Spielman Seth E, Yoo Eun-Hye
Brown University, Spatial Structures in the Social Sciences, Maxcy Hall, 112 George Street, Box 1916, Providence, RI 02912, USA.
Soc Sci Med. 2009 Mar;68(6):1098-105. doi: 10.1016/j.socscimed.2008.12.048. Epub 2009 Jan 23.
In the decade or so of renewed interest in neighborhood contexts and health, significant progress has been made conceptualizing the relationships between the urban environment and public health. Applied research on the link between the environment and health remains limited by the way spatial concepts, such as "the neighborhood" or "the built environment" are operationalized. In this paper we argue that representations of these spatial concepts in statistical models should be based upon the individuals, the place, and the problem under study. Through a series of simulation experiments we describe the sensitivity of estimates of the association between neighborhoods and health to the operationalization of spatial concepts. We explore the practice of conducting the same analysis at multiple scales and find that using model fit to "discover" the spatial dimension is problematic. In sum, there is a gap between our understanding of how the environment influences health and spatial statistical modeling techniques. For quantitative spatial inquiry into the relationship between the neighborhood environment and health to be effective this gap must be closed.
在对社区环境与健康重新产生兴趣的大约十年时间里,在将城市环境与公共卫生之间的关系概念化方面已经取得了重大进展。环境与健康之间联系的应用研究仍然受到诸如“社区”或“建成环境”等空间概念的操作方式的限制。在本文中,我们认为这些空间概念在统计模型中的表示应该基于个体、地点以及所研究的问题。通过一系列模拟实验,我们描述了社区与健康之间关联估计对空间概念操作化的敏感性。我们探索了在多个尺度上进行相同分析的实践,并发现使用模型拟合来“发现”空间维度存在问题。总之,我们对环境如何影响健康的理解与空间统计建模技术之间存在差距。为了使对社区环境与健康之间关系的定量空间探究有效,必须弥合这一差距。