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«Urban Problems and sPatial methods VolUme 17, nUmber 1 • 2015 U.S. Department of Housing and Urban Development | Office of Policy Development and ...»

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Acknowledgments The author thanks the multitude of student researchers for their contributions in data collection throughout the multiple parts of the project and acknowledges the financial support from the Laramie Main Street program.

Author William J. Gribb is a professor and director of the graduate program in planning in the Geography Department at the University of Wyoming.

References Albany County, Wyoming Assessor’s Office. 2013. “Albany County, WY Parcel Data File.” http:// www.co.albany.wy.us/gis-map-property-data-download.aspx.

Bergman, David, ed. 1991. Off-Street Parking Requirements: A National Review of Standards. Chicago:

American Planning Association.

Birch, Eugenie L. 2009. “Downtown in the ‘New American City,’” Annals of the American Academy of Political and Social Science 626 (Nov): 134–153.

City of Laramie. 2007. City of Laramie, WY Comprehensive Plan, 2007. http://www.cityoflaramie.org/ index.aspx?nid=230.

Cook, Edward A., and Marion T. Bentley. 1986. Revitalizing the Small Town Mainstreet: Hard Times, Communities in Transition. WREP-92. Corvallis, OR: Oregon State University, Cooperative Extension Service, Western Rural Development Center.

82 Urban Problems and Spatial Methods 3-D Residential Land Use and Downtown Parking: An Analysis of Demand Index Doner, Fatih, and Cemal Biyik. 2011. “Modelling and Mapping Third Dimension in a Spatial Database,” International Journal of Digital Earth 4 (6): 505–520.

Edwards, John D. 1994. Parking: The Parking Handbook for Small Communities. Washington, DC:

National Main Street Center.

Faulk, Dagney. 2006. “The Process and Practice of Downtown Revitalization,” Review of Policy Research 23 (2): 625–645.

Ferguson, Gary. 2005. Characteristics of Successful Downtowns: Shared Attributes of Outstanding Small and Mid-Sized Downtowns. Unpublished manuscript. Ithaca, NY: Cornell University Civic Fellows Program.

Filion, Pierre, Heidi Hoernig, Trudi Bunting, and Gary Sands. 2004. “The Successful Few: Healthy

Downtowns of Small Metropolitan Regions,” Journal of the American Planning Association 70 (3):

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Glaeser, Edward. 2012. Triumph of the City: How Our Greatest Invention Makes Us Richer, Smarter, Greener, Healthier, and Happier. London, United Kingdom: Penguin Books.

Greenfield, Beth. 2012. “America’s Fastest-Growing Small Towns,” Forbes, January 23, 2012.

http://www.forbes.com/sites/bethgreenfield/2012/01/23/fastest-growing-small-towns/.

Gribb, William J. 2007. Parking Inventory and Analysis, Laramie, Wyoming, 2006. Laramie, WY:

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Leinberger, Christopher B. 2005. Turning Around Downtown: Twelve Steps to Revitalization. Research Brief. Washington, DC: The Brookings Institution, Metropolitan Policy Program.

Litman, Todd. 2006. Parking Management Best Practices. Chicago: APA Press.

Manville, Michael. 2013. “Parking Requirements and Housing Development,” Journal of the American Planning Association 79 (1): 49–66.

Mukhija, V., and D. Shoup. 2006. “Quantity Versus Quality in Off-Street Parking Requirements,” Journal of the American Planning Association 72 (3): 296–308.

Robertson, Kent A. 1999. “Can Small-City Downtowns Remain Viable? A National Study of Development Issues and Strategies,” APA Journal 65 (3): 270–283.

Rypkema, Donovan D. 2003. “The Importance of Downtown in the 21st Century,” Journal of the American Planning Association 69 (1): 9–15.

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Shields, Martin, and Tracey Farrigan. 2001. A Manual for Small Downtowns. University Park, PA:

The Pennsylvania State University, Center for Rural Pennsylvania.

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———. 2013. “North American Industrial Classification System, 2012.” https://www.census.gov/ cgi-bin/sssd/naics/naicsrch?chart=2012.

van Ommeren, Jos, Derek Wentink, and Jasper Dekkers. 2011. “The Real Price of Parking Policy,” Journal of Urban Economics 70: 25–31.

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84 Urban Problems and Spatial Methods Rethinking Food Deserts Using Mixed-Methods GIS Jerry Shannon University of Georgia Abstract Food deserts—low-income neighborhoods with poor access to affordable, healthy food— have increasingly been seen as a driver of obesity and related health conditions in urban neighborhoods. Most current research uses an approach based on a Geographic Information System, or GIS, to identify food deserts using store locations, but data that link food environments to health outcomes have been inconsistent. This article outlines an alternative methodology that shifts from the proximity of healthy food stores to the foodprovisioning practices of neighborhood residents. Using a mixed-methods approach, this research relies on several data sources: (1) geographic tracking on daily mobility created using Global Positioning System, or GPS, software on a smartphone, (2) georeferenced photographs also created using smartphones, (3) food-shopping diaries and store receipts, and (4) semistructured qualitative interviews. The resulting analysis identified how factors ranging from perceived neighborhood disorder to available transit options shape decisions about how and where to get food. By more explicitly focusing on the foodprovisioning strategies of low-income households and the factors that shape them, this research suggests potential pathways toward healthier, more livable cities.

Introduction British researchers first popularized the term food desert in the mid-1990s (Cummins and Macintyre, 1999; Wrigley, 2002). Since that time, it has become an increasingly common way to refer to neighborhoods where nutritious foods—most often defined as fresh produce and meats— are unavailable, of poor quality, or overly expensive. In the United States, several policy initiatives have been based on this research. Pennsylvania’s Fresh Food Financing Initiative, which began in 2004, was one major response to this research, providing grants and loans to improve food-related infrastructure in areas with low food access (Pennsylvania Fresh Food Financing Initiative, 2014).

Many of these funds were used to expand or create new supermarkets. President Barack Obama expanded this model at the federal level by creating the Healthy Food Financing Initiative (HHS, Cityscape 85 Cityscape: A Journal of Policy Development and Research • Volume 17, Number 1 • 2015 U.S. Department of Housing and Urban Development • Office of Policy Development and Research Shannon 2010). Along with the creation of these federal and state programs, several U.S. cities have created initiatives to improve food access in low-income neighborhoods, including the creation of a food policy task force by the U.S. Conference of Mayors (Boston Mayor’s Office, 2012).

Current research on food deserts primarily makes use of an approach based on Geographic Information Systems (GIS)-based analysis that relies on the proximity of supermarkets to residential areas (Black, Moon, and Baird, 2014; Caspi et al., 2012). This methodology is conceptually clear and relatively easy to implement. It requires census data and a listing of major food retailers, both widely available, in addition to data on health outcomes such as body mass index, or BMI, or reported food consumption. Recent research shows little or no association between food deserts and these health outcomes, however, which puts into question the efficacy of this spatial analytical approach (Cummins, Flint, and Matthews, 2014; Lee, 2012).

This article describes an alternative methodology, one that moves from measures of food proximity to the food-provisioning practices of urban residents. This mixed-methods study combines Global Positioning System (GPS) data on daily mobility, food-shopping diaries, georeferenced photos, and semistructured qualitative interviews. It identifies the role of other major factors affecting food access, including perceived neighborhood disorder and store quality, the role of social networks, and the effect of available transit options. In contrast to approaches that privilege only objective analysis of geospatial data, this method is also more explicitly participatory, including the voices and perspectives of urban residents. It thus provides a useful lens on the daily food provisioning of urban households and the factors that shape them.

Measuring Food Access Early research on food deserts mostly used market-basket studies, comparing the availability and price of goods across store types and neighborhoods (Block and Kouba, 2007; Cummins and Macintyre, 2002; Hendrickson, Smith, and Eikenberry, 2006). This research often documented discrepancies in food price and quality between lower and middle-to-upper-class neighborhoods.

This labor- and time-intensive research limits analysis, however, because it usually assesses only a small number of neighborhoods. As a result, spatial analysis of food-store distribution across urban areas has become increasingly common (Apparicio, Cloutier, and Shearmur, 2007; Zenk, et al., 2005). In this approach, proximity to healthy food sources—most often supermarkets—is combined with measures of social deprivation, such as poverty level, racial composition, and/or vehicle access.

The U.S. Department of Agriculture’s (USDA’s) own Food Access Research Atlas is arguably the most widely used example of this approach (USDA Economic Research Service, 2014). This online tool1 provides an interactive national map showing the locations of all low-access, low-income census tracts, which are defined using only two variables: poverty level and distance to the nearest supermarket.

Spatial analytical approaches enjoy wide usage and reflect increasingly common use of geospatial data in “smart city” approaches to urban governance (Townsend, 2013). The relationship between neighborhood store environment and health and dietary outcomes is a tenuous one, however (Caspi et al., 2012). Some studies have shown that distance from place of residence to food stores http://www.ers.usda.gov/data-products/food-access-research-atlas.aspx.

–  –  –

is associated with food consumption habits (Gustafson et al., 2013; Hutchinson et al., 2012). Several studies have found little or no link between the two characteristics (Boone-Heinonen et al., 2011; Lee, 2012). One recent well-publicized study examined changes in residents’ eating habits in a neighborhood targeted by the Pennsylvania Fresh Food Financing Initiative. Although residents were aware of their new neighborhood supermarket, their shopping and eating habits did not change significantly as a result (Cummins, Flint, and Matthews, 2014).

The inconsistency of these results questions the reliability of distance-based measures that rely on place of residence as a sole predictor of food-provisioning and consumption habits. Indeed, other studies have shown how distance is only one factor shaping food provisioning. In the late 1990s, USDA-sponsored research found that the supermarkets that food stamp clients used were more than twice as far from home as the closest supermarket (USDA Economic Research Service, 2009).

Subsequent research has also demonstrated that low-income households often purchase food from stores outside their home neighborhoods (Clifton, 2004; Ledoux and Vojnovic, 2012; Shannon, 2014).

Aside from distance, numerous other factors shape decisions about how and where to get food, including cultural preferences, perceptions of neighborhood safety, and store quality (Latham, 2003;

Sampson, 2012; Zenk et al., 2011). By developing data sources that illustrate how individuals make use of urban food systems, rather than simply mapping the locations where food is available, research on food access can better identify neighborhood- and metropolitan-level factors that shape the ways residents procure food. The research outlined in this article provides a model of one such approach.



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