The map below shows majority race by area in Gang Mills, 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 Gang Mills racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Gang Mills overall is white, making up 73.0% of residents. The next most-common racial group is asian at 14.2%. There are more white people in the southeast areas of the city. People who identify as asian 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 Gang Mills
Self-Identified Race |
Gang Mills, NY Population |
|---|---|
White |
73.0% |
Hispanic |
1.1% |
Black |
3.8% |
Asian |
14.2% |
Native American |
0.0% |
Other |
7.9% |
Diversity and Diversity Scores for Gang Mills, NY
The map below shows diversity in Gang Mills. 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.
Gang Mills Diversity Score
43.9
More diverse than 77% of US cities
Gang Mills has a diversity score of 43.9: 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. Gang Mills is more diverse than other US cities, more diverse than 77% of them. Within Gang Mills's proper boundaries, the most diverse area is north Gang Mills, and the least diverse areas are in southeast Gang Mills.
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.