Collaborative Research: Measuring Spatial Segregation

研究种族和社会经济居住隔离的原因、模式和后果，需要仔细衡量隔离模式。这反过来又要求隔离措施包含对空间接近/距离的理解，由于桌面地理信息系统（GIS）软件的可用性、复杂性和易用性的增加，这一点现在已经成为可能。本项目将开发和完善一种衡量空间（种族/民族）隔离的新方法，以解决其他措施中已知的缺陷。这种方法是基于这样一种认识，即隔离指数是衡量个人在种族或社会经济构成（或更一般地说，任何人口特征）方面的当地环境差异程度的指标。这种方法是通过假设每个个体都居住在一个“局部环境”中来实现的，该环境的人口由感兴趣区域内每个点的人口的空间加权平均值组成。给定特定的空间权重函数，通过计算研究区域内每个地点（或人）的当地环境的空间加权种族（或社会经济）组成，然后比较每个群体成员的当地环境的平均组成，来衡量隔离。这种方法有许多特点，使其非常适合于测量空间隔离。特别地，从这种方法得到的测量1)是独立于路径边界的选择；2)对任何尺度的隔离模式都很敏感；3)测量空间暴露度和空间均匀度；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)衡量多种族/族裔群体之间的隔离；6)很容易适用于收入隔离的测量。该项目将开发、评估和完善一套隔离措施，这些措施a)可以从现有的人口普查和地理空间数据中计算出来，b)使研究人员能够根据理论驱动的社会接近度和距离定义来衡量隔离。此外，该项目将开发软件工具、提供联机培训材料、举办讲习班和出版关于隔离模式和趋势的描述性分析，以便研究界能够使用这些措施。种族和收入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，隔离与生活在孤立的贫困和少数民族社区的家庭、青年和儿童的负面结果有关。收入隔离导致贫困集中，似乎对弱势社区的儿童产生了特别负面的影响，包括高中毕业率较低和青少年怀孕率较高。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，对社会政策具有重要意义。该项目将产生a)关于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分离的方法；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。

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.