About this dataset

Context

This list ranks the 88 cities in the Los Angeles County, CA by Hispanic Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic Asian Population: This column displays the rank of cities in the Los Angeles County, CA by their Hispanic Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Asian Population: The Hispanic Asian population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Hispanic Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Los Angeles County Hispanic Asian Population: This tells us how much of the entire Los Angeles County, CA Hispanic Asian population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

The dataset tabulates the population of Los Angeles County by race. It includes the population of Los Angeles County across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Los Angeles County across relevant racial categories.

Key observations

The percent distribution of Los Angeles County population by race (across all racial categories recognized by the U.S. Census Bureau): 35.43% are white, 7.81% are Black or African American, 1.27% are American Indian and Alaska Native, 15.03% are Asian, 0.22% are Native Hawaiian and other Pacific Islander, 23.56% are some other race and 16.68% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Los Angeles County
• Population: The population of the racial category (excluding ethnicity) in the Los Angeles County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Los Angeles County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Los Angeles County Population by Race & Ethnicity. You can refer the same here

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Asian Alone (5-year estimate) in Los Angeles County, CA (B03002006E006037) from 2009 to 2023 about Los Angeles County, CA; Los Angeles; asian; CA; non-hispanic; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Asian Alone (5-year estimate) in Orange County, CA (B03002006E006059) from 2009 to 2023 about Orange County, CA; Los Angeles; asian; CA; non-hispanic; estimate; persons; 5-year; population; and USA.

Race categories for White, Black, Asian, American Indian or Alaska Native, Native Hawaiian or Pacific Islander, other race, and two or more races are non-Hispanic. Due to rounding, race and ethnicity categories may not sum to 100%. Estimates are based on provisional data and subject to change.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Population Estimate, Total, Not Hispanic or Latino, Asian Alone (5-year estimate) in Los Angeles County, CA was 1458413.00000 Persons in January of 2023, according to the United States Federal Reserve. Historically, Population Estimate, Total, Not Hispanic or Latino, Asian Alone (5-year estimate) in Los Angeles County, CA reached a record high of 1467279.00000 in January of 2020 and a record low of 1260344.00000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Estimate, Total, Not Hispanic or Latino, Asian Alone (5-year estimate) in Los Angeles County, CA - last updated from the United States Federal Reserve on June of 2025.

In 2023, there were over **** million White residents of Los Angeles city in California. In comparison, there were ******* Asian residents and ******* Black or African American residents amongst the Los Angeles population in that year.

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail.The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts.The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate.More information about these data is available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review FAQs.Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data.Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR).1. Population Density: 2020 Population per square mile,2. Poverty Rate: Percentage of population under 100% FPL,3. Median Household income: Based on countywide median HH income of $71,538.4. Highschool Education Attainment: Percentage of 18 years and older population without high school graduation.5. English Speaking Ability: Percentage of 18 years and older population with less or none English speaking ability. 6. Household without Internet Access: Percentage of HH without internet access.7. Non-Hispanic White Population: Percentage of Non-Hispanic White population.8. Non-Hispanic African-American Population: Percentage of Non-Hispanic African-American population.9. Non-Hispanic Asian Population: Percentage of Non-Hispanic Asian population.10. Hispanic Population: Percentage of Hispanic population.

The China Multi-Generational Panel Dataset - Liaoning (CMGPD-LN) is drawn from the population registers compiled by the Imperial Household Agency (neiwufu) in Shengjing, currently the northeast Chinese province of Liaoning, between 1749 and 1909. It provides 1.5 million triennial observations of more than 260,000 residents from 698 communities. The population mainly consists of immigrants from North China who settled in rural Liaoning during the early eighteenth century, and their descendants. The data provide socioeconomic, demographic, and other characteristics for individuals, households, and communities, and record demographic outcomes such as marriage, fertility, and mortality. The data also record specific disabilities for a subset of adult males. Additionally, the collection includes monthly and annual grain price data, custom records for the city of Yingkou, as well as information regarding natural disasters, such as floods, droughts, and earthquakes. This dataset is unique among publicly available population databases because of its time span, volume, detail, and completeness of recording, and because it provides longitudinal data not just on individuals, but on their households, descent groups, and communities.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Abstract (en): IIMMLA was supported by the Russell Sage Foundation. Since 1991, the Russell Sage Foundation has funded a program of research aimed at assessing how well the young adult offspring of recent immigrants are faring as they move through American schools and into the labor market. Two previous major studies have begun to tell us about the paths to incorporation of the children of contemporary immigrants: The Children of Immigrants Longitudinal Study (CILS), and the Immigrant Second Generation in New York study. The Immigration and Intergenerational Mobility in Metropolitan Los Angeles study is the third major initiative analyzing the progress of the new second generation in the United States. The Immigration and Intergenerational Mobility in Metropolitan Los Angeles (IIMMLA) study focused on young adult children of immigrants (1.5- and second-generation) in greater Los Angeles. IIMMLA investigated mobility among young adult (ages 20-39) children of immigrants in metropolitan Los Angeles and, in the case of the Mexican-origin population there, among young adult members of the third- or later generations. The five-county Los Angeles metropolitan area (Los Angeles, Orange, Ventura, Riverside and San Bernardino counties) contains the largest concentrations of Mexicans, Salvadorans, Guatemalans, Filipinos, Chinese, Vietnamese, Koreans, and other nationalities in the United States. The diverse migration histories and modes of incorporation of these groups made the Los Angeles metropolitan area a strategic choice for a comparison study of the pathways of immigrant incorporation and mobility from one generation to the next. The IIMMLA study compared six foreign-born (1.5-generation) and foreign-parentage (second-generation) groups (Mexicans, Vietnamese, Filipinos, Koreans, Chinese, and Central Americans from Guatemala and El Salvador) with three native-born and native-parentage comparison groups (third- or later-generation Mexican Americans, and non-Hispanic Whites and Blacks). The targeted groups represent both the diversity of modes of incorporation in the United States and the range of occupational backgrounds and immigration status among contemporary immigrants (from professionals and entrepreneurs to laborers, refugees, and unauthorized migrants). The surveys provide basic demographic information as well as extensive data about socio-cultural orientation and mobility (e.g., language use, ethnic identity, religion, remittances, intermarriage, experiences of discrimination), economic mobility (e.g., parents' background, respondents' education, first and current job, wealth and income, encounters with the law), geographic mobility (childhood and present neighborhood of residence), and civic engagement and politics (political attitudes, voting behavior, as well as naturalization and transnational ties). ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Young adults aged 20-39 from six foreign-born and foreign-parentage groups: Mexican, Vietnamese, Filipino, Korean, Chinese, and Central American (Guatemalan and Salvadoran), as well as native-born and native-parentage Mexican-Americans, and non-Hispanic Whites and Blacks, in the Los Angeles metropolitan area. Multistage random sampling. 2008-07-01 Edits were made to the metadata record. Funding insitution(s): Russell Sage Foundation. telephone interview Data collection for IIMMLA was subcontracted to and carried out by the Field Research Corporation, San Francisco, CA.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..Armed Forces data are not shown for the population 65 years and over..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.