The map below shows majority race by area in Green Sea, 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 Green Sea racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Green Sea overall is white, making up 72.4% of residents. The next most-common racial group is black at 21.9%. There are more white people in the northwest areas of the city. People who identify as black are most likely to be living in the southwest 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 Green Sea
Self-Identified Race |
Green Sea, SC Population |
|---|---|
White |
72.4% |
Hispanic |
1.2% |
Black |
21.9% |
Asian |
0.0% |
Native American |
0.1% |
Other |
4.4% |
Diversity and Diversity Scores for Green Sea, SC
The map below shows diversity in Green Sea. 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.
Green Sea Diversity Score
42.6
More diverse than 75% of US cities
Green Sea has a diversity score of 42.6: 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. Green Sea is more diverse than other US cities, more diverse than 75% of them. Within Green Sea's proper boundaries, the most diverse area is southwest Green Sea, and the least diverse areas are in northwest Green Sea.
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.