

6. Diet-Related Disease Rates Index
µ
Data Source: www.infoshare.org

7. Data source: www.infoshare.org

8. Transit Index
Source: NYC MTA developers resource

9. Bronx Subway Access
Convert polygons to census tract data
61% of Bronx
pop. lives
within .8km
of a subway
stop, 19.6%
have access
to a car*
*Data source: US Census Bureau

10. GeoProcessing the Transit Map
Clipped and erased layers
Original polygon source
Dissolve census tracts to
rejoin fragments
Union clipped and erase layers

11. Data source: US Census Bureau

12. Primary Input: Access To Transportation

13. Bus Access – Why is it not included?
[#Bus stops per census tract/(population/area)]
Homogenous
distribution of bus
stops per population
density would of
have a net effect of 0
on the mobility
index.

14. Source data: spatiality.com, US Census Bureau

15. Supermarket Access
74.7% of Bronx
residents live
within .8km of
a supermarket,
55% of subway
stops have a
supermarket
w/in 1 block

16. GeoProcessing the Supermarket Index
Original polygon source
“Clipped” and “Erased” layers
Clip and erase “Union” “Dissolve” census tracts to
rejoin fragments

17. Primary Input: Supermarket Index
Data source: US Census Bureau

18. Primary Input: Access To Transportation
Data source: US Census Bureau

19. Primary Input: DRD Index
Data source: US Census Bureau, www.infoshare.org

20. Combination of Indexes
•
• +
- =

21. The Grand Finale!

23. Afterthought: Comparing Diagnosis and Food/Mobility
to get a perspective on the accuracy of my method

24. References and Datasets
Census tract, water, population, car ownership data and shapefiles obtained from:
• US Census Bureau. (2008). 2008 tiger/line® shapefiles for: New York. Retrieved
from http://www2.census.gov/cgi-bin/shapefiles/state-files?state=36
Diet-Related Disease Diagnosis data obtained from:
• Hospital sparks/icd-9 code data for the Bronx, NY. (2012, February 1). Retrieved
from http://www.infoshare.org
Subway station point, subway line, bus station, and bus line data sets obtained from:
• New York City MTA. (2012, February 1). MTA Developers Resources. Retrieved from
http://www.mta.info/developers/download.html
• Romalewski, S. (2010, July 8). MTA GIS data update. Retrieved from
http://spatialityblog.com/2010/07/08/mta-gis-data-update/
Supermarket location data obtained from: GoogleEarth query (2012)