Sustainability and Social Justice

Concrete Evidence & Geographically Weighted Regression: A Regional Analysis of Wealth and the Land Cover in Massachusetts

Document Type

Article

Abstract

Several studies indicate that there is a positive relationship between green vegetation land cover and wealthy socio-economic conditions in urban areas. The purpose of this research is to test for and explore spatial variation in the relationship between socio-economic and green vegetation land cover across urban, suburban, and rural areas, using geographically weighted regression (GWR). The analysis was conducted at the census block group level for Massachusetts, using Census 2000 data and impervious surface data at 1-m resolution. To explore regional variations in the relationship, four scenarios were generated by regressing each of the following socio-economic variables - median household income, percentage of poverty, percentage of minority population, and median home value - against two environmental variables - percent of impervious surface and population density. GWR results show that there is a considerable spatial variation in the character and the strength of the relationship for each model. There are two main conclusions in this study. First, the impervious surface is generally a strong predictor of the level of wealth as measured by four variables included in the analysis, at the scale of census block group; however, the strength of the relationship varies geographically. Second, GWR, not ordinary least squares technique, should be used for regional scale spatial analysis because it is able to account for local effects and shows geographical variation in the strength of the relationship. © 2009 Elsevier Ltd. All rights reserved.

Publication Title

Applied Geography

Publication Date

12-1-2009

Volume

29

Issue

4

First Page

478

Last Page

487

ISSN

0143-6228

DOI

10.1016/j.apgeog.2009.03.001

Keywords

geographically weighted regression, impervious surface, Massachusetts, regression, wealth

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