The map below shows majority race by area in San Haven, 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 San Haven racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in San Haven overall is native american, making up 79.2% of residents. The next most-common racial group is white at 17.8%. There are more native american people in the east areas of the city. People who identify as white are most likely to be living in the west 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 San Haven
Self-Identified Race |
San Haven, ND Population |
|---|---|
White |
17.8% |
Hispanic |
1.3% |
Black |
0.0% |
Asian |
0.1% |
Native American |
79.2% |
Other |
1.6% |
Diversity and Diversity Scores for San Haven, ND
The map below shows diversity in San Haven. 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.
San Haven Diversity Score
34.1
More diverse than 66% of US cities
San Haven has a diversity score of 34.1: 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. San Haven is more diverse than other US cities, more diverse than 66% of them. Within San Haven's proper boundaries, the most diverse area is northwest San Haven, and the least diverse areas are in west San Haven.
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.