The purpose of this project is to evaluate the characteristics and GIS methods used to determine an urban food desert. The intention of this research is to implement an improved methodology for determining a food desert and exhibit the value of additional data sources. This should produce a universal GIS method and criteria for profiling a food desert in an urban setting across the United States.https://engagedscholarship.csuohio.edu/u_poster_2012/1034/thumbnail.jp

Mikelbank, Brian

Russell, Monique N.

English

Cleveland-Marshall College of Law

Cleveland State UniversityEngagedScholarship@CSUUndergraduate Research Posters 2012 Undergraduate Research Posters9-6-2012Defining and Analyzing a Food DesertMonique N. RussellCleveland State University, M.N.RUSSELL48@csuohio.eduBrian MikelbankCleveland State University, b.mikelbank@csuohio.eduFollow this and additional works at: https://engagedscholarship.csuohio.edu/u_poster_2012Part of the Health Policy Commons, and the Urban Studies and Planning CommonsHow does access to this work benefit you? Let us know!This Book is brought to you for free and open access by the UndergraduateResearch Posters at EngagedScholarship@CSU. It has been accepted forinclusion in Undergraduate Research Posters 2012 by an authorizedadministrator of EngagedScholarship@CSU. For more information, pleasecontact library.es@csuohio.edu.Recommended CitationRussell, Monique N. and Mikelbank, Brian, "Defining and Analyzing a Food Desert" (2012). Undergraduate Research Posters 2012. 36.https://engagedscholarship.csuohio.edu/u_poster_2012/36This digital edition was prepared by MSL AcademicEndeavors, the imprint of the Michael Schwartz Library atCleveland State University.Defining and Analyzing a Food Desert Monique Russell, Brian Mikelbank⃰, Ph.D. Cleveland State University, Cleveland OH 44115                   INTRODUCTION                       A food desert is a term to describe areas that are separated from food venues where the residents experience physical and economic barriers to accessing healthy food (Reisig and Hobbiss, 2000, as referenced in Sparks et al., 2009). The term “food desert” was first used when a resident in public housing expressed their view of what it was like to live in an area where food was costly and geographically not tangible (McEntee 2010). The topic of food deserts has been the talk of researchers in the GIS world very recently. Many aspects of this topic have been touched upon, such as, what are the health effects of a food desert, what is a reasonable walking distance considering the weight of the groceries and the physical capability of the person, and comparing methods of evaluating a food desert.                   While researchers have looked at a comparison of network analysis and straight-line distance using a grocery store or a fast food restaurant as a reference point, no research known to the author has considered food pantries, urban gardens, and farmers markets to see if a classification of a food desert or non-food desert varies based on these additional, but periodic food sources. In addition to this, no one has explored if this trend is true for the area of Cuyahoga County in Ohio. In the process of reviewing the literature, comparisons in certain guidelines for an urban food desert were examined, such as, what was the walking distance used in all the studies, what level did they measure this (census tracts, census blocks or 1 Kilometer grids), to which food venue did they measure (supermarkets, class 1 and 2 stores, etc), and what were the characteristics of the results (vehicle availability, demographics, etc.).            METHODS                  In ARC GIS, there are various methods for mapping and finding the distance between different locations. The most common methods are network analysis and straight-line (Euclidean) distance. Network analysis is a method of ARC GIS mapping that takes the address of a particular location and travels along the street network to get to a certain destination. Straight-line or Euclidean distance is a method of ARC GIS mapping where the length of the straight distance between the initial and ending location is measured, without the consideration of the street network.   Comparing these two methods, network analysis seems to be the best method for mapping because it is more realistic. Straight-line distance uses a radius and does not take into consideration the constructed or natural barriers that get in the way of a pedestrian getting to a food venue. Those barriers could be lakes, rivers, buildings, highways, etc.                  The first step in this process was to replicate the methods used by the Cuyahoga County Food Policy Coalition.  This was done by calculating the straight-line distances from the centroid of each Cuyahoga County census tract to Class 1 and Class 2 food venues. Census tracts were used for consistency, since the CCFPC used them. One should note that when comparing data from performing the network analysis, including the Class 1 and Class 2 food stores, food pantries, markets, and gardens, we are not using the values to declare the census tracts as a food desert or non-food desert, we are simply describing whether or not those areas are provided with relief when adding those food additional food venues.     RESULTS AND DISCUSSION                                                                 The data presented in Table 1 compare straight-line and network analysis methods and categorizes the results based on the characteristics of the tracts. When performing the straight-line distance analysis, only Class 1 and Class 2 stores were used because this was replicating the methods used by the CCFPC. The results that were obtained from doing the straight-line analysis in this project showed that 101 census tracts were food deserts, out of the 446 census tracts in the county. In doing the network analysis, the results showed that 135 census tracts were food deserts; 34 more census tracts than the straight-line analysis. Simply changing the methodology of the distance calculation increased the number of Cuyahoga County food deserts by roughly one third.  Since the network analysis seems a more appropriate method for urban settings, this method and its results were used throughout the rest of the project.                    Network analysis was performed again, but this time adding food pantries, markets, and urban gardens. The results showed that 94 census tracts out of the original 135 census tracts that were deemed a food desert were provided with some relief from one or more of these food venues. This finding is one of the highlights of our analysis – food pantries and harvest locations are collectively providing food relief in nearly 70% of the county’s food desert neighborhoods.      CONCLUSION                      In conclusion to this study, network analysis is a better way to map out areas that are food deserts. Network analysis uses streets to find the closest facility from a certain location. With this in mind, this is the most logical way to present data that will help the general population.  Adding food pantries, markets, and urban gardens suggests that with the effort of providing these additional food venues, there is some food relief in desert areas. These data can also help in figuring out where more food venues need to be located in areas that still need food relief.                     Although the results were not complete for the poverty rate and percent of households that have no vehicle, there is a reasonable cause to say that food deserts are located in areas that have a higher poverty rate and higher rate of homes that have no vehicle available.   FUTURE RESEARCH                   Other characteristics of food desert areas are of interest for future research.  These might include crime, and condition/availability of the sidewalks. These variables are thought to affect the availability of food venue options for where citizens shop. Other variables that might affect our findings are the seasons. Although it was found that there is food relief in markets and urban gardens, these food venues are on a seasonal basis. Additionally, there might be eligibility and/or timing issues relating to food pantry access.  A focus on these issues would paint a clearer year-round picture of food access for Cuyahoga County neighborhoods.   ACKNOWLEDGEMENTS              Special thanks to the Cuyahoga County Planning Committee, Cuyahoga County Food Policy Coalition (CCFPC), Cleveland Foodbank, Cleveland State University's Urban College Computer  Labs, and TRIO McNair Scholars Program.          CITATIONS          U.S. Census Bureau . (2012, August 9). American Community Survey. Retrieved August 13, 2012, from http://www.census.gov/acs/www/              Agriculture, U. S. (2009). Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Consequences. Economic Research Service.              Agyeman, J., & McEntee, J. (2009). Towards the Development of a GIS Method for Identifying Rural Food Deserts: Geographic Access in Vermont, USA. Applied Geography, 1-12.               Cuyahoga County Food Policy Coalition. (2011). Cuyahoga County Assessment: Access to Supermarkets. Cleveland, OH.               Dutko, P., Ver Ploeg, S., & Farrigan, T. (n.d.). Retail Wastelands: Characteristics and Influential Factors of Food Deserts.               Gallagher, M. (2006). Good Food: Examining the Impact of Food Deserts on Public Health. Chicago, IL: Mari Gallagher Research & Consulting Group.               Leete, L., Bania, N., & Sparks-Ibanga, A. (2012). Congruence and Coverage: Alternative Approaches to Identifying Urban Food Deserts and Food Hinterlands. Journal of Planning Education and Research, 204-218.               Opfer, P. (2010). Using GIS Technology to Identify and Analyze 'Food Deserts' on the Southern Oregon Coast.              Sparks, A., Bania, N., & Leete, L. (2009). Finding Food Deserts: Methodology and Measurement of Food Access in Portland, Oregon. National Poverty Center/USDA Economic Research Service, (pp. 1-26). Washington DC.               U.S Census Bureau. (n.d.). 2010 Census Data. Retrieved August 13, 2012, from http://2010.census.gov/2010census/data/               U.S. Census Bureau, Geography Division. (2010). TIGER Line Files. Cuyahoga County, Ohio, USA. http://www.census.gov/geo/www/tiger/      For more information, contact Monique Russell at m.russell_09@live.com                      Type of Tract Number of Tracts Poverty Rate % of HH with no Veh Avail Straight-line distance food deserts 101 0.351 0.259 Food deserts added by using network distance 34 0.338 0.249 All network distance food deserts 135     Network distance food deserts with relief 94     Network distance food deserts without relief 41     High poverty non-food desert tracts 40     Tracts with poverty rates less than 20% 264 0.08 0.07 Tracts with no poverty data reported 7     TABLE  1 

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