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Trees Grow on Money: Urban Tree Canopy Cover and Environmental Justice

机译:树木依靠金钱成长:城市树冠覆盖和环境正义

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摘要

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.
机译:这项研究调查了巴尔的摩,马里兰州,洛杉矶,加利福尼亚,纽约,纽约,费城,宾夕法尼亚州,罗利,北卡罗来纳州,萨克拉门托,加利福尼亚州和华盛顿特区使用高空间分辨率土地的城市树冠(UTC)的分布公平性覆盖数据和人口普查数据。使用Spearman相关性,普通最小二乘回归(OLS)和空间自回归模型(SAR)在人口普查区块组级别分析数据。在所有城市中,UTC覆盖率与家庭收入中位数之间都存在很强的正相关关系。在某些城市的双变量模型中,种族和UTC覆盖率之间存在负相关关系,但使用多元回归分析通常无法观察到它们之间的相关性,其中包括收入,教育和居住年龄等其他变量。与所有城市的OLS模型相比,SAR模型产生的r平方值更高,这表明空间自相关是我们数据的重要特征。基于气候,种族/民族和规模的共同特征,可以发现城市之间的相似性。我们的发现表明,包括收入在内的一系列变量有助于UTC的覆盖范围。这些发现可以帮助针对UTC目标和环境正义问题同时制定战略。

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