The map below shows majority race by area in Maple Leaf, 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 Maple Leaf racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Maple Leaf overall is native american, making up 75.9% of residents. The next most-common racial group is white at 21.1%. There are more native american people in the southeast 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 Maple Leaf
Self-Identified Race |
Maple Leaf, SD Population |
|---|---|
White |
21.1% |
Hispanic |
0.7% |
Black |
0.0% |
Asian |
0.0% |
Native American |
75.9% |
Other |
2.3% |
Diversity and Diversity Scores for Maple Leaf, SD
The map below shows diversity in Maple Leaf. 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.
Maple Leaf Diversity Score
37.8
More diverse than 70% of US cities
Maple Leaf has a diversity score of 37.8: 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. Maple Leaf is more diverse than other US cities, more diverse than 70% of them. Within Maple Leaf's proper boundaries, the most diverse area is west Maple Leaf, and the least diverse areas are in southeast Maple Leaf.
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.