Compilation of the grocery store layer included referencing several online sources plus verifying the information in Google Streetview. Online sources included Yellow Pages, Manta, and Google Maps which turned out not to be very reliable. Two grocery stores were shown in completely wrong locations. This was verified by personal knowledge and double-checking street view. Verifying grocery stores in the outlying rural areas involved more research to verify whether they were full-service or convenience stores (street view, store websites or descriptions, and reviews were used). Grocery stores outside the study area were included, because of the 10-mile marker used to define rural food deserts. (http://www.ers.usda.gov/data-products/food-access-research-atlas/documentation.aspx) Addresses for the grocery stores were tabulated in Excel, saved as csv file, and geocoded before performing the Near analysis to determine which urban census tract centroids exceeded the 1-mile marker and which rural census tract centroids exceeded the 10-mile distance from a grocery store.
Centroids of the census tracts were calculated to provide a comparison point for the one-mile marker food desert determinant (urban) and 10-mile marker (rural). Interestingly, the centroid for one of the rural census tracts landed inside one of the city census tracts. Both city census tracts were considered to be food deserts which surprised me. If one of the grocery stores had been located on the east end of town or farther south, at least one of the city census tracts probably would not have been classified as a food desert. The grocery stores are both located near the western edge of town (and extreme NW part of the larger urban census tract). Because of the town size and number of grocery stores, this type of analysis might not be as informative as anticipated. Perhaps an evaluation by census block would be more appropriate to really define the at-risk areas, or an assessment by residents’ age and mobility/transportation issues could be done. I was surprised that at least one of the two southern rural census tracts was not a food desert. However, the grocery stores in that area are of a smaller nature and do not carry stock nearly as comprehensive as a chain grocery store would. So while not technically food deserts, these rural areas might still be prone to a lower-quality diet unless supplemented with home-grown produce.
The process of creating a web map of food deserts included tiling the shapefiles described above. Tiling a shapefile enables more rapid loading of layers into a web map because the layers are displayed in smaller portions (tiles). In TileMill each of the data layers for this map was centered in a tight-fitting bounding box with a centralized marker. The zoom level was adjusted so that the resulting tiles would be 10MB. Each of these map layers had up to 19 tiles. The default world layer was removed by editing the code, and the symbology (colors, opacity) for the layers was adjusted as needed. Each layer was exported for use in Mapbox and by default was sent off to a folder not of my choosing. I opted to re-organize those layers into project folders prior to adding them to the map in Mapbox. The method of saving layers or projects in both TileMill and Mapbox was not very intuitive for me (save/don't save, save again, export, etc.), so several times some steps had to be repeated in order to ensure that I had the desired product. As so often happens, this was just enough exposure to new software to let me know how much there is to learn! The screenshot below shows the food deserts, census tract boundaries, and grocery stores added to the revised basemap in Mapbox.
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