The map below shows majority race by area in Carbondale, 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 Carbondale racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Carbondale overall is hispanic, making up 41.7% of residents. The next most-common racial group is white at 37.3%. There are more hispanic people in the central areas of the city. People who identify as white 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 Carbondale
Self-Identified Race |
Carbondale, CA Population |
|---|---|
White |
37.3% |
Hispanic |
41.7% |
Black |
10.9% |
Asian |
2.0% |
Native American |
2.4% |
Other |
5.7% |
Diversity and Diversity Scores for Carbondale, CA
The map below shows diversity in Carbondale. 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.
Carbondale Diversity Score
67.0
More diverse than 98% of US cities
Carbondale has a diversity score of 67.0: 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. Carbondale is much more diverse than other US cities, more diverse than 98% of them. Within Carbondale's proper boundaries, the most diverse area is southwest Carbondale, and the least diverse areas are in northwest Carbondale.
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.