TY - JOUR
T1 - Trees grow on money
T2 - Urban tree canopy cover and environmental justice
AU - Schwarz, Kirsten
AU - Fragkias, Michail
AU - Boone, Christopher
AU - Zhou, Weiqi
AU - McHale, Melissa
AU - Grove, J. Morgan
AU - O'Neil-Dunne, Jarlath
AU - McFadden, Joseph P.
AU - Buckley, Geoffrey L.
AU - Childers, Daniel
AU - Ogden, Laura
AU - Pincetl, Stephanie
AU - Pataki, Diane
AU - Whitmer, Ali
AU - Cadenasso, Mary L.
PY - 2015/4/1
Y1 - 2015/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84926621227&partnerID=8YFLogxK
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U2 - 10.1371/journal.pone.0122051
DO - 10.1371/journal.pone.0122051
M3 - Article
C2 - 25830303
AN - SCOPUS:84926621227
SN - 1932-6203
VL - 10
JO - PloS one
JF - PloS one
IS - 4
M1 - e0122051
ER -