Browsing by Author "Wilson, Ron"
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Item Calculating Varying Scales of Clustering Among Locations(U.S. Department of Housing and Urban Development, 2018) Wilson, Ron; Din, AlexanderThe Nearest Neighbor Index (NNI) is a spatial statistic that detects geographical patterns of clustered or dispersed event locations. Unless the locations are randomly distributed, the distances of either clustered or dispersed nearest neighbors form a skewed distribution that biases the average nearest neighbor distance used in calculating the NNI. If the clustering or dispersion of locations is moderate to extreme, the NNI can be inaccurate if the skew is substantial. Using Housing Choice Voucher program residential locations, we demonstrate in this article the method to derive an NNI based on a median and two quartiles that more accurately represents the midpoint of a set of nearest neighbor distances. We also demonstrate how to use these alternative point estimates to gauge multiple scales of clustering from different positions across the nearest neighbor distance distribution. Finally, we discuss how to use the average and standard deviation distances from the calculation of each NNI to more comprehensively gauge the scale of the geographic patterns. We also include a Python program that creates a randomized set of locations to calculate statistical significance for the median and quartile NNIs.Item Crosswalking ZIP Codes to Census Geographies: Geoprocessing the U.S. Department of Housing & Urban Development’s ZIP Code Crosswalk Files(U.S. Department of Housing and Urban Development, 2020) Din, Alexander; Wilson, RonAlthough ZIP Codes are a commonly used geographic unit for mapping and spatial analysis, they frequently distort data (Beyer, Schultz, and Rushton, 2007; Cudnick et al., 2012; Dai, 2010; Grubesic and Matisziw, 2006; Hipp, 2007; Krieger et al., 2002; Montalvo and Reynal-Querol, 2017; Wilson, 2015). ZIP Codes are designed for efficient mail delivery, not for geographic analysis. The large area that ZIP Codes cover make them susceptible to data aggregation problems that corrupt local geographic patterns. Due to the irregular—and often contorted—shapes of ZIP Codes, smaller geographic boundaries are ignored when overlain with smaller geographies, to which population counts are disproportionally distributed between the multiple areas that are cross-cut. The U.S. Department of Housing and Urban Development (HUD) provides several crosswalk files to estimate incident counts at different geographic scales from data at the ZIP-Code level.Item HUD Crosswalk Files Facilitate Multi-State Census Tract COVID-19 Spatial Analysis(U.S. Department of Housing and Urban Development, 2021) Din, Alexander; Wilson, RonThe coronavirus COVID-19 has infected millions of Americans. Datasets like the national county-level aggregation of COVID-19 case counts that Johns Hopkins University & Medicine assembled have been widely used, but few analyses have been performed at the local level due to the low supply of data. Like many things American, the distribution of COVID-19 data varies due to differing state, county, and local government reporting policies. The result is a patchwork of COVID-19 data at the local level, mostly aggregated to ZIP Codes due to ease of data processing rather than census tracts which are a better geographical unit for analysis. Local level COVID-19 data are rare and often only available for small areas. In this article, we demonstrate how the U.S. Department of Housing and Urban Development (HUD) Crosswalk Files can be used to assemble a census tract-level dataset of COVID-19 case rates in the Washington, D.C. Metropolitan Statistical Area across multiple states.Item Measuring Distance to Resources(U.S. Department of Housing and Urban Development, 2017) Wilson, Ron; Din, AlexanderMapping counts or rates of residents by areal geographies is useful for visualizing distributions across regions. However, this approach limits the understanding of resource proximity to visual approximations. Taking advantage of exact location information in a geographic information system (GIS), direct proximity statistics can be created by geoprocessing residence locations to population centers. In this article, we demonstrate how to geoprocess location information to create a table of the distances between resident locations and the nearest population centers to gain a more precise understanding of how far people live, as groups, from their closest resource centers.Item Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files(Office of Policy Development and Research (PD&R) U.S. Department of Housing and Urban Development, 2018) Wilson, Ron; Din, AlexanderZIP Codes are commonly used for mapping, spatial analyses, creating tables, or other reporting products. Used for these tasks, the results from using these geographies often are distorted because of adverse statistical properties inherent with ZIP Codes. Summarizing ZIP Code data to other large geographies (for example, county, Core Based Statistical Area, state) associates them with these other geographies to create aggregate counts so that metropolitan or county rankings can be reported. This process requires ZIP Codes to be properly allocated to these other geographies to accurately associate a record with that area. Although some companies or government organizations already provide a crosswalk to these geographies, the allocation method used is unclear, leaving it indiscernible as to the accuracy of the assignment of ZIP Codes. In this article, we demonstrate how to use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files to more directly control the ZIP Code allocation process of records to alternative geographies. In meeting this objective, we also provide results of an analysis using the HUD Crosswalk File in associating a ZIP Code with U.S. counties.Item Using HUD Crosswalk Files to Improve COVID-19 Analysis at the ZIP Code and Local Level(U.S. Department of Housing and Urban Development, 2020) Din, Alexander; Wilson, RonAs the novel coronavirus disease (COVID-19) continues to infect, harm, and kill thousands of Americans, many jurisdictions and institutions are publishing data at the ZIP Code-level, including counts of tests performed, people infected, hospitalizations, and deaths. These data are leading to quickly produced publications with strong conjectures about the forming of geographic patterns. We present an alternative to ZIP Codes when working with local COVID-19 data.