The map below shows majority race by area in Forest Glen, 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 Forest Glen racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Forest Glen overall is white, making up 69.8% of residents. The next most-common racial group is other at 15.4%. There are more white people in the east areas of the city. People who identify as other are most likely to be living in the northeast 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 Forest Glen
Self-Identified Race |
Forest Glen, CA Population |
|---|---|
White |
69.8% |
Hispanic |
13.8% |
Black |
0.2% |
Asian |
0.0% |
Native American |
0.8% |
Other |
15.4% |
Diversity and Diversity Scores for Forest Glen, CA
The map below shows diversity in Forest Glen. 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.
Forest Glen Diversity Score
47.0
More diverse than 80% of US cities
Forest Glen has a diversity score of 47.0: 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. Forest Glen is much more diverse than other US cities, more diverse than 80% of them. Within Forest Glen's proper boundaries, the most diverse area is northeast Forest Glen, and the least diverse areas are in east Forest Glen.
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.