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Nnenna Okoye
Intro to GIS: Final Project
Due November 12, 2010
Barriers to Care for Diabetic Patients in San Francisco
1. The aim of this project will be to illustrate where diabetes patients are distributed in San Francisco
and analyze the resources and structural barriers to care in their communities. Are the communities
with a high number of diabetics well equipped with the following resources?
-Many grocery stores for healthy food
--Open space for exercise. Regular exercise helps maintains normal blood sugar levels and lose
weight.
-A very low presence of fast food to facilitate the making of health food options?
-Are the communities with a prevalent number of diabetics bike or walk-friendly? Are there bike paths
to allow diabetics that may be unable to afford gym options to use biking as an alternative way for
exercising
It would also be interesting delve further into the topic and determine if there is any correlations with
the distribution of diabetic patients in a city with the distribution of fast-food restaurants. It would also
be interesting to map the walkability of pharmacies for diabetic patients by analyzing the distribution of
diabetic households with that of pharmacies (geocoding using AggData).
2. Papers:
Davies-Cole, John O., Gebreyesus Kidane, and Garret Lum. "Spatial Distribution of Obesity, Diabetes, and
Hypertension in the District of Columbia." ESRI Conference Proceedings. Web. 9 Nov. 2010.
<http://proceedings.esri.com/library/userconf/health03/papers/pap3013/p3013.htm>.
This paper briefing used GIS to plot the distribution of diabetic patients in the District of Columbia,
which confirmed to me that the project was in fact possible perhaps using block group data. The briefing
was straightforward in the sense that it did exactly what it claimed to do in the title: map a spatial
distribution of obesity, diabetes and hypertension. More important for the project was reading about
their methodology and the way the chose to plot their data. The authors obtained their data from
Behavioral Risk Factor Surveillance System, which is likely where I might get my data. However, when I
explored the website, I wasn’t sure of how to utilize it. So that task may be the most important and
complicated part of my project.
Daniel J. Kruger, Jan S. Brady and Lauren A. Shirey.”Using GIS to Facilitate Community-Based
Public Health Planning of Diabetes Intervention Efforts.” Health Promot Pract. 2008 9: 76
This paper, published in the peer-reviewed journal Health Promotion Practices was interesting because
of how it linked GIS analysis to direct recommendation for community action regarding diabetesscreening methods. However, in terms, of mapping the actual data, I didn’t find the paper too helpful
and more instructive of what not to do. For a paper that emphasized GIS in its title, I was surprised to
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find discover that the paper only included one small GIS map, shown below. From looking at the map, I
don’t think that in my case this will be the best way to convey distribution and/or prevalence. Perhaps it
was just because the map was in black and white but I hope I can create a map that will draw the
conclusion for the reader rather than one that requires the reader to interpret the findings. I do like the
concept of using one’s analysis to make direct recommendation and I will try to do this in my paper as
well.
Andrew J Curtis Wei-An Andy Lee. “Spatial patterns of diabetes related health problems for vulnerable
populations in Los Angeles” International Journal of Health Geographics 2010, 9:43 accessed from:
http://www.ij-healthgeographics.com/content/9/1/43
So this paper was really interesting! And its use of GIS was really innovative and the visualization of the
maps was so unique I had to include in this assignment. The abstract announced the following question:
“Can hospital (Emergency Department) surveillance data reveal fine scale spatial variation in diabetes
related conditions for a highly vulnerable population?” The authors compiled data from emergency
room--mapping the home addresses of diabetic patients coming into the emerging room—to designate
“hot spots” of areas where critical intervention was needed. One of the paper’s major focus was to
demonstrate how GIS could use emergency room data to inform management and intervention
strategies. Pretty cool. Another aspect that was quite interesting was the way the authors chose to map
the data, layering contoured surfaces above census tract map.
"Interactive: Diabetes - Thestar.com." News, Toronto, GTA, Sports, Business, Entertainment, Canada,
World, Breaking - Thestar.com. Web. 10 Nov. 2010. <http://www.thestar.com/staticcontent/772097>.
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Although there are some other articles that I read when looking at diabetes-related issues, one of the
more helpful/interesting links I found was actually a online newspaper article by the Toronto Star that
featured a really clear map on the prevalence of diabetes in different counties. I think I am going to try
to create a map as the one shown below because it was the clearest way to illustrate the prevalence of
the disease across all counties.
3. Methodology: My analysis would likely need to start by locating the areas in San Francisco with the
highest prevalence of diabetic patients. Then I would need to delineate the resources that are crucial for
self-care for diabetic patients: insulin availability (as measured by proximity to pharmacies), open space
or access to pedestrian/bike pathways for exercise and grocery stores for purchase of cheap food. It
would be great to map the prevalence of community gardens as well, as these are locations that often
offer cheaper fruits and vegetable. For my recommendation/conclusion portion of the project, I plan to
do a suitability analysis of the creation of community gardens in the San Francisco areas so that more
diabetic patients could have access to cheaper and healthier foods.
I discovered a possible effective way to determine the accessibility of grocery stores that may work for
my project as well. To illustrate the extent to which communities with the highest prevalence of diabetic
patients are equipped with healthy food options, I could map the proportion of each population in a
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block group that is within a grocery store or supermarket as the instructors did below.
Source: http://www.ccsfgis.org/phginfofall
4.
Layer
San Francisco Parcel Data Layer
Open Space
Grocery Stores
Fast Food
Pharmacies
Pedestrian Walkways to analyze
“walkability” to pharmacies
Diabetic Patients Household Street
Addresses or general information
of prevalence
Community Garden?
Source
San Francisco Data Clearing House
Same
AggData (Geocoding)
Agg Data
Agg Data (Geocoding)
AggData (Geocoding)
+/-Accuracy Requirements
200 feet
200
20 feet
20 feet
10-20 feet
50 feet
Behavioral Risk Factor Surveillance
System (CDC)
20 feet
Unknown
200 feet
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