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The Topography of Poverty in the United States: A Spatial Analysis Using County-Level Data From the Community Health Status Indicators Project

机译:美国的贫困状况:使用社区健康状况指标项目中县级数据进行的空间分析

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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.
机译:现在可以通过社区健康状况指标(CHSI)数据库获得县级的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映射应用程序,以可视化各种社区相关措施中的模式。也可以下载这些数据进行本地分析。本文介绍了2000年美国县级贫困的空间分析。地理信息系统中的空间统计技术用于量化重要的空间模式,例如集中贫困率和空间离群值。分析显示了明显的贫困现象。在贫困率与贫困率相对的地区,发现了南北贫困区与高贫困区的明显界限,以及高低贫困区的孤立区域。可以将这种模式描述为一个大陆性的贫困鸿沟。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格局的基本过程。空间分析技术广泛适用于与社会经济和健康相关的数据,并且可以提供有关数据集空间结构的重要信息,这对于选择适当的分析方法非常重要。

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