The map below shows majority race by area in Mount Olive, 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 Olive racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Mount Olive overall is white, making up 57.9% of residents. The next most-common racial group is black at 37.9%. There are more white people in the northeast areas of the city. People who identify as black 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 Olive
Self-Identified Race |
Mount Olive, MS Population |
|---|---|
White |
57.9% |
Hispanic |
1.3% |
Black |
37.9% |
Asian |
0.1% |
Native American |
0.0% |
Other |
2.7% |
Diversity and Diversity Scores for Mount Olive, MS
The map below shows diversity in Mount Olive. 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 Olive Diversity Score
52.0
More diverse than 86% of US cities
Mount Olive has a diversity score of 52.0: 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 Olive is much more diverse than other US cities, more diverse than 86% of them. Within Mount Olive's proper boundaries, the most diverse area is west Mount Olive, and the least diverse areas are in central Mount Olive.
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.