Holt James B
Division of Adult and Community Health, Centers for Disease Control and Prevention, Mailstop K-67, 4770 Buford Hwy NE, Atlanta, GA 30341, USA.
Prev Chronic Dis. 2007 Oct;4(4):A111. Epub 2007 Sep 15.
Socioeconomic and health-related data at the county level are now available through the Community Health Status Indicators (CHSI) database. These data are useful for assessing the health of communities and regions. Users of the CHSI data can access online reports and an online mapping application for visualizing patterns in various community-related measures. It also is possible to download these data to conduct local analyses. This paper describes a spatial analysis of poverty in the United States at the county level for 2000. Spatial statistical techniques in a geographic information system were used to quantify significant spatial patterns, such as concentrated poverty rates and spatial outliers. The analysis revealed significant and stark patterns of poverty. A distinctive north-south demarcation of low versus high poverty concentrations was found, along with isolated pockets of high and low poverty within areas in which the predominant poverty rates were opposite. This pattern can be described as following a continental poverty divide. These insights can be useful in explicating the underlying processes involved in forming such spatial patterns that result in concentrated wealth and poverty. The spatial analytic techniques are broadly applicable to socioeconomic and health-related data and can provide important information about the spatial structure of datasets, which is important for choosing appropriate analysis methods.
现在可以通过社区健康状况指标（CHSI）数据库获取县级社会经济和健康相关数据。这些数据对于评估社区和地区的健康状况很有用。CHSI数据的用户可以访问在线报告和在线地图应用程序，以可视化各种社区相关指标中的模式。也可以下载这些数据进行本地分析。本文描述了2000年美国县级贫困状况的空间分析。利用地理信息系统中的空间统计技术来量化显著的空间模式，如集中贫困率和空间异常值。分析揭示了显著且鲜明的贫困模式。发现了贫困浓度低与高的明显南北分界线，以及在主要贫困率相反的地区内孤立的高贫困和低贫困区域。这种模式可以描述为遵循大陆贫困分界线。这些见解有助于阐明形成这种导致财富和贫困集中的空间模式所涉及的潜在过程。空间分析技术广泛适用于社会经济和健康相关数据，并可以提供有关数据集空间结构的重要信息，这对于选择合适的分析方法很重要。
