The map below shows majority race by area in Ladys Island, 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 Ladys Island racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Ladys Island overall is white, making up 79.4% of residents. The next most-common racial group is black at 11.6%. There are more white people in the north areas of the city. People who identify as black are most likely to be living in the south 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 Ladys Island
Self-Identified Race |
Ladys Island, SC Population |
|---|---|
White |
79.4% |
Hispanic |
6.8% |
Black |
11.6% |
Asian |
0.8% |
Native American |
0.0% |
Other |
1.5% |
Diversity and Diversity Scores for Ladys Island, SC
The map below shows diversity in Ladys Island. 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.
Ladys Island Diversity Score
35.2
More diverse than 68% of US cities
Ladys Island has a diversity score of 35.2: 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. Ladys Island is more diverse than other US cities, more diverse than 68% of them. Within Ladys Island's proper boundaries, the most diverse area is south Ladys Island, and the least diverse areas are in central Ladys Island.
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.