The map below shows majority race by area in Mount Aetna, 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 Mount Aetna racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Mount Aetna overall is white, making up 91.3% of residents. The next most-common racial group is hispanic at 4.1%. There are more white people in the southeast areas of the city. People who identify as hispanic are most likely to be living in the central 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 Mount Aetna
Self-Identified Race |
Mount Aetna, PA Population |
|---|---|
White |
91.3% |
Hispanic |
4.1% |
Black |
0.6% |
Asian |
0.4% |
Native American |
0.0% |
Other |
3.6% |
Diversity and Diversity Scores for Mount Aetna, PA
The map below shows diversity in Mount Aetna. 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.
Mount Aetna Diversity Score
16.3
More diverse than 37% of US cities
Mount Aetna has a diversity score of 16.3: 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. Mount Aetna is less diverse than other US cities, more diverse than 37% of them. Within Mount Aetna's proper boundaries, the most diverse area is central Mount Aetna, and the least diverse areas are in southeast Mount Aetna.
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.