Abstract
The United States is increasingly becoming a multi-racial society. To understand multiple consequences of this overall trend to our neighborhoods we need a methodology capable of spatio-temporal analysis of racial diversity at the local level but also across the entire U.S. Furthermore, such methodology should be accessible to stakeholders ranging from analysts to decision makers. In this paper we present a comprehensive framework for visualizing and analyzing diversity data that fulfills such requirements. The first component of our framework is a U.S.-wide, multi-year database of race sub-population grids which is freely available for download. These 30 m resolution grids have being developed using dasymetric modeling and are available for 1990-2000-2010. We summarize numerous advantages of gridded population data over commonly used Census tract-aggregated data. Using these grids frees analysts from constructing their own and allows them to focus on diversity analysis. The second component of our framework is a set of U.S.-wide, multi-year diversity maps at 30 m resolution. A diversity map is our product that classifies the gridded population into 39 communities based on their degrees of diversity, dominant race, and population density. It provides spatial information on diversity in a single, easy-to-understand map that can be utilized by analysts and end users alike. Maps based on subsequent Censuses provide information about spatio-temporal dynamics of diversity. Diversity maps are accessible through the GeoWeb application SocScape (http://sil.uc.edu/webapps/socscape_usa/) for an immediate online exploration. The third component of our framework is a proposal to quantitatively analyze diversity maps using a set of landscape metrics. Because of its form, a grid-based diversity map could be thought of as a diversity “landscape” and analyzed quantitatively using landscape metrics. We give a brief summary of most pertinent metrics and demonstrate how they can be applied to diversity maps.
Highlights
Census Bureau population projections [1] indicate that the racial dynamic in the U.S will steer the country toward a society with no absolute racial majority by 2044
We review the shortcomings of aggregated data and argue that a high resolution population grid provides a data format which is more suitable for diversity studies, especially studies aiming at spatio-temporal dynamics of diversity over the entire U.S We proceed to describe the process of obtaining a database consisting of high resolution population grids, and introduce SocScape—a freely available online resource for the distribution and visualization of gridded 1990–2000–2010 population data over the entire conterminous United States (CONUS) at the nominal resolution of 30 m
Our goal in this paper was to present a comprehensive framework for visualizing and analyzing spatio-temporal dynamics of racial diversity in the entire conterminous U.S We believe that all parts of the presented framework lower barriers for investigating issues related to segregation and diversity in the United States
Summary
Census Bureau population projections [1] indicate that the racial dynamic in the U.S will steer the country toward a society with no absolute racial majority by 2044. A common thread to all proposed methods of analysis is their reliance on data format (areal unit-based aggregation of population counts) provided by the U.S Census Bureau data. For predominantly historical reasons [28] the Census releases data in aggregated (vector) format As this has always been the case, this format may appear as the natural choice for providing information about population characteristics, but, in reality, it has multiple shortcomings in comparison to the grid format. Mapping segregation or diversity using Census units results in maps (for example, see [14] or [37]) that suggest spatial uniformity across each unit and discontinuity at the boundaries between the units This is an artifact of aggregated data and the visual manifestation of the MAUP. The boundaries of census aggregation units change from one census to another, making the analysis of population change difficult [39,40,41]
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