supermarket location, tranportation options, and their relationship to diet-related disease
DESCRIPTION
A GIS analysis of the relationship between diet-related diseases, supermarket location, and transportation optionsTRANSCRIPT
Diet-Related Disease, Supermarket Location, and Access to Transportation
Options:Identifying food-critical areas and
vulnerable populations
Christopher BrideGEP690 – Capstone project
Dr. Andrew MarokoSpring 2012
© 2012 C.Bride
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.
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.
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
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
Flow Chart1. Create .8km Polygons with
Network Dataset
2. Calculate population w/in
and outside polygons
3. Convert to percentages per
census tract
4.Map according to accessibility (not
lack of access)
Subway Access Index
1.Create .8km Polygons with
Network Dataset
2.Calculate population w/in
and outside polygons
3. Convert to percentages per
census tract
4.Map according to accessibility (not
lack of access)
1. Obtain diagnosis data from
infoshare.org
2. Join with Census tract table
3. Calculate percentage of
population w/ICD9 code
4. Sum percentages of relevant diagnosis
5. Map according to Diagnosis
1. Obtain car data from census
2.Convert to number of cars
(from households with cars)
3. Calculate percentage of
population w/car (one car per person)
4. Map according to car access per census tract
Mobility Index
Supermarket Access Index
Car Access Index
Diet-Related Disease Index
Master Index and final evaluation
Diet-Related Disease Rates Index
µ
Data Source: www.infoshare.org
Data source: www.infoshare.org
Transit Index
Source: NYC MTA developers resource
Bronx Subway AccessConvert 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
GeoProcessing the Transit Map
Original polygon sourceClipped and erased layers
Union clipped and erase layers
Dissolve census tracts to rejoin fragments
Data source: US Census Bureau
Primary Input: Access To Transportation
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.
Source data: spatiality.com, US Census Bureau
74.7% of Bronx residents live within .8km of a supermarket, 55% of subway stops have a supermarket w/in 1 block
Supermarket Access
GeoProcessing the Supermarket Index
Original polygon source“Clipped” and “Erased” layers
Clip and erase “Union” “Dissolve” census tracts to rejoin fragments
Primary Input: Supermarket Index
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Primary Input: Access To Transportation
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Primary Input: DRD Index
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Combination of Indexes • • +
- =
The Grand Finale!
Afterthought: Comparing Diagnosis and Food/Mobility to get a perspective on the accuracy of my method
References and DatasetsCensus 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)