Comprehensive framework for visualizing and analyzing spatio-temporal dynamics of racial diversity in the entire United States

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.

Imperfect melting pot – Analysis of changes in diversity and segregation of US urban census tracts in the period of 1990–2010

“…Although standardized tracts are available, for example, from the National Historical Geographic Information System (NHGIS) (https://www.nhgis.org/) or from GeoLytics (http://www.geolytics.com/), standardized blocks are not available. Therefore, we use the SocScape (http://sil.uc.edu) high-resolution grids (Dmowska et al, 2017) which are available for 1990, 2000, and 2010.…”

“…The racial patterns within a single tract located in the Cook County are also shown. These patterns were obtained using a cartographic method of racial dot maps (Roth, 2010) applied to high-resolution population grids (Dmowska et al, 2017). Each inhabitant of Cook County is depicted as a dot having a color corresponding to his race and placed in the best estimation of the location of his residence.…”

A geographic identifier assignment algorithm with Bayesian variable selection to identify neighborhood factors associated with emergency department visit disparities for asthma

“…When health studies use areal units in analyses, they must also contend with the modifiable areal unit problem (MAUP), in which the delineation of boundaries affects the values (e.g., percent of the population living in poverty) within the unit [10,11]. There are additional limitations in areal analyses, including coarse spatial resolution, assumptions about distributions within spatial units, temporal boundary changes, and boundaries that are arbitrary for health research [12]. As such, health researchers often prefer point process or grid-based analyses that can overcome areal unit limitations.…”

Visualizing demographic evolution using geographically inconsistent census data

“…Another option considers the use of grid data [10], [16], where small rectangular areas are used, in an approach similar to satellite imagery. Beyond the increased spatial accuracy, this approach does not require complex harmonization when new data is considered.…”

“…This is laborious work that inevitably introduces some amount of error [8], even when additional data is provided [9]. Nevertheless, this step is considered mandatory in the current literature: "(...) tract-by-tract comparison is not possible unless data from 2000 is interpolated to 2010 boundaries (...)" [10].…”