The map below shows majority race by area in Rio del Mar, 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 Rio del Mar racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Rio del Mar overall is white, making up 80.1% of residents. The next most-common racial group is hispanic at 11.0%. There are more white people in the northwest areas of the city. People who identify as hispanic are most likely to be living in the north 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 Rio del Mar
Self-Identified Race |
Rio del Mar, CA Population |
|---|---|
White |
80.1% |
Hispanic |
11.0% |
Black |
0.1% |
Asian |
3.1% |
Native American |
0.0% |
Other |
5.7% |
Diversity and Diversity Scores for Rio del Mar, CA
The map below shows diversity in Rio del Mar. 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.
Rio del Mar Diversity Score
34.2
More diverse than 66% of US cities
Rio del Mar has a diversity score of 34.2: 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. Rio del Mar is more diverse than other US cities, more diverse than 66% of them. Within Rio del Mar's proper boundaries, the most diverse area is central Rio del Mar, and the least diverse areas are in northwest Rio del Mar.
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.