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PLoS One. 2015 Apr 1;10(4):e0122051. doi: 10.1371/journal.pone.0122051. eCollection 2015.

Trees grow on money: urban tree canopy cover and environmental justice.

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Department of Biology, Northern Kentucky University, Highland Heights, Kentucky, United States of America.
Department of Economics, College of Business and Economics (COBE), Boise State University, Boise, Idaho, United States of America.
School of Sustainability, Arizona State University, Tempe, Arizona, United States of America.
Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Haidian District, Beijing, China.
Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, United States of America.
USDA Forest Service, Northern Research Station, Baltimore, Maryland, United States of America.
University of Vermont, Rubenstein School of Environment and Natural Resources and Spatial Analysis Lab, Burlington, Vermont, United States of America.
Department of Geography, University of California Santa Barbara, Santa Barbara, California, United States of America.
Department of Geography, Ohio University, Clippinger Laboratories 109, Athens, Ohio, United States of America.
Department of Global and Sociocultural Studies, Florida International University, FIU Modesto A. Maidique Campus, Miami, Florida, United States of America.
Institute of the Environment and Sustainability, University of California Los Angeles, Los Angeles, California, United States of America.
Department of Biology, University of Utah, Salt Lake City, Utah, United States of America.
Georgetown University, Washington D.C., United States of America.
Department of Plant Sciences, University of California Davis, Davis, California, United States of America.


This study examines the distributional equity of urban tree canopy (UTC) cover for Baltimore, MD, Los Angeles, CA, New York, NY, Philadelphia, PA, Raleigh, NC, Sacramento, CA, and Washington, D.C. using high spatial resolution land cover data and census data. Data are analyzed at the Census Block Group levels using Spearman's correlation, ordinary least squares regression (OLS), and a spatial autoregressive model (SAR). Across all cities there is a strong positive correlation between UTC cover and median household income. Negative correlations between race and UTC cover exist in bivariate models for some cities, but they are generally not observed using multivariate regressions that include additional variables on income, education, and housing age. SAR models result in higher r-square values compared to the OLS models across all cities, suggesting that spatial autocorrelation is an important feature of our data. Similarities among cities can be found based on shared characteristics of climate, race/ethnicity, and size. Our findings suggest that a suite of variables, including income, contribute to the distribution of UTC cover. These findings can help target simultaneous strategies for UTC goals and environmental justice concerns.

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