Spatial Demography最新文献

Changes in the spatial patterns of ethnic diversity and residential segregation are often highly localized, but inconsistencies in geographical data units across different time points limit their exploration. In this paper, we argue that, while they are often over-looked, population grids provide an effective means for the study of long-term fine-scale changes. Gridded data represent population structures: there are gaps where there are no people, and they are not (unlike standard zones) based on population distributions at any one time point. This paper uses an innovative resource, PopChange, which provides spatially fine-grained (1 km by 1 km) gridded data on country of birth (1971-2011) and ethnic group (1991-2011). These data enable insight into micro-level change across a long time period. Exploring forty years of change over five time points, measures of residential ethnic diversity and segregation are employed here to create a comprehensive 'atlas' of ethnic neighbourhood change across the whole of Britain. Four key messages are offered: (1) as Britain's ethnic diversity has grown, the spatial complexity of this diversity has also increased, with greater diversity in previously less diverse spaces; (2) ethnic residential segregation has steadily declined at this micro-scale; (3) as neighbourhoods have become more diverse, they have become more spatially integrated; (4) across the whole study period, the most dynamic period of change was between 2001 and 2011. While concentrating on Britain as a case study, the paper explores the potential offered by gridded data, and the methods proposed to analyse them, for future allied studies within and outside this study area.

Poverty has been studied across many social science disciplines, resulting in a large body of literature. Scholars of poverty research have long recognized that the poor are not uniformly distributed across space. Understanding the spatial aspect of poverty is important because it helps us understand place-based structural inequalities. There are many spatial regression models, but there is a learning curve to learn and apply them to poverty research. This manuscript aims to introduce the concepts of spatial regression modeling and walk the reader through the steps of conducting poverty research using R: standard exploratory data analysis, standard linear regression, neighborhood structure and spatial weight matrix, exploratory spatial data analysis, and spatial linear regression. We also discuss the spatial heterogeneity and spatial panel aspects of poverty. We provide code for data analysis in the R environment and readers can modify it for their own data analyses. We also present results in their raw format to help readers become familiar with the R environment.

Most quantitative studies of neighborhood racial change rely on census tracts as the unit of analysis. However, tracts are insensitive to variation in the geographic scale of the phenomenon under investigation and to proximity among a focal tract's residents and those in nearby territory. Tracts may also align poorly with residents' perceptions of their own neighborhood and with the spatial reach of their daily activities. To address these limitations, we propose that changes in racial structure (i.e., in overall diversity and group-specific proportions) be examined within multiple egocentric neighborhoods, a series of nested local environments surrounding each individual that approximate meaningful domains of experience. Our egocentric approach applies GIS procedures to census block data, using race-specific population densities to redistribute block counts of whites, blacks, Hispanics, and Asians across 50-meter by 50-meter cells. For each cell, we then compute the proximity-adjusted racial composition of four different-sized local environments based on the weighted average racial group counts in adjacent cells. The value of this approach is illustrated with 1990-2000 data from a previous study of 40 large metropolitan areas. We document exposure to increasing neighborhood racial diversity during the decade, although the magnitude of this increase in diversity-and of shifts in the particular races to which one is exposed-differs by local environment size and racial group membership. Changes in diversity exposure at the neighborhood level also depend on how diverse the metro area as a whole has become.