The map below shows majority race by area in Many Farms, 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 Many Farms racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Many Farms overall is native american, making up 91.5% of residents. The next most-common racial group is white at 2.3%. There are more native american people in the northwest areas of the city. People who identify as white are most likely to be living in the south 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 Many Farms
Self-Identified Race |
Many Farms, AZ Population |
|---|---|
White |
2.3% |
Hispanic |
1.6% |
Black |
0.5% |
Asian |
2.1% |
Native American |
91.5% |
Other |
2.0% |
Diversity and Diversity Scores for Many Farms, AZ
The map below shows diversity in Many Farms. 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.
Many Farms Diversity Score
16.0
More diverse than 36% of US cities
Many Farms has a diversity score of 16.0: 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. Many Farms is less diverse than other US cities, more diverse than 36% of them. Within Many Farms's proper boundaries, the most diverse area is south Many Farms, and the least diverse areas are in northwest Many Farms.
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.