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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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Smartphones were a key technology in the study. They combined GPS tracking with the ability to take georeferenced photographs, both of which could be monitored in real time by syncing data through the phone’s data plan to ensure I was aware of any ongoing technical problems. The phones could also be used to collect food-shopping diary entries from study participants, although only one participant did so in this case. Phones had sporadic technical problems. Finding reliable software to collect GPS data was one obstacle. Future research using this approach should confirm the availability of suitable software or consider budgeting for the development of a custom application. The inclusion of photographs in this study was useful, but many photographs were unfortunately of low quality (were blurry or had poor lighting). Phones with better cameras may have 92 Urban Problems and Spatial Methods Rethinking Food Deserts Using Mixed-Methods GIS improved image quality through technologies like image stabilization. This project did not explicitly use the photo-voice method (Belon et al., 2014; Mahmood et al., 2012), but providing more instruction on how to use the cameras and encouraging participants to capture defining features of each food source would have borrowed elements of this approach that may have improved photo quality and usefulness.

The analysis of food environments is only one aspect of increasing reliance on spatial analytics in “smart city” initiatives (Townsend, 2013). This data-based approach to urban governance provides valuable insight on how policy initiatives can alter patterns of everyday life. As even this smallscale study suggests, however, the idea that a reliance on robust quantitative indicators produces a theory-free form of governance is fundamentally problematic (Anderson, 2008). The qualitative data developed in this project were essential to interpretation of both shopping diaries and GPS data. Through study interviews and focus groups, participants and I developed a shared understanding of the factors that shaped their food-provisioning behavior. The situated perspectives of neighborhood residents, not just their data, were needed (Haraway, 1988; Pavlovskaya and St.

Martin, 2007). This mixed-methods approach thus provides one model for a participatory geospatial analysis of food access.

The growth of mobile GIS applications provides new opportunities for representing and understanding the everyday practices that form the rhythm of urban life. Future research can leverage this technology by exploring not just where food is, but where, how, and why urban residents draw on available food sources. This approach might use a smartphone application that city governments or nonprofit groups would custom design for their research. Targeted case studies in selected neighborhoods, similar to this project, might also prove useful. This small-scale study also generates broader research possibilities, such as analysis of existing transit data or citywide surveys of perceived store quality and neighborhood safety. Using this mix of methodologies to understand the everyday experiences of urban residents can thus suggest pathways to healthier, more livable cities.

Author Jerry Shannon is a limited-term assistant professor in the Department of Geography at the University of Georgia.

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Black, Christina, Graham Moon, and Janis Baird. 2014. “Dietary Inequalities: What Is the Evidence

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Shannon, Jerry. 2014. “What Does SNAP Benefit Usage Tell Us About Food Access in Low-Income Neighborhoods?” Social Science and Medicine 107: 89–99. DOI: http://dx.doi.org/10.1016/ j.socscimed.2014.02.021.

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African American Neighborhood,” Health Education and Behavior 38 (3): 282–292. DOI:

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DOI: 10.2105/AJPH.2004.042150.

96 Urban Problems and Spatial Methods

Spatializing Segregation Measures:

An Approach To Better Depict Social Relationships Masayoshi Oka University of Alcalá David W.S. Wong University of Hong Kong George Mason University Abstract Segregation involves more than one population group, and segregation measures quantify how different population groups are distributed across space. One of the key conceptual and methodological foundations of segregation studies is to account for the potential of spatial interaction among two or more population groups across areal units. This foundation implies the need for a spatial approach to portray the spatial (and thus social) interaction among neighbors. In general, simple percentages (for example, percent Black) are not a measure of segregation. Because local spatial segregation measures did not emerge until recently, the objectives of this article are threefold: (1) to explain a spatial approach for measuring the level of segregation at the neighborhood (or local) level, (2) to demonstrate the deficiencies of using a percentage of racial/ethnic group as a measure of segregation, and (3) to clarify the appropriateness of two commonly used indexes of dissimilarity and diversity. Data from St. Louis, Missouri, and Chicago, Illinois, are used to discuss these three points.



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