The map below shows majority race by area in Indian Mountain Lake, 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 Indian Mountain Lake racial diversity and diversity scores. Click here to scroll to diversity data.
WhiteHispanicBlackAsianMultiple/Other
Majority race by area
The majority race in Indian Mountain Lake overall is white, making up 53.0% of residents. The next most-common racial group is hispanic at 25.1%. There are more white people in the southwest areas of the city. People who identify as hispanic are most likely to be living in the northeast 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 |
Indian Mountain Lake, PA Population |
|---|---|
White |
53.0% |
Hispanic |
25.1% |
Black |
19.3% |
Asian |
2.4% |
Native American |
0.0% |
Other |
0.4% |
Racial Distribution Key
White
Hispanic
Black
Asian
Native American
Other
Self-Identified Race |
Indian Mountain Lake, PA Population |
|---|---|
White |
53.0% |
Hispanic |
25.1% |
Black |
19.3% |
Asian |
2.4% |
Native American |
0.0% |
Other |
0.4% |
Diversity and Diversity Scores for Indian Mountain Lake, PA
The map below shows diversity in Indian Mountain Lake. 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.
Diversity Score
Diverse
Moderately diverse
No diversity
Indian Mountain Lake Diversity Score
95
With a diversity score of 95 out of 100, Indian Mountain Lake is much more diverse than other US cities. The most diverse area within Indian Mountain Lake's proper boundaries is to the east of the city. The least diverse areas are located in the northwest parts of Indian Mountain Lake.
Data Sources: U.S. Census, proprietary data mapping and analysis. Special thanks to the University of Virginia.
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