The map below shows majority race by area in Old Station, 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 Old Station racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Old Station overall is white, making up 95.1% of residents. The next most-common racial group is hispanic at 3.4%. There are more white people in the north areas of the city. People who identify as hispanic 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 Old Station
Self-Identified Race |
Old Station, CA Population |
|---|---|
White |
95.1% |
Hispanic |
3.4% |
Black |
0.2% |
Asian |
0.5% |
Native American |
0.3% |
Other |
0.5% |
Diversity and Diversity Scores for Old Station, CA
The map below shows diversity in Old Station. 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.
Old Station Diversity Score
9.4
More diverse than 17% of US cities
Old Station has a diversity score of 9.4: 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. Old Station is much less diverse than other US cities, more diverse than 17% of them. Within Old Station's proper boundaries, the most diverse area is northwest Old Station, and the least diverse areas are in north Old Station.
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.