A Spatial Analysis of the Relationship between Vegetation and Poverty

Abstract

The goal of this paper was to investigate poverty and inequities that are associated with vegetation. First, we performed a pixel-level linear regression on time-series and Normalized Difference Vegetation Index (NDVI) for 72 United States (U.S.) cities with a population ≥250,000 for 16 Radiometer 1-kilometer (1-km). Second, from the pixel-level regression, we selected five U.S. cities (Shrinking: Chicago, Detroit, Philadelphia, and Growing: Dallas and Tucson) that were one standard deviation above the overall r-squared mean and one standard deviation below the overall r-squared mean to show cities that were different from the typical cities. Finally, we used spatial statistics to investigate the relationship between census tract level data (i.e., poverty, population, and race) and vegetation for 2010, based on the 1-km grid cells using Ordinary Least Squares Regression and Geographically Weighted Regression. Our results revealed poverty related areas were significantly correlated with positive high and/or negative high vegetation in both shrinking and growing cities. This paper makes a contribution to the academic body of knowledge on U.S. urban shrinking and growing cities by using a comparative analysis with global and local spatial statistics to understand the relationship between vegetation and socioeconomic inequality. neighborhoods in crisis undergo abandonment and vacancy, all of which has the positive potential to drive an increase in vegetation due to clearance and natural ecosystem changes. This study addressed the following research question. What is the relationship between poverty and vegetation? Building on previous research on vegetation, our paper makes original and significant contributions to the corpus of literature on U.S. shrinking and growing cities [3,4]. First, we used a comparative analysis to study three shrinking and two growing cities that were one standard deviation above the overall r-squared mean and one standard deviation below the overall r-squared mean to show cities that were different from the typical city. Second, we used global and local spatial statistics to study the spatial relationship between vegetation and socioeconomic inequality. Finally, we employed a novel methodology to study this relationship by using 1-km grids, rather than U.S. census tracts. The findings from this research provide much needed insight on the difference between shrinking and growing cities.

Impaired Water Hazard Zones: Mapping Intersecting Environmental Health Vulnerabilities and Polluter Disproportionality

“…The present study supports previous research that demonstrates GWLR generally offers a better model fit when compared to global models like the GLR. Likewise, this study supports the notion that GWLR is a powerful tool for uncovering spatial non-stationarity in the predictors of binary outcomes [56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73].…”

“…These include applications of geographically weighted logistic regression (GWLR) to model local variation in the predictors of binary outcomes spanning multiple subfields in human and physical geography [56][57][58][59][60][61][62][63][64][65][66][67][68]. Within the environmental inequality outcomes literature, spatial non-stationarity has been identified in the aggregate-level population vulnerability predictors of industrial air-toxic releases in New Jersey, USA [69], estimated lifetime cancer risk from cumulative ambient air-toxic pollution in Florida, USA [70], and vegetation land cover in shrinking and growing US cities [71]. GWR-based environmental inequality outcomes research has also uncovered spatial non-stationarity in the effect of particulate matter and other environmental exposures on adverse childhood respiratory conditions in the United Kingdom [72] and in the USA [73].…”

Using Areal Interpolation to Deal with Differing Regional Structures in International Research

“…Before we can use either method, it is necessary to choose a target regional structure. We decided to create a regular grid structure which can be found in many population studies [14,15]. We did so across the whole study area to harmonize municipal size differences in the respective countries.…”

Correction: Dawson, T.; et al. A Spatial Analysis of the Relationship between Vegetation and Poverty. Int. J. Geo-Inf. 2018, 7, 83

“…The paragraph, formula, and citations in Section 3, page 5 of 26, reported in their recently published paper [1] were incorrect. Currently it reads: "We used the pixel level regression Curve Fit tool, an extension in ArcMap (ArcGIS).…”

“…Please add the following new references [44][45][46] Due to this correction, reference numbers were adjusted to follow a numerical order. In [1], the previous References [44][45][46][47][48][49][50][51][52][53] are now [47][48][49][50][51][52][53][54][55][56].…”