Racial Dot Maps Based on Dasymetrically Modeled Gridded Population Data

Dados Bibliográficos

ID13027005
AutoresAnna Dmowska (0000-0001-9333-3768, University of Cincinnati, autor correspondente), T F Stepinski (0000-0001-6818-203X, University of Cincinnati)
Ano2019
Volume8
Fascículo5
Páginas157-157
Data de publicação2019-05-18
Open AccessSim
TipoARTICLE
PeriódicoSocial Sciences (JOURNAL)
Identificadores do periódicoISSN: 2076-0760 • E-ISSN: 2076-0760
EditoraMDPI AG (PUBLISHER • IT)
DOI10.3390/socsci8050157
OpenAlexW2944983913
IdiomaEN
Citações recebidas3
Referências citadas23

Racial geography, mapping spatial distributions of different racial groups, is of keen interest in a multiracial society like the United States. A racial dot map is a method of visualizing racial geography, which depicts spatial distribution, population density, and racial mix in a single, easy-to-understand map. Because of the richness of information it carries, the dot map is an excellent tool for visual analysis of racial distribution. Presently-used racial dot maps are based on the Census data at the tract or the block level. In this paper, we present a method of constructing a more spatially-accurate racial dot map based on a sub-block-resolution population grid. The utility of our dot maps is further enhanced by placing dots on the map in random order regardless of the race they represent in order to achieve a more accurate depiction of local racial composition. We present a series of comparisons between dot maps based on tract, block, and grid data. The advantage of a grid-based dot map is evident from the visual comparison of all maps with an actual image of the mapped area. We make available the R code for constructing grid-based dot maps. We also make available 2010 grid-based racial dot maps for all counties in the conterminous United States

Block (permutation group theory · Cartography · Census · Census tract · Geography · Grid · Grid reference · Population · Sociology · Computer Science · Demography · Mathematics · Racial and Ethnic Identity Research · Urban, Neighborhood, and Segregation Studies · Artificial Intelligence

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