GIS 2 Final Project - Food Deserts in Eau Claire, Wisconsin

Background

What IS a food desert?

A food desert is defined differently by various agencies, municipalities, and localities. Definitions can include varying qualities such as socioeconomic, geospatial, and nutrition. Socioeconomic variables can include economic barriers such as reliable access to transportation, food prices, and income. It could also be spatially analyzed by area type (think rural vs urban), distance to nearest supermarkets, or number of supermarkets in a given area. Nutritional qualities can be described by the variety of foods provided at grocery stores, fresh or prepared food, or the nutritional values of foods offered at locations. In this project, I will be using the definition provided by the American Nutrition Association, stated as

"parts of the country vapid of fresh fruit, vegetables, and other healthful whole foods, usually found in impoverished areas. This is largely due to a lack of grocery stores, farmers' markets, and healthy food providers." (County 2014-2015) (Ploeg and Breneman 2017)

Why is studying food deserts important?

The city of Eau Claire is known to have several residential areas that are considered food deserts. Children's school performance, quality of life, and future health issues can be significantly influenced through the lack of access to healthy food. How easily and close vulnerable populations can access good quality, healthy food is essential for proper nutrition and performance in schools for children. Grocery stores are significantly harder to come by compared to fast food options, which can be cheaper and easier for low-income populations to consume. This can negatively impact the health and quality of life for many individuals who may not have access to a car. (County 2014-2015) (Ploeg and Breneman 2017)

My motivation for studying the topic of food insecurity and food deserts is through previous study and discussion in several other classes I’ve taken in my time at university. Such classes such as Sustainable Cities, International Environmental Problems, Waste and Society, Human Biology, and others have elaborated on the topic of food deserts and food insecurity. Reliable access to healthy options for people of all demographics (income, race, age, location, ability) should be a right, not a privilege. According to Eau Claire’s most recent Community Health Assessment, obesity is one of the top three health priorities among Eau Claire residents who contributed to the Community Conversations discussion. (County 2014-2015)

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Methods

Parameters

The parameters I chose for this project were influenced by the USDA Atlas and include geographic and socioeconomic factors. I defined by geographic factors as low access to a grocery store, which is quantified by distance to a grocery store over a mile. I defined the socioeconomic factors as low income (around $42,314; under 80% median Wisconsin income) and low access to transportation (tracts with over 100 households with no access to a vehicle and are over 1/2 mile from food.)

Data Sources

My sources for my food locations came from Google Maps, using several search queries to find all reasonable locations for quality food. I collected data from the U. S. Census Bureau's 2012-2016 American Community Survey 5-Year Estimates for the income/economic characteristics. I also used the U.S. Census Bureau's 2013-2017 American Community Survey 5-Year Estimates for vehicular access data. I also used data previously collected on the topic from a SNAP resource to find locations of businesses with grocery options that accept SNAP.

Limitations

Limitations to my analysis include the quality of Census data, online data presence (or lack thereof) of farmer's markets, produce exchanges, or community/home gardens, seasonality of produce available, and the fact that the issue has multiple factors.

Census data in it of itself has some flaws. Census data is only a representative sample of the resident population and depends on community participation. This may exclude people who opt out of participating out of fear of government interference in the case for immigrant families or other personal reasons. Given that, not all demographics are accurately represented in Census data.

Luckily, there are plenty of farmer's markets, community gardens, and personal home gardens around Eau Claire. However, those are more difficult to get accurate spatial data from, as access and participation may vary according to season and location. I chose to not include these from my analysis but hope to further my research at some point in the future to include them.

I did my spatial analysis as distance from grocery stores to residences, as those locations are available in Census data. I would have liked to include workplaces or other key places in families' lives, as people may go grocery shopping while running other errands as well. Places such as routes to day cares, schools, or workplaces may influence a person's produce purchasing patterns and access as well. This data is not available for a good reason. Ethically, it could be considered too invasive of a study, despite the valuable output that may result from such data.

Another limitation to my analysis was that I only geocoded for food locations in the city of Eau Claire, for time's sake. Further analysis into the entire county would need updated data on food locations outside of the city.

Techniques

I used several techniques to complete my spatial analysis. First, I searched for food locations, inputted them into an Excel spreadsheet, and exported it as a .csv file. Once I had that, I could go in and geocode the addresses in ArcMap to find their correct spatial references. I then created a buffer of 1 mile from each location to find census tracts that had low access to grocery stores. I imported my economic and vehicular data tables into ArcMap and joined them to the Eau Claire County tracts. Then, I classified and coded each tract.

Classifying/Coding

I classified each tract using binary coding for the geographic and socioeconomic factors. A 0 classification represented a tract not significantly influenced by a specific factor. A 1 classification represents a tract that meets the criteria for a specific factor. Once I had all my tracts in my study area coded, I could identify which areas are impacted by each factor.

Initial coding results in the slideshow below

Multifactorial Analysis

Then, I added up the results from my previous analysis to find tracts that met at least two of my quantifying variables. These tracts in the maps slideshow below are considered vulnerable to food desert factors.

Results

The results of my final analysis show that Census tracts 600, 1200, and 1400 are most impacted by the combination of factors mentioned earlier. Areas that fit all three criteria: more than a mile from a grocery store, contain a low median household income and also contain residences that have low reliable access to transportation. These tracts are most vulnerable to the effect of the food desert phenomenon. If the City of Eau Claire were to implement a new grocery store, somewhere between these tracts would be an ideal location. My analysis shows that over 60% of Eau Claire County's tracts are considered low impact to food desert factors.

Discussion

For further analysis, I would like to answer more questions that came up when I was working on the project, as well as brought to my attention following my presentation. I would like to investigate how we can make healthy food more accessible to those in need, especially in tracts 600, 1200, and 1400. Looking into further demographic data to find specific populations that may be at more of a risk from health issues attributed to access to healthy food such as the elderly, disabled persons, single parents, unemployed persons, or undocumented persons could be beneficial. However, certain levels of integrity are necessary to remain ethical. I could also impose a network layer for further distance and access analysis including bus routes and walkable roads.

Conclusion

Food deserts are continually an issue that we as a society are still facing in the twenty-first century, despite advances in so many other areas of life. Basic human needs such as access to healthy food remain largely unaddressed in impoverished areas across the country. By using spatial analysis, we can make a difference by finding areas lacking access and provide a new resource for such necessities.

References

2010. American Nutrition Association: Nutrition Digest Vol. 38, No. 2. Accessed 9 18, 2018. http://americannutritionassociation.org/newsletter/usda-defines-food-deserts.

County, Eau Claire. 2014-2015. 2014-2015 Community Health Assessment. Chippewa and Eau Claire Counties: Chippewa and Eau Claire Counties Community Health Assessment Partnership. http://eauclaire.wi.networkofcare.org/content/client/1148/2015-Eau-Claire-County-Community-Health-Assessment.pdf.

Dowd, Andrew. 2016. "Heart of Eau Claire Drawing not one but two Grocery Stores." Leader-Telegram.

2014. Food Atlas Data Documentation. Economic Research Service: United States Department of Agriculutre.

Gundersen, A, E Engelhard, M Kato, A Crumbaugh, and A Dewey. 2018. Map the Meal Gap 2018: A Report on County and Congressional District Food Insecurity and County Food Cost in the United States in 2016. Feeding America.

Ploeg, Michele Ver, and Vince Breneman. 2017. Economic Research Service. https://www.ers.usda.gov/data-products/food-access-research-atlas/go-to-the-atlas/.

Rhone, Alana, Ver Michele Ploeg, Chris Dicken, Ryan Williams, and Vince Breneman. 2017. Low-Income and Low-Supermarket-Access Census Tracts, 2010-2015. United States Department of Agriculture, 21.

US Census Bureau. 2018. "2012-2016 American Community Survey 5-Year Estimates." American FactFinder.

Vince, Breneman, Alana Rhone. 2017. Food Environment Atlas. September 2017. https://www.ers.usda.gov/data-products/food-environment-atlas/go-to-the-atlas/.