Brenna Friday and David Criss are graduate students at Wayne State University (WSU) who are dedicated to studying the drivers and consequences of food insecurity in Detroit, Michigan. Their work in this field is made possible through the support of the Transformative Research in Urban Sustainability Training (T-RUST) program at WSU. This analysis builds upon the food insecurity index model originally developed by Data Driven Detroit (D3) in 2017 and it identifies a troubling pattern: the rising risk of food insecurity appears to overshadow the modest improvements observed in specific areas of Detroit.
Food insecurity is a reality faced by many families across the United States. In 2021 alone, a staggering 13.5 million households in the country struggled to provide adequate nutrition for their families (Coleman-Jensen et al, 2022). It is imperative that these households receive resources to avoid this situation, but they are oftentimes overlooked by government support or find it difficult to navigate applications for resources.
In 2022, researchers, community leaders, and policymakers from Michigan identified a troubling and interconnected relationship between low income, food insecurity, and adverse health outcomes. Our research digs deeper into the factors that push households into this cycle and pinpoints the neighborhoods facing the greatest challenges. In this article, we provide an updated and comprehensive look at the food insecurity situation in Detroit using food insecurity risk factors to identify areas where food insecurity is most acute.
In 2017, D3, in collaboration with Forgotten Harvest Food Bank, developed their own model to assess food insecurity in Wayne, Oakland, and Macomb counties. Building upon their work, we conducted an analysis to uncover the similarities and differences in food insecurity within Detroit between two specific time periods: 2014 and 2021. Our investigation identifies zip codes where residents have remained at high risk of food insecurity, as well as zip codes where progress has been made in reducing their risk.
We used 2021 data from the American Community Survey and followed similar methods from the 2017 Constructing an Index of Food Insecurity report by D3 with some slight modifications to focus on zip code level data. If you’re interested in a more in-depth look at how we created our model and established our scoring system, you can find additional information in our Supplemental Data section.
Each map captures a glimpse of the 27 zip codes within Detroit city limits. We’ve classified these areas into five distinct levels of vulnerability to food insecurity. The index begins with the lowest risk index score, sitting at 14 in 2014 and increasing to 15 in 2021. The highest risk index score peaked at 57 in 2014 and 53 in 2021.>
As we explore these maps, the shades of each zip code paint a vivid picture. The deeper the hue, the more severe the vulnerability to food insecurity. Together, these maps unveil the stories of communities striving for security, a testament to resilience in the face of adversity.
Figure 1: Average Food Insecurity Index Score 2014
The index scores range from 14 to 57, a 43 point difference.
Figure 2: Average Food Insecurity Index Score 2021
The index scores range from 15 to 53, a 38-point difference.
In the above maps, the darker-shaded zip code regions represent areas where residents face significant challenges with food insecurity. In 2014 and 2021, zip codes 48219, 48224, 48209, 48214, and 48227 consistently held the highest rankings in terms of food insecurity risk. These are the communities where essential food assistance programs like SNAP (Supplemental Nutrition Assistance Program) and WIC (Women, Infants, and Children) can make a critical difference. While nearly all high-risk zip codes (with a score above 45) retained their high-risk status each year, one exception was observed in 48205, which decreased by 13 points from 2014 to 2021.
On the opposite end of the spectrum, lighter blue zip codes indicate areas where residents face lower levels of food insecurity risk. Additionally, areas classified as low-risk (with scores below 29) in 2014 predominantly remained low-risk in 2021. These zip codes encompass a substantial portion of Midtown, Downtown, as well as the Corktown, Woodbridge, and Dexter Linwood areas west of M-10. However, it’s crucial to note that even in these zip codes with comparatively lower risk, there are still families at risk of going without food.
Every zip code in Detroit could strengthen their local food systems through community initiatives and programs to further reduce chances of food insecurity. These initiatives serve as a safety net for families who may occasionally struggle to put food on the table. Some of the solutions that contribute to improved food security include:
- Shared Food Resources: These programs provide shared tools and spaces for food preparation and storage, along with offering free classes on food preparation and preservation.
- Financial Literacy Workshops: Classes on financial management help families stretch their food budgets further, enhancing their overall food security.
- Community Refrigeration Facilities: Access to shared, generator-powered refrigeration spaces helps to reduce food losses during power outages. Continuous safe storage of perishable foods is important to reduce potential for foodborne illness.
These short-term supportive policies and practices work together to create more resilient and food-secure communities. Notably, three zip codes (48216, 48211, 48208) showed remarkable stability in terms of food insecurity vulnerability, maintaining consistent scores for both years.
We feel it is important to note that the average index score over both years was 36 and the median score was 38.
Figure 3: Rate of Change between 2014 index and 2021 index
In the above rate of change map, we’ve measured the progress made in the battle against food insecurity from 2014 to 2021. To calculate this change, we took the 2021 index scores, subtracted the corresponding 2014 scores, and divided by the original 2014 scores. These changes are expressed as percentages, with zip codes that saw no change highlighted in gray.
Now, let’s dive into the colors! Positive changes in blue signify a decrease in vulnerability, while negative changes in orange represent areas where food insecurity increased. Deeper blues signal substantial progress, with larger decreases in vulnerability from 2014 to 2021. In contrast, darker orange shades point to challenges, indicating larger increases in vulnerability.
Figure 4 uses arrows of different lengths and colors to illustrate the same changes as we see in Figure 3. Again, blue arrows show negative changes and orange arrows show positive changes for each specific zip code between 2014 and 2021. The length of the arrows here indicates the magnitude of the change whether the score increased, decreased, or remained consistent. This visual highlights the unique changes in risk for each zip code.
Understanding these patterns is the first step towards targeted efforts and resource allocation to reduce food insecurity. These changes from 2014 to 2021 stand out:
- 9 zip codes jumped more than 10% in food insecurity risk, an indication that emergency support should be considered in these areas.
- 5 zip codes dropped more than 10% in food insecurity risk. However, this result does not necessarily mean these areas are not in need of support.
- 7 zip codes in the low risk category (light blue from Fig 1 and 2) changed unpredictably, with some decreasing scores by 40% and others increasing by 38%. This suggests that food insecurity could be a dynamic situation, especially for households living slightly above the risk level.
- Zip codes 48204, 48205, 48206 all reduced food insecurity risk between 2014 and 2021. This result in our model was mostly due to a decrease in low income movers and individuals with less than a high school diploma. While policies mentioned above like shared food resources may have also facilitated these changes, they mostly help address food insecurity in the short-term. Policies that increase minimum wages or decrease non-food costs (childcare, health care, etc.) would better improve the overall system that allows for food insecurity to persist.
Why This Matters
In areas where food insecurity risk is getting better, most of the changes only suggest slight improvements.
In areas where food insecurity risk is getting worse, it’s common for things to severely break down.
This perspective challenges the idea that food insecurity is changing uniformly across the city. Instead, our analysis highlights a concerning trend: the increase in the risk of food insecurity likely outweighs the modest improvements seen in certain pockets of Detroit. Figure 4 illustrates this trend with the long orange arrows
At the same time, the areas that are at risk of severe food insecurity in 2014 don’t see much change in 2021. This is very different from the low-risk zip codes in 2014 that see large changes, both positive and negative, in their index scores by 2021. Together these trends suggest that household food security status is only stable in severe cases of food insecurity.
These insights highlight the urgency in addressing food insecurity as a growing concern for many Detroiters. It serves as a reminder that behind the numbers and data are real people facing daily challenges, emphasizing the need for targeted efforts to support vulnerable communities. Measuring the complex experience of food insecurity with a single value is obviously challenging. Still, measuring the risk associated with this issue is crucial if we hope to minimize the cultural, physical, and emotional consequences of insufficient access to food.
The differences gleaned between the 2014 and 2021 food insecurity indexes suggest that food insecurity vulnerability has not changed on average between both time points, but that vulnerability is increasing and declining at smaller scales in opposite ways across the city.
Data on food insecurity risk is only publicly available at the state-wide level, which can complicate the pursuit of identifying neighborhoods of need and installing targeted policies to mitigate the problem. Hence, our findings are particularly sensitive to how we treat and define the data and model. The general methods used for this analysis are outlined in the 2017 report by D3, Constructing an Index of Food Insecurity. Like the original D3 model, our analysis relies on the following six key indicators related to food insecurity:
- Housing burden: number of households with housing costs greater than 30% of household income
- Households with no vehicles: number of households in a zip code without a vehicle available
- Individuals with less than a high school diploma: number of individuals age 25 years or older in a zip code with less than a high school diploma
- Low-income movers: number of individuals in a zip code with income less than $25,000 who moved in the past year. Children below working age (15 years old) were not included in this metric.
- Single-parent households: the percentage of families in a zip code with children younger than 18 years that are headed by a single parent
- Beyond 1/4 mile of transit: the percentage and number of people in a zipcode living more than one-quarter mile from a bus stop
Data for these metrics in our analysis were collected from the 2017-2021 five-year American Communities Survey (ACS) except for the beyond ¼ mile of transit indicator. Data on active bus stops were collected from DDOT (2022) and SMART (2017) transportation websites. Unlike the original 2017 model, we limited our study to Detroit and scaled up our spatial unit from the census tract to zip code level. This was done to align with the scale of other data products shared with us from collaborations outside the scope of this blog post.
To construct the updated index, we ranked each of the 6 indicators in each zip code from lowest to highest and assigned them a score from 1 to 11. Lower scores indicate the area is less at-risk of experiencing food insecurity, and higher scores indicate the area is more at-risk. The lowest possible score in our index is 6 while the highest possible score is 66. Scores for each predictor were summed within zip codes to calculate a final index value.
To visualize a comparable map from the D3 2017 model, food insecurity indicators at the census tract level from the original project were rescored to the zip code level within Detroit. Census tracts were grouped within Detroit zip codes using the spatial join function in ArcPro, then scores were averaged and summed within zip codes. Scores for census tracts that spanned multiple zip codes were only included in whichever zip code contained the majority of the tract. Changes in tract assignment between the 2014 and 2021 ACS datasets were manually reviewed to ensure that redistricted areas were not left out of the analysis.