The map below shows majority race by area in Sugar Land, 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 Sugar Land racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Sugar Land overall is asian, making up 39.9% of residents. The next most-common racial group is white at 28.1%. There are more asian people in the west areas of the city. People who identify as white are most likely to be living in the east 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 Sugar Land
Self-Identified Race |
Sugar Land, TX Population |
|---|---|
White |
28.1% |
Hispanic |
16.7% |
Black |
11.8% |
Asian |
39.9% |
Native American |
0.1% |
Other |
3.4% |
Diversity and Diversity Scores for Sugar Land, TX
The map below shows diversity in Sugar Land. 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.
Sugar Land Diversity Score
71.9
More diverse than 99% of US cities
Sugar Land has a diversity score of 71.9: 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. Sugar Land is much more diverse than other US cities, more diverse than 99% of them. Within Sugar Land's proper boundaries, the most diverse area is northwest Sugar Land, and the least diverse areas are in west Sugar Land.
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.