The map below shows majority race by area in Old Neely, 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 Old Neely racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Old Neely overall is white, making up 86.1% of residents. The next most-common racial group is asian at 6.8%. There are more white people in the southwest 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 Old Neely
Self-Identified Race |
Old Neely, AR Population |
|---|---|
White |
86.1% |
Hispanic |
3.3% |
Black |
0.0% |
Asian |
6.8% |
Native American |
0.0% |
Other |
3.8% |
Diversity and Diversity Scores for Old Neely, AR
The map below shows diversity in Old Neely. 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.
Old Neely Diversity Score
25.2
More diverse than 55% of US cities
Old Neely has a diversity score of 25.2: 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. Old Neely is about average for diversity versus other US cities, more diverse than 55% of them. Within Old Neely's proper boundaries, the most diverse area is north Old Neely, and the least diverse areas are in southwest Old Neely.
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.