Spatial Demography最新文献

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.

There is a large body of literature examining transportation impacts on population and employment growth. However, the possible impacts that transportation infrastructures have on socioeconomic statuses are less clear. This study fills the gap in the literature by associating education and income-two socioeconomic status measures-with transportation infrastructures. In specific, this study examines the associations of railroads, highways, and airports collectively with high school, Bachelor's degree, graduate degree, and income change in the continental United States for the period between 1970 and 2010. Data come from various sources, such as National Transportation Atlas Database, Decennial Census, Cartographic Boundary Shapefiles, and Land Developability Index. Standard regression and spatial analysis are conducted at decade levels and at the entire study period to test the consistency of the associations between transportation infrastructures and education and income. The study shows that railroads have a distributive and highways have a facilitative association with both education and income. Airports behave as a growth factor with education and as a facilitator with income. The findings clearly show the increased complexity of the roles performed by transportation infrastructures and do not show straightforward behaviors as has been considered for a long time. This study provides new insights into the role of transportation infrastructures for transportation planning and decision making.