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Abstract BackgroundProximity to food sources is one of the quantifiable factors measurable across space impacting diet-related health outcomes. Contemporary research has coined the terms ‘food desert’ and ‘food swamp’, sometimes combined with a poverty component, to highlight disproportionate access to healthy and unhealthy food sources. However, there are various ways to measure this proximity—i.e., food availability in this research. Dollar stores such as Dollar General, Family Dollar, and Dollar Tree are one emerging facet of the food environment that provides healthy and unhealthy food options yet have not fully been studied. With more ways to easily measure food availability within the confines of a GIS, this paper proposes a new raster-based Point Density metric to measure the availability of these Dollar stores. In this study, this raster-based metric was calculated for a 6-county region in central North Carolina and compared to six other availability metrics utilized in food security research. A novel Python-based tool to compute the Jaccard Index between these various availability metrics and a matrix to compare these pairwise Jaccard Index calculations was created for this raster-based metric, which is very easy to derive. ResultsUsing a pairwise Jaccard Index summarized and then averaged in a correlation table, the Point Density measure rated the highest (.65) when compared to 6 other popular vector-based techniques. Our results showed the density metric performed statistically better than Euclidean distance, drive-time, density, and point-in-polygon vector metrics when measuring availability for Dollar stores in Central North Carolina. ConclusionsResults reinforce the efficacy of this easy-to-compute metric comparable to vector-based counterparts that require more robust network and/or geoprocessing calculations. Results quantitatively evaluate food availability with an eventual goal of dictating local, regional, and even state-level policy that critically and holistically consider this metric as powerful and convenient metric that can be easily calculated by the lay GIS user and understood by anyone. 

The National Agricultural Statistics Service, the statistical arm of the US Department of Agriculture, and the Multi-Resolution Land Characteristics Consortium, a group of the US federal agencies, collect and publish several land-use and land-cover data sets. The aim of this study is to analyze the consistency of forestland estimates based on two widely used, publicly available products: the National Land-Cover Database (NLCD) and Cropland Data Layer (CDL). Both remote-sensing-based products provide raster-formatted land-cover categorization at a spatial resolution of 30 m. Although the processing of the yearly published CDL non-agricultural land-cover data is based on less frequently updated NLCD, the consistency of large-area forestland mapping between these two datasets has not been assessed. To assess the similarities and the differences between CDL- and NLCD-based forestland mappings for the state of North Carolina, we overlay the two data products for the years 2011 and 2016 in ArcMap 10.5.1 and analyze the location and attributes of the matched and mismatched forestland. We find that the mismatch is relatively smaller for the areas of the state where forests occupy larger shares of the total land, and that the relative mismatch is smaller in 2011 when compared to 2016. We also find that a large portion of the forestland mismatch is attributable to the dynamics of re-growth of periodically harvested and otherwise disturbed forests. Our results underscore the need for a holistic approach to data preparation, data attribution, and data accuracy when performing high-scale map-based analyses using each of these products. 

Food desert communities face persistent barriers in accessing affordable fresh and healthy foods, particularly for the underserved and limited-resourced minority population. This research brief proposes an integrated design concept examining human-environment dynamics of food deserts to identify strategies that would provide effective planning to prevent, prepare for, or respond to disruptive events such as natural disasters or pandemics in the future. The North Carolina example we describe identifies the potential overlapping areas between food deserts and number of COVID-19 cases to demonstrate how an unpredictable event could exacerbate public health in food desert communities to a greater extent than in communities with better food access, availability, and accessibility. The improved understanding of food systems could help in addressing unprecedented challenges such as those due to the COVID-19 crisis.