The map below shows majority race by area in Red Level, 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 Red Level racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Red Level overall is white, making up 86.4% of residents. The next most-common racial group is black at 7.2%. There are more white people in the southwest 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 Red Level
Self-Identified Race |
Red Level, AL Population |
|---|---|
White |
86.4% |
Hispanic |
0.4% |
Black |
7.2% |
Asian |
0.0% |
Native American |
0.0% |
Other |
6.0% |
Diversity and Diversity Scores for Red Level, AL
The map below shows diversity in Red Level. 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.
Red Level Diversity Score
24.5
More diverse than 54% of US cities
Red Level has a diversity score of 24.5: 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. Red Level is about average for diversity versus other US cities, more diverse than 54% of them. Within Red Level's proper boundaries, the most diverse area is northwest Red Level, and the least diverse areas are in southwest Red Level.
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.