Understanding the geographical distribution and correlates of special segments of the population has the potential for offering insight into human behavior. Our study examines the Emergency and Transitional Shelter Population (ETSP)—which includes what are commonly referred to as “homeless” people. We use 2010 data from two sources: United States (US) Census Bureau county-level ETSP estimates; and North America Land Data Assimilation System Phase 2 (NLDAS-2). We investigate the ecological correlates of ETSP concentration by using a geographically-aware multilevel linear model. The specific aim is to investigate if an how atmospheric temperature is related with ETSP concentration by county—after accounting for population density and percent non-Hispanic-White. We use ArcGIS® 10.1 to create a spatial weight matrix of the ten most proximal counties and use SAS® 9.3 to create an algorithm that estimates County Cluster Dyadic Averages (CCDAs). By nesting the 31,090 CCDAs over the 3,109 counties in the continental US, we find a positive and statistically significant relationship between ETSP density and atmospheric temperature. Ecological studies should continue to explore the spatial heterogeneity of the ETSP.
Social scientists investigating how context varies by geographical location and/or how macro-level phenomenon affects individual outcomes often make use of U.S. Census Bureau Public Use Microdata Sample (PUMS) files where micro-units can only be geographically located to Public Use Microdata Area (PUMA) polygons. Most spatial analysis investigations with PUMAs ignore the fact that many of them are multipart polygons—spatially separated polygons that share the same attribute and are stored as a single feature in a vector file. We briefly discuss the theoretical premises of how geo-graphical boundaries are created for macro units and investigate the quantity, degree, and location of PUMA fragmenta-tion. We argue that the basic contiguity principle (the assumption that spatial analysis uses polygon centroids for solid and contiguous geographic units) in spatial dependence analysis is being violated with many PUMAs in the U.S. mainland—where Texas, California, Tennessee, and Illinois merit special attention. Future research should outline a method for handling multipart polygons in spatial and hierarchical analyses.
Small area estimates on where services for potential Medicare beneficiaries may be needed, could provide unique research opportunities for improving the healthcare quality of the ageing U.S. population. The project described in this paper validates this argument by contrasting the spatial clustering results from an analysis that uses large geographical units with proxy measures to the results from an analysis using small area geographic units with direct measures. Large-area proxy measures come from county-level U.S. Census Bureau 2010 cross sectional data on the number of people aged 65 and over. Medicare beneficiary estimates in 2007 with Primary Care Service Areas (PCSAs) make up the small-area direct-measure analysis. Findings show that the latter offers a more geographically defined appraisal of where healthcare quality efforts should focus to aid potential Medicare beneficiary populations. Because the healthcare quality of an aging population will only increase in importance as their numbers grow in the US, further research is needed.
Type II diabetes is a growing health problem in the United States. Understanding geographic variation in diabetes prevalence will inform where resources for management and prevention should be allocated. Investigations of the correlates of diabetes prevalence have largely ignored how spatial nonstationarity might play a role in the macro-level distribution of diabetes. This paper introduces the reader to the concept of spatial nonstationarity—variance in statistical relationships as a function of geographical location. Since spatial nonstationarity means different predictors can have varying effects on model outcomes, we make use of a geographically weighed regression to calculate correlates of diabetes as a function of geographic location. By doing so, we demonstrate an exploratory example in which the diabetes-poverty macro-level statistical relationship varies as a function of location. In particular, we provide evidence that when predicting macro-level diabetes prevalence, poverty is not always positively associated with diabetes.