The map below shows majority race by area in Mount Lucas, 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 Lucas racial diversity and diversity scores. Click here to scroll to diversity data.
The majority race in Mount Lucas overall is white, making up 56.0% of residents. The next most-common racial group is other at 22.3%. There are more white people in the southeast areas of the city. People who identify as other 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 Mount Lucas
Self-Identified Race |
Mount Lucas, TX Population |
|---|---|
White |
56.0% |
Hispanic |
21.1% |
Black |
0.1% |
Asian |
0.5% |
Native American |
0.0% |
Other |
22.3% |
Diversity and Diversity Scores for Mount Lucas, TX
The map below shows diversity in Mount Lucas. 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 Lucas Diversity Score
59.2
More diverse than 94% of US cities
Mount Lucas has a diversity score of 59.2: 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. Mount Lucas is much more diverse than other US cities, more diverse than 94% of them. Within Mount Lucas's proper boundaries, the most diverse area is east Mount Lucas, and the least diverse areas are in southeast Mount Lucas.
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.