Race, Diversity, and Ethnicity in Lancaster, CA

The map below shows majority race by area in Lancaster, 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 Lancaster racial diversity and diversity scores. Click here to scroll to diversity data.

Lancaster, CA map of race and ethnicity
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White
Hispanic
Black
Asian
Multiple/Other
WhiteHispanicBlackAsianMultiple/Other
Majority race by area

The majority race in Lancaster overall is hispanic, making up 46.1% of residents. The next most-common racial group is white at 28.6%. There are more hispanic people in the east areas of the city. People who identify as white are most likely to be living in the northwest 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 Lancaster
Race
Lancaster, CA Population
White
28.6%
Hispanic
46.1%
Black
17.3%
Asian
3.6%
Native American
0.6%
Other
3.8%
 

Racial Distribution Key

White
Hispanic
Black
Asian
Native American
Other
 
Self-Identified Race
Lancaster, CA Population
White
28.6%
Hispanic
46.1%
Black
17.3%
Asian
3.6%
Native American
0.6%
Other
3.8%
   

Diversity and Diversity Scores for Lancaster, CA

The map below shows diversity in Lancaster. 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.

Lancaster, CA map of race and ethnicity
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Diversity Score

Diverse
Moderately diverse
No diversity
 

Lancaster Diversity Score

98

With a diversity score of 98 out of 100, Lancaster is much more diverse than other US cities. The most diverse area within Lancaster's proper boundaries is to the west of the city. The least diverse areas are located in the north parts of Lancaster.

Data Sources: U.S. Census, proprietary data mapping and analysis. Special thanks to the University of Virginia.

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