A spatially explicit approach to the study of socio-demographic inequality in the spatial distribution of trees across Boston neighborhoods.
A spatially explicit approach to the study of socio-demographic inequality in the spatial distribution of trees across Boston neighborhoods.
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研究波士顿社区树木空间分布的社会人口不平等的空间明确方法。
DOI：
10.1007/bf03354902
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发表时间：
2014-04
影响因子：
1.9
通讯作者：
Williams DR
中科院分区：
文献类型：
--
作者：
Duncan DT;Kawachi I;Kum S;Aldstadt J;Piras G;Matthews SA;Arbia G;Castro MC;White K;Williams DR
社区的种族/民族和收入构成通常会影响当地的便利设施，包括树木的潜在空间分布，这对于人口健康和社区福祉非常重要，特别是在城市地区。这项生态研究使用空间分析方法来评估马萨诸塞州波士顿（美国）人口普查机智水平上邻里社会人口特征（即少数种族/族裔构成和贫困）与树木密度之间的关系。我们检查了所有研究变量和普通最小二乘 (OLS) 回归残差中的 Global Moran’s I 的空间自相关性，以及针对社会人口特征和树木密度之间的空间自相关性未经调整和调整的计算 Spearman 相关性。接下来，我们酌情拟合传统回归（即 OLS 回归模型）和空间回归（即空间联立自回归模型）。我们发现所有邻里社会人口统计特征（Global Moran’s I 范围为 0.24 至 0.86，所有 P=0.001）、树木密度（Global Moran’s I=0.452，P=0.001）和 OLS 回归残差（Global Moran’s I 范围为 0.32 至 0.38，所有 P<0.001）均存在显着的正空间自相关。因此，我们拟合空间联立自回归模型。非西班牙裔黑人社区百分比与树木密度之间存在负相关性（rS=−0.19；传统 P 值=0.016；空间调整 P 值=0.299），并且以非西班牙裔黑人为主（超过 60% 黑人）社区与树木密度之间存在负相关性（rS=−0.18；传统 P 值=0.019；空间调整 P 值=0.180）。虽然传统的 OLS 回归模型发现黑人社区和树木密度之间存在略微显着的负相关关系，但我们在空间回归模型中发现社区社会人口构成和树木密度之间没有统计上显着的关系。从方法上讲，我们的研究表明需要考虑空间自相关，因为当忽略空间自相关时，发现/结论可能会发生变化。从本质上讲，我们的研究结果表明不需要对波士顿的树木进行政策干预，尽管我们急忙补充说需要重复研究以及有关树木质量、年龄和多样性的更细致的数据。
The racial/ethnic and income composition of neighborhoods often influences local amenities, including the potential spatial distribution of trees, which are important for population health and community wellbeing, particularly in urban areas. This ecological study used spatial analytical methods to assess the relationship between neighborhood socio-demographic characteristics (i.e. minority racial/ethnic composition and poverty) and tree density at the census tact level in Boston, Massachusetts (US). We examined spatial autocorrelation with the Global Moran’s I for all study variables and in the ordinary least squares (OLS) regression residuals as well as computed Spearman correlations non-adjusted and adjusted for spatial autocorrelation between socio-demographic characteristics and tree density. Next, we fit traditional regressions (i.e. OLS regression models) and spatial regressions (i.e. spatial simultaneous autoregressive models), as appropriate. We found significant positive spatial autocorrelation for all neighborhood socio-demographic characteristics (Global Moran’s I range from 0.24 to 0.86, all P=0.001), for tree density (Global Moran’s I=0.452, P=0.001), and in the OLS regression residuals (Global Moran’s I range from 0.32 to 0.38, all P<0.001). Therefore, we fit the spatial simultaneous autoregressive models. There was a negative correlation between neighborhood percent non-Hispanic Black and tree density (rS=−0.19; conventional P-value=0.016; spatially adjusted P-value=0.299) as well as a negative correlation between predominantly non-Hispanic Black (over 60% Black) neighborhoods and tree density (rS=−0.18; conventional P-value=0.019; spatially adjusted P-value=0.180). While the conventional OLS regression model found a marginally significant inverse relationship between Black neighborhoods and tree density, we found no statistically significant relationship between neighborhood socio-demographic composition and tree density in the spatial regression models. Methodologically, our study suggests the need to take into account spatial autocorrelation as findings/conclusions can change when the spatial autocorrelation is ignored. Substantively, our findings suggest no need for policy intervention vis-à-vis trees in Boston, though we hasten to add that replication studies, and more nuanced data on tree quality, age and diversity are needed.