This layer shows Hispanic or Latino origin by specific origin. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of the population with Hispanic or Latino origins. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2016-2020ACS Table(s): B03001 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: March 17, 2022The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

A layer that displays hispanic/latino predominance of Central American origin including: Mexican, Honduran, Nicaraguan, Guatemalan, Panamanian, and Costa Rican. The data is displayed across Broward County defined by the census tract geography.

Data Driven Detroit calculated the predominant race (if any) for census tracts in the Detroit, Tri-County region. The data come from the 2010 Census PL file. The census table splits out races by hispanic and non-hispanic ethnicity. For the purposes of this feature, White, Black, Hispanic or no predominant race were used as the possible categories. If there was no race or ethnicnicity over 50% of the population, then there is no predominant race.

A layer that displays hispanic/latino predominance of Caribbean Island Hispanic Groups including: Cuban, Puerto Rican, and Dominican. The data is displayed across Broward County defined by the census tract geography.

This multi-scale map shows the predominant (most numerous) race/ethnicity living within an area. Map opens at the state level, centered on the lower 48 states. Data is from U.S. Census Bureau's 2020 PL 94-171 data for state, county, tract, block group, and block.The map's colors indicate which of the eight race/ethnicity categories have the highest total count.Race and ethnicity highlights from the U.S. Census Bureau:White population remained the largest race or ethnicity group in the United States, with 204.3 million people identifying as White alone. Overall, 235.4 million people reported White alone or in combination with another group. However, the White alone population decreased by 8.6% since 2010.Two or More Races population (also referred to as the Multiracial population) has changed considerably since 2010. The Multiracial population was measured at 9 million people in 2010 and is now 33.8 million people in 2020, a 276% increase.“In combination” multiracial populations for all race groups accounted for most of the overall changes in each racial category.All of the race alone or in combination groups experienced increases. The Some Other Race alone or in combination group (49.9 million) increased 129%, surpassing the Black or African American population (46.9 million) as the second-largest race alone or in combination group.The next largest racial populations were the Asian alone or in combination group (24 million), the American Indian and Alaska Native alone or in combination group (9.7 million), and the Native Hawaiian and Other Pacific Islander alone or in combination group (1.6 million).Hispanic or Latino population, which includes people of any race, was 62.1 million in 2020. Hispanic or Latino population grew 23%, while the population that was not of Hispanic or Latino origin grew 4.3% since 2010.View more 2020 Census statistics highlights on race and ethnicity.

This layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. (This map is embedded in the Roanoke County Demographics Website, and thus the county has been filtered to be the only geography shown.)

Data Driven Detroit calculated the predominant race (if any) for census tracts in the Detroit, Tri-County region. The data come from the 2010 Census PL file. The census table splits out races by hispanic and non-hispanic ethnicity. For the purposes of this feature, White, Black, Hispanic or no predominant race were used as the possible categories. If there was no race or ethnicnicity over 50% of the population, then there is no predominant race.

Relative concentration of the Sierra Nevada region's Hispanic and/or Black, Indigenous or person of color (HSPBIPOC) population. The variable HSPBIPOC is equivalent to all individuals who select a combination of racial and ethnic identity in response to the Census questionnaire EXCEPT those who select "not Hispanic" for the ethnic identity question, and "white race alone" for the racial identity question. This is the most encompassing possible definition of racial and ethnic identities that may be associated with historic underservice by agencies, or be more likely to express environmental justice concerns (as compared to predominantly non-Hispanic white communities). Until 2021, federal agency guidance for considering environmental justice impacts of proposed actions focused on how the actions affected "racial or ethnic minorities." "Racial minority" is an increasingly meaningless concept in the USA, and particularly so in California, where only about 3/8 of the state's population identifies as non-Hispanic and white race alone - a clear majority of Californians identify as Hispanic and/or not white. Because many federal and state map screening tools continue to rely on "minority population" as an indicator for flagging potentially vulnerable / disadvantaged/ underserved populations, our analysis includes the variable HSPBIPOC which is effectively "all minority" population according to the now outdated federal environmental justice direction. A more meaningful analysis for the potential impact of forest management actions on specific populations considers racial or ethnic populations individually: e.g., all people identifying as Hispanic regardless of race; all people identifying as American Indian, regardless of Hispanic ethnicity; etc.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as HSPBIPOC alone to the proportion of all people that live within the 775 block groups in the Sierra Nevada RRK region that identify as HSPBIPOC alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of HSPBIPOC individuals compared to the Sierra Nevada RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HSPBIPOC individuals are highly concentrated locally.

Relative concentration of the Northern California region's Hispanic and/or Black, Indigenous or person of color (HSPBIPOC) population. The variable HSPBIPOC is equivalent to all individuals who select a combination of racial and ethnic identity in response to the Census questionnaire EXCEPT those who select "not Hispanic" for the ethnic identity question, and "white race alone" for the racial identity question. This is the most encompassing possible definition of racial and ethnic identities that may be associated with historic underservice by agencies, or be more likely to express environmental justice concerns (as compared to predominantly non-Hispanic white communities). Until 2021, federal agency guidance for considering environmental justice impacts of proposed actions focused on how the actions affected "racial or ethnic minorities." "Racial minority" is an increasingly meaningless concept in the USA, and particularly so in California, where only about 3/8 of the state's population identifies as non-Hispanic and white race alone - a clear majority of Californians identify as Hispanic and/or not white. Because many federal and state map screening tools continue to rely on "minority population" as an indicator for flagging potentially vulnerable / disadvantaged/ underserved populations, our analysis includes the variable HSPBIPOC which is effectively "all minority" population according to the now outdated federal environmental justice direction. A more meaningful analysis for the potential impact of forest management actions on specific populations considers racial or ethnic populations individually: e.g., all people identifying as Hispanic regardless of race; all people identifying as American Indian, regardless of Hispanic ethnicity; etc.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as HSPBIPOC alone to the proportion of all people that live within the 1,207 block groups in the Northern California RRK region that identify as HSPBIPOC alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of HSPBIPOC individuals compared to the Northern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HSPBIPOC individuals are highly concentrated locally.

This layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area, and the total population in that area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B03002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This graph shows the population of the U.S. by race and ethnic group from 2000 to 2023. In 2023, there were around 21.39 million people of Asian origin living in the United States. A ranking of the most spoken languages across the world can be accessed here. U.S. populationCurrently, the white population makes up the vast majority of the United States’ population, accounting for some 252.07 million people in 2023. This ethnicity group contributes to the highest share of the population in every region, but is especially noticeable in the Midwestern region. The Black or African American resident population totaled 45.76 million people in the same year. The overall population in the United States is expected to increase annually from 2022, with the 320.92 million people in 2015 expected to rise to 341.69 million people by 2027. Thus, population densities have also increased, totaling 36.3 inhabitants per square kilometer as of 2021. Despite being one of the most populous countries in the world, following China and India, the United States is not even among the top 150 most densely populated countries due to its large land mass. Monaco is the most densely populated country in the world and has a population density of 24,621.5 inhabitants per square kilometer as of 2021. As population numbers in the U.S. continues to grow, the Hispanic population has also seen a similar trend from 35.7 million inhabitants in the country in 2000 to some 62.65 million inhabitants in 2021. This growing population group is a significant source of population growth in the country due to both high immigration and birth rates. The United States is one of the most racially diverse countries in the world.

Relative concentration of the Southern California region's Black/African American population. The variable HSPBIPOC is equivalent to all individuals who select a combination of racial and ethnic identity in response to the Census questionnaire EXCEPT those who select "not Hispanic" for the ethnic identity question, and "white race alone" for the racial identity question. This is the most encompassing possible definition of racial and ethnic identities that may be associated with historic underservice by agencies, or be more likely to express environmental justice concerns (as compared to predominantly non-Hispanic white communities). Until 2021, federal agency guidance for considering environmental justice impacts of proposed actions focused on how the actions affected "racial or ethnic minorities." "Racial minority" is an increasingly meaningless concept in the USA, and particularly so in California, where only about 3/8 of the state's population identifies as non-Hispanic and white race alone - a clear majority of Californians identify as Hispanic and/or not white. Because many federal and state map screening tools continue to rely on "minority population" as an indicator for flagging potentially vulnerable / disadvantaged/ underserved populations, our analysis includes the variable HSPBIPOC which is effectively "all minority" population according to the now outdated federal environmental justice direction. A more meaningful analysis for the potential impact of forest management actions on specific populations considers racial or ethnic populations individually: e.g., all people identifying as Hispanic regardless of race; all people identifying as American Indian, regardless of Hispanic ethnicity; etc.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as HSPBIPOC alone to the proportion of all people that live within the 13,312 block groups in the Southern California RRK region that identify as HSPBIPOC alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of HSPBIPOC individuals compared to the Southern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HSPBIPOC individuals are highly concentrated locally.

A web map for the 2022 BBTN- Hispanic Population issue. This map displays the Hispanic predominance in Broward County by census tract.

Data Source: Open Data DC and American Community Survey (ACS) 1-Year Estimates.

Why This Matters

Living further from full-service grocery stores can force residents to shop for food at locations that are more expensive or have fewer healthy options, leading to worse health outcomes for conditions such as obesity or diabetes.

Beyond basic nutrition, food is an integral part of culture. Having access to a wide array of culturally relevant foods has been shown to improve well-being among Black, Indigenous, and people of color communities.

Across the United States, predominantly-Black communities have fewer supermarkets than predominantly white and Hispanic communities. A pattern of disinvestment limits the availability of fresh and healthy foods.

The District Response

The Food Access Fund (FAF) Grant increases equitable access to fresh, healthy, and affordable food by supporting the opening of new grocery stores in areas with low food access, with priority given to locations in Ward 7 or Ward 8. The Produce Plus Program provides financial support for residents with low access to fresh foods to spend at local farmers markets.

The SUN Bucks program provides additional grocery-buying benefits to income-eligible families when schools are closed for the summer and children no longer have access to free or reduced-cost meals at school.

The DC Food Policy Council convenes six working groups, including the Food Access & Equity working group that aims to communicate and collaborate with residents to increase awareness of District food benefit programs and healthy food retail.

This study examined reasons for participation in a genetic study of risk for multiple sclerosis (MS). Our sample consisted of 101 patients diagnosed with MS who were approached about enrolling in the Multiple Sclerosis Genetic Susceptibility Study. Participants were predominantly Hispanic (80%), female (80%), and well educated (71%), having at least some level of college education. Of these 101 individuals who were approached, 95 agreed to participate and are the focus of this report. Among enrollees, the most frequently cited reasons for participation were to find a cure for MS (56%), having MS (46%), and helping future generations (37%). Regression models comparing ethnic groups, Hispanics endorsed having MS as a reason to participate significantly more frequently than non-Hispanics (HI 52%, non-HI 19%, p = 0.015) while non-Hispanics endorsed finding new and better treatments significantly more frequently than Hispanics (Hispanic 17%, non-Hispanic 50%, p = 0.003). Among our three age groups, younger individuals endorsed finding a cure for MS significantly more frequently (74% of 18–35-year olds vs. 56% of 36–55 year olds vs. 39% of >55 year olds). Our results suggest that motivations for participation in genetic research vary by ethnicity, and that these influences need to be considered in developing more inclusive programs of disease-related genetic research. Future efforts should focus on development of standard methods for understanding participation in genetic and genomic research, especially among underrepresented groups as a catalyst for engaging all populations.

The aim of this study was to examine the association between adverse childhood experiences (ACEs) and risk for depression among 480 predominantly low-income Hispanic/Latina women in the Maternal and Development Risks from Environmental and Social Stressors pregnancy cohort. Models were fitted to evaluate associations between ACEs and prenatal probable depression measured by the Center for Epidemiologic Studies-Depression Scale adjusting for recruitment site, age, income, race/ethnicity, marital status and parity. The ACEs Questionnaire parameterised experiences as counts (0–10), categories (0, 1–3 and 4+ ACEs) and domains. Participants had a significantly higher likelihood of prenatal probable depression per unit increase in ACEs count or if they reported 4+ ACEs relative to 0 ACEs. Higher likelihood of probable depression was also associated with higher counts of each ACEs domains: abuse, neglect and household dysfunction. Findings suggest systematic screening for depressive symptoms in those with a history of childhood adversities may be important in prenatal care practice.Impact StatementWhat is already known on this subject? Experiencing depression during pregnancy has been associated with later adverse maternal mental and physical health outcomes. Emerging studies indicate that adverse childhood experiences (ACEs) may maintain or increase the predisposition to prenatal depression.What do the results of this study add? Although prenatal depressive symptoms are prevalent among racial/ethnic minority samples including Hispanic/Latinas, research determining whether the association between ACEs and prenatal depression varies by nativity is scarce. Overall, ACEs were common among Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) participants and were associated with a higher likelihood of probable depression during pregnancy. These patterns did not significantly differ among the foreign-born versus U.S.-born Hispanic/Latina women, although the associations were stronger among U.S.-born Hispanic/Latina women.What are the implications of these findings for clinical practice and/or further research? Research should continue to focus on the effects of ACEs in communities that have been historically excluded in perinatal mental health services such as pregnant women from racial and ethnic minority groups. It may be important for clinicians to routinely screen for mental health during pregnancy as an adverse, psychological environment may impact both women and children. These findings suggest a need for improvement in systematic screening for depressive symptoms in those with a history of childhood adversities. What is already known on this subject? Experiencing depression during pregnancy has been associated with later adverse maternal mental and physical health outcomes. Emerging studies indicate that adverse childhood experiences (ACEs) may maintain or increase the predisposition to prenatal depression. What do the results of this study add? Although prenatal depressive symptoms are prevalent among racial/ethnic minority samples including Hispanic/Latinas, research determining whether the association between ACEs and prenatal depression varies by nativity is scarce. Overall, ACEs were common among Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) participants and were associated with a higher likelihood of probable depression during pregnancy. These patterns did not significantly differ among the foreign-born versus U.S.-born Hispanic/Latina women, although the associations were stronger among U.S.-born Hispanic/Latina women. What are the implications of these findings for clinical practice and/or further research? Research should continue to focus on the effects of ACEs in communities that have been historically excluded in perinatal mental health services such as pregnant women from racial and ethnic minority groups. It may be important for clinicians to routinely screen for mental health during pregnancy as an adverse, psychological environment may impact both women and children. These findings suggest a need for improvement in systematic screening for depressive symptoms in those with a history of childhood adversities.

Objective(s)Culinary education may be one way to improve children's eating behaviors. We formatively evaluated the effect of a hands-on afterschool 12-module, registered dietitian-led culinary education program on healthy eating behaviors in a predominately Hispanic/Latino, low-socioeconomic community.MethodsOf 234 children participating in the program, 77% completed both pre- and post-assessment surveys (n = 180; mean age 9.8 years; 63.3% female; 74.3% Hispanic/Latino, 88.4% receiving free/reduced lunch). In addition to program satisfaction, we assessed changes in children's self-reported fruit, vegetable, and whole-grain consumption, knowledge, and culinary skills using binary and continuous mixed effects models. We report false discovery rate adjusted p-values and effect sizes.Results95.5% of participants reported liking the program. Improved whole grain consumption had a medium effect size, while effect sizes for whole grain servings and vegetable consumption were small, but significant (all p < 0.05). Culinary skills increased between 15.1 to 43.4 percent points (all p < 0.01), with medium to large effect sizes.Conclusion(s)The program was well-received by participants. Participants reported improved eating behaviors and culinary skills after program completion. Therefore, this hands-on afterschool culinary education program can help improve healthy eating in a predominantly Hispanic/Latino, low-socioeconomic community.

This is a de identified data set including 719 patients older than 50 years of age. The patients has a fragility fracture of the hip, shoulder, wrist and ankle. Comorbidities and medications taken at the time of presentation are documented.

A web mapping application that displays the overall Hispanic Latino predominance map and by origin groups for Census Tracts in Broward County.

This multi-scale map shows the predominant (most numerous) race/ethnicity living within an area. Map opens at the state level, centered on the lower 48 states. Data is from U.S. Census Bureau's 2020 PL 94-171 data for state, county, tract, block group, and block.The map's colors indicate which of the eight race/ethnicity categories have the highest total count.Race and ethnicity highlights from the U.S. Census Bureau:White population remained the largest race or ethnicity group in the United States, with 204.3 million people identifying as White alone. Overall, 235.4 million people reported White alone or in combination with another group. However, the White alone population decreased by 8.6% since 2010.Two or More Races population (also referred to as the Multiracial population) has changed considerably since 2010. The Multiracial population was measured at 9 million people in 2010 and is now 33.8 million people in 2020, a 276% increase.“In combination” multiracial populations for all race groups accounted for most of the overall changes in each racial category.All of the race alone or in combination groups experienced increases. The Some Other Race alone or in combination group (49.9 million) increased 129%, surpassing the Black or African American population (46.9 million) as the second-largest race alone or in combination group.The next largest racial populations were the Asian alone or in combination group (24 million), the American Indian and Alaska Native alone or in combination group (9.7 million), and the Native Hawaiian and Other Pacific Islander alone or in combination group (1.6 million).Hispanic or Latino population, which includes people of any race, was 62.1 million in 2020. Hispanic or Latino population grew 23%, while the population that was not of Hispanic or Latino origin grew 4.3% since 2010.View more 2020 Census statistics highlights on race and ethnicity.