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.
The authors wish to make the following corrections to their paper [1]: Change in Main BodyThe 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). This allowed for us to run regression trend analysis on a series of 72 cities using AVHRR raster datasets for temporal analysis (1990, 1991, 1995, 1996, 1997, 1998, and 2001 to 2010), taken in consideration that vegetation can be impacted by the climate during the years i.e., really hot, dry, wet, etc. The approach is similar to Geographically Weighted Regression (GWR), (Fotheringham, Brunsdon et al. 2003).whereDue to an undetected mistake in Section 3: Methods, certain errors appeared in the formula and we omitted some citations and references; for this reason, we would like to replace the above with: "We used the pixel level regression Curve Fit tool [44][45][46] an extension in ArcMap (ArcGIS) [31]. This allowed us to run regression trend analysis on 72 cities using AVHRR raster datasets for temporal analysis (1990, 1991, 1995, 1996, 1997, 1998, and 2001 to 2010).where:
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