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Meta Data Name: Community Overlay Variables
Last Modified: Feburary 23, 2021
Author: Qinyun Lin

Data Location:

BE04 at the county level. Files can be found here.

  • BE04_C

Data Source(s) Description:

Variables were obtained from The US Covid Atlas Project. Hypersegregated cities refer to American metropolitan areas where black residents experience hypersegregation. See here for more information. The Black Belt refers to Southern US counties where at least 30% of the population identified Black or African American in the 2000 Census (see here). Native American or American Indian reservation boundaries come from the TIGER/Line 2017 dataset.

Description of Data Processing:

The following variables were included from the source data:

  • Hypersegregated City or Metropolitan Area - dummy variable
  • Black Belt County - dummy variable
  • Native American Reservations - numeric percent of county land area

Key Variable and Definitions:

Variable Variable ID in .csv Description
Hypersegregated City DmySgrg Dummy variable for whether county is part of a hypersegregated city or its metropolitan area
Southern Black Belt DmyBlckBlt Dummy variable for whether county is in the Southern Black Belt region
Native American Reservations PrctNtvRsrv Percentage of county land area that belongs to Native American reservation(s)

Data Limitations:

The variable representing Native American reservations was calculated as a percent of each county's total land area in US Albers Equal Area projection. Due to consistency issues of spatial projections, there may be some distortion of the percentage of land area calculation in Alaska.

Comments/Notes:

In defining counties in the Southern Black Belt, we included southern US counties where at least 30% of the population identified as Black or African American as of the 2000 Census. However, the Southern Black Belt areas have been variously defined in literature. See, for example, Webster, G. R., & Bowman, J. (2008). Quantitatively Delineating the Black Belt Geographic Region. Southeastern Geographer, 48(1), 3–18. https://doi.org/10.1353/sgo.0.0007.