What’s in a Food Desert? Using Location Intelligence to Analyze Place Visits in Washington, D.C.

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In 2018, the Office of Planning GIS published a polygon dataset of low food access areas in the District of Columbia. A census block group (CBG) was determined to be a low food access area, or food desert, when shopping if the area was estimated to be more than a 10-minute walk from the nearest full-service grocery store.

Food deserts form due to numerous factors, but perhaps the biggest cause is public policy that negatively affects low-income and historically marginalized populations. These policies prevent community members from being able to easily access food, and this affects their overall health and well-being. To address and prevent food deserts, it’s critical for local municipalities and organizations to utilize various tools and technologies to gain a better understanding of food deserts and the impacts these areas have on members of their communities.

Our team of data analysts was curious about how location intelligence could be used to enrich the findings of the earlier study by the Office of Planning GIS. Would it corroborate the results? What else could we learn about what it means to live in a food desert, and how would food deserts compare to areas where food is more readily available? To find out, data obtained between July 2021 and March 2022 was analyzed in conjunction with the open-source polygon dataset to better understand D.C. residents’ visitation patterns to food stores and many other places in the metro area.

About Our Methodology

Unacast examined consumer visits to commercial places of interest in the Washington, D.C., metropolitan area as measured by consumer mobile devices. By reviewing the signal patterns for these devices, our analysts could focus the data on likely residents of Washington, D.C., and exclude visits by travelers and residents of other areas. Devices were then further assigned to a CBG and categorized either as a part of the food desert or non-food desert group in accordance with the Office of Planning GIS study.

Three key metrics were then calculated based on the behavior of these two groups of consumer mobile devices: the number of visits to each chain or venue category, the travel distance, and the attendance index. Travel distance reflects the average distance traveled at each percentile; for brevity, all travel distance metrics cited in this blog post are given at the 50th percentile. An attendance index represents the likelihood of a group of consumers visiting a particular place or category. An index of 100 is average; any number below 100 indicates that consumers are less likely to visit a place, while numbers above 100 show a greater likelihood of visiting.

Here are 5 key takeaways from this important project:

1. Location intelligence verified the findings of the earlier research project.

Our data confirmed that residents of Washington, D.C., neighborhoods identified as “food deserts” traveled much farther than residents of “non-food desert” neighborhoods to visit a grocery store or supermarket. Specifically, while non-food desert residents traveled just 0.8 miles, food desert residents traveled approximately 2.4 miles to visit a grocery store—or 200% farther. This is the difference between an approximately 10-minute walk and one that is a minimum of 30 minutes. As might be expected, residents of food deserts were slightly less likely to visit grocery stores, compared to their non-food desert counterparts.

2. D.C. food desert residents may be relying on convenience stores, discount/dollar stores, and wholesalers for their daily needs instead.

Convenience stores, discount/dollar stores, and wholesalers are all more accessible to residents of food deserts than to residents of non-food deserts. On average, food desert residents traveled 3.2 miles to visit a dollar store, and 3.5 miles to visit a convenience store, while non-food desert residents traveled 3.9 and 4.4 miles, respectively. Food desert residents also traveled about 5 miles to visit a wholesaler like Costco or Sam’s Club, while non-food desert residents traveled nearly 6.7 miles. Across these categories, food desert residents were significantly more likely to visit stores in these categories than their non-food desert counterparts. Still, compared to grocery stores, each alternative requires food desert residents to travel a greater distance to shop for groceries and household goods.

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3. Fast food restaurants are a relatively close and convenient dining option in all D.C. neighborhoods.

Although food desert residents still have to travel a bit farther, on average, to fast food restaurants (3.1 miles vs. 2.3 miles for non-food desert residents, or 35% farther), they are more likely to visit fast food restaurants than their non-food desert counterparts. Accessibility also varies dramatically by fast food chain, with some restaurant brands, including Burger King, McDonald’s, and KFC, equally or more convenient to residents of food deserts than to non-food deserts.

4. Other key services also appear harder to come by in D.C.’s food deserts.

The data also showed that food deserts aren’t just lacking when it comes to food, but also in other critical services. Residents of D.C. food deserts also travel farther to visit pharmacies, banks, and big-box stores, for example. Food desert residents travel twice as far (2.4 miles) to visit a pharmacy as non-food desert residents (1.2 miles). Non-food desert residents travel just 1.25 miles to visit a bank, while food desert residents travel 2.8 miles, or 77% farther. Big-box stores that sell clothing, housewares, and groceries (such as Target or Walmart) are also less convenient to food desert neighborhoods; residents of non-food desert areas travel just 2.7 miles compared to food desert residents’ 4.5 miles (67% farther). Across the board, non-food desert residents are significantly more likely to visit pharmacies, banks, and big-box stores as a result.

5. Public transportation options are equally accessible across neighborhoods.

For both food deserts and non-food desert residents, access to public transportation options is more or less the same. Although food desert residents are more likely to visit a bus station and, by extension, take the bus, residents of both neighborhood types travel approximately 2.5 miles to a station. The same can be said for metro stations which, while visited more often by food desert residents, appear equally accessible to residents of both food desert and non-food desert neighborhoods.

Our analysis shows that residents living in food deserts are forced to go out of their way for not just food, but for other daily needs. Since food desert residents already need to travel long distances to visit a grocery store, it’s possible that they are more willing to go even farther afield to save a bit by shopping for food in bulk at a wholesaler. Our analysis also suggests that living in a food desert may have a material impact on residents’ overall cost of food as well as their health. Residents of food deserts are much more likely to visit convenience stores and fast food restaurants, for example, and less likely to visit pharmacies, which could possibly contribute to higher food costs and greater health issues over the long haul.

Addressing Food Deserts with Location Analytics

This study shows how location analytics can be used to determine the availability of specific goods and services in a specific neighborhood. This information can be used not only to identify areas that are food deserts but also areas that are devoid of other types of businesses, such as bakeries, clothing stores, or gas stations, as well as various community services. This is very relevant information for anyone planning to open a new business, or for established businesses that are looking to expand. Beyond commercial applications, this information can be used by local governments and city planning groups to inform long-term development. By understanding the availability of goods and services in local neighborhoods, city planners can work toward making every neighborhood a more desirable place to live by allocating resources toward underserved areas, or offering incentives to businesses whose services are most in need.

Learn more about how to use location data in your business by booking a meeting with us today.


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