Fall 2021
Food Access Atlas
Mapping Food Accessibility
Embedded Files
Greater Boston, Massachusetts and the United States:
Access to nutritious and affordable food is essential to the wellbeing of every community. Unfortunately, even in the United States there is a lack of access to healthy and affordable food impacting a significant portion of communities.
The USDA defines “food access” as: Limited access to supermarkets, supercenters, grocery stores, or other sources of healthy and where low-cost fast food may make it less convenient for some Americans to eat a healthy diet (USDA-ERS, 2015 March 11).
Boston Supermarkets Service Networks
This first map is an overview of the food retail environment by visualizing accessibility.
1 mile service network for each supermarket and access to bus lines.
Distance can be a barrier to access healthy food. Available mode of transportation and wealth level of the neighborhood are also determining factors since supermarkets with a lucrative drive, tend to open in high income neighborhoods.
Mapping different types of food store in contrast to wealth by census tract, is another approach to identify food areas that lack healthy food sources. Retailers that offer fresh food increase the food access, however, price and affordability may still be a barrier.
Median Income per Census Tract and Food Store Locations in Massachusetts
Food access impacts most of the populations in Massachusetts. It is a challenge especially for low-income communities, people of color, seniors, and people with disabilities
(Jamie Fanous, 2016).
Food Deserts in the U.S.
The USDA studies of food access are done according to census tracts, which are based on population (between 1,200 and 8,000 people per tract). An urban census tract can portray a neighborhood, and a rural census tract, an entire town (U.S. Census Bureau, 2012).
- click on food desert tract areas for details.
- drag to move the map, zoom with scroll
This limited mapping of food access according to geographic locations of supermarkets and income by tract can only reveal the tip of the problem.
Other limitations in this kind of study are the availability and reliability of data, localization of food access models for specific communities, regions, and even other states (Jamie Fanous, 2016).
“Much more goes into food access than can ever be captured by a GIS map,” says Tim Stallmann, a Ph.D. student in geography at the University of North Carolina and a member of the Counter Cartographies Collective. “There’s a whole dimension around money and differential access to stores, how different stores make different groups of people feel welcomed. There’s the amount of time folks have to go shopping in the first place, and when they have it” (Anna Lena Phillips, 2011). It will take more engagement with the people to better understand experiences of food.
References:
Demographic Data: USDA
GIS Data: Open Street Map , massDOT, US Census Bureau
Research: MASS Fodd Access Index, American Scientist
Process:
This is my first attempt at making a deep map with different layers of information in an interactive piece. This project is an attempt to build a tool to measure and explore food accessibility on three scales:
City / State/ Nation.
This tool can be used for researchers and as a way to raise awareness on issues such as Food Deserts and Food Apartheid.
Evaluating food access turns out to be a very complicated task, as different researches approaching this issue have labeled Food Access different criteria. This study runs into the limits of GIS data to represent food access on different scales and since it can vary in meaning for different communities. To take this kind of study further requires direct engagement with the people.
Lessons:
There is a lot of open source data on this topic. Gathering data and cleaning it up was a big part of this project. Most demographic data is from the USDA, a lot of the market data is from the quickOSM plugin for QGIS.
Various mapping techniques have been used to visualize the data; combining service area networks, choropleths and population dot density were effective way to represent the data in hand.
The biggest challenge was to figure out how to export GIS data from QGIS into geojson files with the correct CRS so that they can be worked with in D3, in order to make interactive maps.
Special thanks to Todd Linkner for all the encouragement and guidance in his Mapping Strategies class at Northeastern University - Fall 2021.
QGIS Network Analysis Service Area.
Population Dot Density per Block from Census Data.
Example of interactivity with second map: Filtering layers to show Whole Foods Supermarket in Census Tract area with a high Median Family Income.
Tools: QGIS, JavaScript, D3.
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