The tensions of mixed methods

This research used a mixed methods approach, with the goal of drawing on the strengths of both quantitative and qualitative approaches. The two phases of my research project had several areas of agreement, including the mobility of low-income

populations, the relative underuse of neighborhood supermarkets, and the more

prominent role of mid-sized grocers within these neighborhoods. In each of these cases, these methods were strong complements for each other, with data on SNAP utilization providing evidence for broad scale patterns and case study research providing

explanatory factors explaining or better describing those patterns.

However, the goal of mixed methods work should also be to highlight “gaps and tensions between differing forms of knowledge” (Elwood, 2009, p. 99). Explicitly recognizing these tensions points to the limits of each approach and the epistemological blind spots and biases implicit in their use. One such tension in this project has been the need for store classification, common to GIS based analyses, against the recognition of substantial variation within each class. Classification was an essential part of my analysis of SNAP data, since creating store level estimates would have been impossible without it. In more conventional distance based analyses of access, store classification plays a similarly key role, with supermarkets (and sometimes smaller grocers) acting as proxies for healthy food access. In practice, however, I found that the dividing lines between store types were not always clear. How much produce and meat should a store carry in order to be classified as healthy? Are dollar stores, which often had several long aisles of processed foods, really equivalent to much smaller corner stores and gas stations? As my case study participants made clear, even supermarkets varied significantly from location

to location. This issue is even more acute when one considers other “gray spaces” rarely included in analyses of food access: community gardens, food trucks, and food shelves. Each of these sites may have widely varying selection and food quality. Balancing the need to classify and place value upon certain food sources against the recognition that these sources can and do vary was one vexing analytical and policy question arising through this research.

Another core tension between these approaches was the difference in view between, as de Certeau (2000) frames it, the skyscraper and the street. Data from the SNAP

program provides an invaluable perspective on spatial patterns of benefit utilization as a proxy for all food shopping. Similarly, when case study data demonstrate that public transit often plays a key role in the food shopping trips of individuals lacking a vehicle, it seems logical to suggest forming transit hubs around major food sources. These “top down” views of the urban food system contrasted with participant accounts of the often informal and unplanned ways people secured transportation or shopped for food. These trips were shaped significantly by social networks, which varied greatly in location and extent. This tension is familiar to those who have worked in public participatory GIS, where localized accounts are balanced against broad datasets, with varying degrees of commensurability. In practice, I have advocated for the need to hold these two views in tension—designing solutions based on broad data on current usage while also providing

more flexibility for grassroots design and food system building at the neighborhood scale. Still, this is another tension lacking easy resolution.

Lastly, this research navigates tensions between thinking of neighborhoods as territories or as nodes in a network. The former is a much more conventional view for GIS-based research, and in my analysis of SNAP data I found it necessary to draw boundaries around high use areas in order to understand what was going on within them. However, as both SNAP data and my case study data makes clear, these boundaries by no means contained residents or their daily activity. I asked each case study participant to outline what they saw as their neighborhood as part of our final interview, and these boundaries were seldom closely aligned with other participants or with my study. In addition, the stores within each study neighborhood obviously have connections to much larger networks of food production and distribution, making a focus only within

neighborhood boundaries highly problematic. That said, it was clear from my research interviews and in broader literature on health effects that neighborhoods do matter. Residents had definite opinions about nearby stores, whether they used them or not, and feelings about the neighborhood were a key factor shaping decisions about when and where to shop. Knowing how to balance the need to identify specific areas of analytical interest against a treatment of them as relational, networked, and dynamic is a task health geography and other fields is only beginning to take on (MP Kwan, 2012).

Each of these tensions lacks easy resolution. Certainly, even several decades since de Certeau wrote “Walking in the City” or Haraway wrote of the need for situated knowledge (Haraway, 1988), the analytic view made possible by broad scale data analysis retains its cultural and political power. This project is not intended to discount the value of this approach, but it does demonstrate the need to recognize its limits and balance it against other epistemologies.