Collaborative Research: Measuring Spatial Segregation

合作研究：测量空间隔离

• 批准号: 0520405
• 负责人: Stephen Matthews
• 金额: $ 10.21万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0520405&HistoricalAwards=false
• 关键词: Collaborative; Research; Measuring; Spatial; Segregation

The study of the causes, patterns, and consequences of racial and socioeconomic residential segregation requires the careful measurement of segregation patterns. This, in turn, requires that measures of segregation incorporate an understanding of spatial proximity/distance, something that is now possible due to the increasing availability, sophistication, and ease-of-use of desktop geographical information system (GIS) software. This project will develop and refine a new approach to measuring spatial (race/ethnic) segregation that addresses known flaws in other measures. The approach is based on the understanding that a segregation index is a measure of the extent to which the local environments of individuals differ in their racial or socioeconomic composition (or, more generally, on any population trait). This approach is operationalized by assuming each individual inhabits a 'local environment' whose population is made up of the spatially-weighted average of the populations at each point in the region of interest. Given a particular spatial weighting function, segregation is measured by computing the spatially-weighted racial (or socioeconomic) composition of the local environment of each location (or person) in the study region and then comparing the average compositions of the local environments of members of each group. This approach has a number of features that make it well suited to measuring spatial segregation. In particular, measures derived from this approach 1) are independent of choices of tract boundaries; 2) are sensitive to segregation patterns at any scale; 3) measure both spatial exposure and spatial evenness; 4) can be computed using any theory-based definition of spatial proximity and distance; 5) measure segregation among multiple racial/ethnic groups; and 6) are readily adaptable to the measurement of income segregation. This project will develop, evaluate, and refine a set of measures of segregation that a) are computable from available census and geospatial data, and b) enable researchers to measure segregation based on theory-driven definitions of social proximity and distance. In addition, the project will develop software tools, provide on-line training materials, conduct workshops, and publish descriptive analyses of segregation patterns and trends in order to enable the research community to use these measures.Residential segregation by race and income remains a stubborn feature of U.S. society, and a growing body of scholarship shows that segregation is associated with negative outcomes for families, youth, and children living in isolated poor and minority neighborhoods. Income segregation, which results in the concentration of poverty, appears to have particularly negative effects for children in disadvantaged neighborhoods, including lower rates of high school completion and higher rates of teen pregnancy. Consequently, the study of racial and socioeconomic residential segregation is an important area of scholarship with significant implications for social policy. This project will produce a) technical knowledge regarding the measurement of segregation; b) user-friendly software tools and training materials to enable other researchers to use the newly-developed methods of measuring segregation; and c) detailed descriptive data on patterns and trends of racial and socioeconomic segregation in U.S. metropolitan areas. These tools and descriptive results will enable researchers to better understand the causes, patterns, and consequences of residential segregation.

要研究种族隔离和社会经济隔离的原因、模式和后果，就需要仔细衡量隔离模式。这又要求隔离措施纳入对空间接近/距离的理解，这是由于桌面地理信息系统(地理信息系统)软件的可用性、复杂性和易用性日益提高，现在才有可能做到这一点。该项目将开发和完善一种衡量空间(种族/族裔)隔离的新方法，以解决其他措施中已知的缺陷。这种办法是基于这样一种理解，即隔离指数是衡量个人的当地环境在其种族或社会经济组成方面(或更广泛地说，在任何人口特征上)的差异程度的一种衡量标准。这种方法的可操作性是假设每个个体生活在一个“局部环境”中，该环境的人口由感兴趣区域中每个点的人口的空间加权平均值组成。给定特定的空间加权函数，通过计算研究区域内每个地点(或个人)的当地环境的空间加权种族(或社会经济)成分，然后比较每个群体成员的当地环境的平均成分，来衡量种族隔离。这种方法具有许多特征，使其非常适合测量空间隔离。特别是，这种方法得出的衡量标准：1)不依赖于地域边界的选择；2)对任何规模的隔离模式都很敏感；3)衡量空间曝光度和空间均衡性；4)可以使用任何基于理论的空间接近和距离定义来计算；5)衡量多个种族/族裔群体之间的隔离；以及6)很容易适应收入隔离的衡量。该项目将开发、评估和改进一套隔离措施，a)可以根据现有的人口普查和地理空间数据进行计算，b)使研究人员能够根据社会距离和距离的理论驱动的定义来衡量隔离。此外，该项目将开发软件工具，提供在线培训材料，举办研讨会，并发布种族隔离模式和趋势的描述性分析，以使研究界能够使用这些措施。按种族和收入进行的居住隔离仍然是美国社会的一个顽固特征，越来越多的学术研究表明，种族隔离与居住在与世隔绝的贫困和少数族裔社区的家庭、青年和儿童的负面后果有关。造成贫困集中的收入隔离似乎对贫困社区的儿童产生了特别不利的影响，包括较低的高中毕业率和较高的青少年怀孕率。因此，对种族和社会经济居住隔离的研究是一个重要的学术领域，对社会政策具有重大影响。该项目将产生a)关于种族隔离测量的技术知识；b)用户友好的软件工具和培训材料，使其他研究人员能够使用新开发的测量种族隔离的方法；以及c)关于美国大都市地区种族和社会经济隔离的模式和趋势的详细描述性数据。这些工具和描述性结果将使研究人员能够更好地理解居住隔离的原因、模式和后果。