The map below shows majority race by area in Cusco Willa, as self-identified on the US census. Darker shades indicate a larger racial majority in that neighborhood. This page also contains data and maps on Cusco Willa racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Cusco Willa overall is white, making up 64.2% of residents. The next most-common racial group is black at 34.1%. There are more white people in the east areas of the city. People who identify as black are most likely to be living in the northwest places. The data below shows how many people identify themselves as each of the following races, which most Americans base on their family's national origin:
Race in Cusco Willa
Self-Identified Race |
Cusco Willa, VA Population |
|---|---|
White |
64.2% |
Hispanic |
0.0% |
Black |
34.1% |
Asian |
0.1% |
Native American |
0.0% |
Other |
1.6% |
Diversity and Diversity Scores for Cusco Willa, VA
The map below shows diversity in Cusco Willa. Areas in green are more diverse, while areas in red are much less diverse. Diversity, in this case, means a mixture of people with different race and ethnicity living close to one another. For example, all-black and all-white areas in the city would both be considered lacking diversity.
Cusco Willa Diversity Score
47.1
More diverse than 80% of US cities
Cusco Willa has a diversity score of 47.1: the chance, in percent, that two residents picked at random belong to different racial or ethnic groups, from ACS race counts. A place where everyone shares one group scores 0, and an even mix of the six groups in the chart above scores the maximum, 83.3. Cusco Willa is much more diverse than other US cities, more diverse than 80% of them. Within Cusco Willa's proper boundaries, the most diverse area is east Cusco Willa, and the least diverse areas are in northwest Cusco Willa.
Source: BestNeighborhood calculations from the U.S. Census Bureau, American Community Survey (ACS) 2020-2024 5-year estimates (race and Hispanic origin); proprietary data mapping and analysis. Special thanks to the University of Virginia.