Neighborhood Contexts, Health, and Behavior: Understanding the Role of Scale and Residential Sorting
Neighborhood Contexts, Health, and Behavior: Understanding the Role of Scale and Residential Sorting
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邻里环境、健康和行为：了解规模和住宅排序的作用
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
C. Linkletter
中科院分区：
文献类型：
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作者：
S. Spielman;Eun;C. Linkletter
最近在社会学、公共卫生和城市规划方面的评论表明，城市环境中的小规模地理变化与个人的健康、行为和幸福感有关。然而，这些“邻里效应”的估计是复杂的。一个复杂的因素是居住分类：种族，年龄和社会经济地位等个人特征与行为和健康相关，这些相同的个人因素通常在地理上聚集在社区内。这种住宅分类导致了行为和健康的个人和环境决定因素之间的相关性，并给统计推断带来了问题。第二个复杂的因素是一个人的邻居的地理尺寸的不确定性。我们探索这两个潜在的混杂因素，通过模拟实验，生成合成的城市景观和合成的行为，地理定位的个人。模拟是用来开发一个模型，如何住宅排序，城市结构，以及个人的邻居的地理定义影响我们的城市环境和行为之间的关联的理解。我们发现，住宅排序并没有系统地影响邻里效应估计的大小，但是，一个人的邻里的地理尺寸的误报导致邻里效应估计的系统性偏差。与以往的研究不同的是，我们发现了一个系统的地理分析单位的定义和回归系数的大小之间的关系。
Recent reviews in sociology, public health, and urban planning suggest small-scale geographic variations in urban environments are associated with an individual's health, behavior, and well-being. However, estimation of these ‘neighborhood effects' are complicated. One complicating factor is residential sorting: Individual characteristics such as race, age, and socioeconomic status are associated with behavior and health and these same individual-level factors are often geographically clustered within neighborhoods. This residential sorting leads to a correlation between individual and environmental determinants of behavior and health and poses problems for statistical inference. A second complicating factor is uncertainty about the geographic dimensions of a person's neighborhood. We explore these two potential confounders through a simulation experiment that generates synthetic cityscapes and synthetic behaviors for geolocated individuals. The simulation is used to develop a model of how residential sorting, urban structure, and the geographic definition of an individual's neighborhood affect our understanding of the association between the urban environment and behavior. We find that residential sorting does not systematically affect the magnitude of neighborhood effect estimates, however, the misrepresentation of the geographic dimensions of an individual's neighborhood leads to systematic bias in estimates of neighborhood effects. Unlike previous research on the modifiable areal unit problem we find a systematic relationship between the definition of geographic units of analysis and the magnitude of regression coefficients.