Summary: Average travel time to work in minutes, one-way, for workers age 16 or older who work outside of home. Data for 2010 and 2020 represent five-year averages (e.g., 2016-2020).

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 1990 and 2000 5% samples, 2010 and 2020 American Community Survey 5-year samples. IPUMS NHGIS, University of Minnesota, www.nhgis.org, NHGIS crosswalk files, 2020 blocks to 2010 blocks.

Universe: All workers age 16 or older who work outside of home.

Methods: The commute time among workers age 16 or older who work outside of home was calculated by race/ethnicity, gender, nativity, ancestry, commute mode, and poverty level for each year and geography. Private vehicles include cars, trucks, and motorcycles; public transportation includes buses, streetcars, subways/light rails, railroads, taxicabs, and ferryboats; walk or bike includes bicycles, walking, and other modes. For the map breakdown, census tract level data from the 2020 5-year American Community survey was re-estimated into 2010 census tract boundaries using a 2020 to 2010 census tract geographic crosswalk developed using 2020 block level population data from the 2020 Census Redistricting Data along with a block level geographic crosswalk (2020 to 2010 blocks) from NHGIS. See the methodology page for other relevant notes.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• Workers are defined as people who reported working during the week prior to the survey (and they had to have worked outside of home to be included in the universe).
• Data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.
• No data are reported if based on fewer than 100 individual (i.e., unweighted) survey respondents in the universe for the by race/ethnicity, trend, by gender, by nativity, by ancestry, by poverty, and ranking breakdowns.
• No data are reported if based on fewer than 500 people in the universe for the map breakdown; a lower minimum threshold for reporting of at least or 100 people was applied for census tract level estimates in the map breakdown.

Summary: The share of owner- and renter-occupied households that are cost-burdened (spending more than 30 percent of income on housing costs) and "severely" cost-burdened (more than 50 percent). Data for 2010 and 2020 represent five-year averages (e.g., 2016-2020).

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 1990 and 2000 5% samples, 2010 and 2020 American Community Survey 5-year samples; U.S. Census Bureau, 2010 and 2020 American Community Survey 5-year Summary Files; GeoLytics, Inc., 2000 Long Form in 2010 Boundaries; IPUMS NHGIS, University of Minnesota, www.nhgis.org, NHGIS crosswalk files, 2020 blocks to 2010 blocks.

Universe: Occupied households with housing costs, excluding non-traditional owner-occupied households (e.g., multi-unit structures and trailers).

Methods: The number and percentage of burdened and severely burdened households was calculated by tenure (owner vs. renter), race/ethnicity, gender, nativity, ancestry, and poverty level for each year and geography. Housing costs for renters include contract rent as well as utilities while housing costs for owners includes most costs of owning a home such as mortgage, insurance, utilities, real estate taxes and other costs. For the map breakdown, census tract level data from the 2020 5-year American Community survey was re-estimated into 2010 census tract boundaries using a 2020 to 2010 census tract geographic crosswalk developed using 2020 block level population data from the 2020 Census Redistricting Data along with a block level geographic crosswalk (2020 to 2010 blocks) from NHGIS. See the methodology page for other relevant notes.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• Demographic characteristics are based on those of the householder.
• Housing costs are based on the month the survey was conducted.
• Income data for 1990 and 2000 is based on a survey that year but reflects income from the year prior, while income data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.
• No data are reported if based on fewer than 100 individual (i.e., unweighted) householders in the universe for the race/ethnicity, trend, by gender, by nativity, by ancestry, by poverty, and ranking breakdowns.
• No data are reported if based on fewer than 500 householders in the universe for the map breakdown; a lower minimum threshold for reporting of at least or 100 householders was applied for census tract level estimates in the map breakdown.

Summary: The percentage of households with no vehicle. Data for 2010 and 2022 represent five-year averages (e.g., 2018-2022).

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 1990 and 2000 5% samples, 2010 and 2022 American Community Survey 5-year samples.

Universe: All households.

Methods: The percentage of households with no vehicle was calculated by race/ethnicity, gender, nativity, and ancestry for each year and geography. See the methodology page for other relevant notes.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• Data for 2010 and 2022 represent 2006-2010 and 2018-2022 averages, respectively.

Summary: Index of exposure to air toxics for cancer and non-cancer risk (combined and separately). Values range from 1 (lowest risk) to 100 (highest risk) on a national scale based on the distribution across census tracts nationwide. For example, a value of 65 for Latinos in a given region suggests that the average pollution exposure for Latinos in that region is equivalent to the census tract that ranks at the 65th percentile nationally in pollution exposure (i.e., has more exposure than 64 percent of US tracts but less exposure than 35 percent of tracts).

Data Source(s): U.S. Environmental Protection Agency (EPA), 1999 and 2011 National Air Toxics Assessment (NATA), and 2018 Air Toxics Screening Assessment (AirToxScreen); U.S. Census Bureau, 2000 Decennial Census Summary File 3, 2010 and 2020 American Community Survey (ACS) 5-Year Summary Files.

Universe: All people.

Methods: The air pollution exposure index was calculated by race/ethnicity, source, and poverty status (above and below the federal poverty level) for each year and geography. Indicator values for 2000 are based on the 1999 NATA and the 2000 Census summary file; values for 2010 are based on the 2011 NATA and the 2010 ACS 5-year summary file; values for 2020 are based on the 2018 AirToxScreen and the 2020 ACS 5-year summary file. In 2017, the EPA succeeded NATA with AirToxScreen. This is part of the EPA’s effort to provide more timely data to better serve the needs of air agencies and the public. The 2018 release was the latest version available at the time of our last update of the air pollution indicator. See the methodology page for other relevant notes.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• The index value is based on percentile ranking each risk measure across all census tracts in the US and taking the average ranking for each Atlas geography and demographic group.
• The NATA/AirToxScreen includes Diesel Particulate Matter exposure in the non-cancer risk estimates but not in the cancer risk estimates (despite being a known carcinogen).
• Data for 2000 are based on the 1999 NATA and the 2000 Census summary file.
• Data for 2010 are based on the 2011 NATA and the 2010 ACS 5-year summary file.
• Data for 2020 are based on the 2018 AirToxScreen and the 2020 ACS 5-year summary file.

Summary: The percentage of the population living in high-poverty neighborhoods, defined as census tracts with a poverty rate of 30 percent or higher. Data for 2010 and 2020 represent five-year averages (e.g., 2016-2020).

Data Source(s): U.S. Census Bureau, 2010 and 2020 American Community Survey (ACS) 5-year Summary Files; Geolytics, Inc., 1990 and 2000 Long Form in 2010 Boundaries; IPUMS NHGIS, University of Minnesota, www.nhgis.org, NHGIS crosswalk files, 2020 blocks to 2010 blocks.

Universe: All people.

Methods: The percentage of people living in high-poverty neighborhoods was calculated by race/ethnicity for each year and geography. The census tract geography changes with each decennial census, which can be problematic for analyzing changes in neighborhood poverty over time. In order to ensure a consistent 2010 tract boundary geographic basis for our calculations, we used data from GeoLytics, Inc. to derive neighborhood poverty estimates for 1990 and 2000. While this data originates from the 1990 and 2000 Census summary files, it has been “re-shaped” to be expressed in 2010 tract boundaries, which is the geographic basis of the 2010 ACS summary file. To derive estimates for 2020, we re-estimated data from the 2020 5-year ACS into 2010 census tract boundaries using a 2020 to 2010 census tract geographic crosswalk developed using 2020 block level population data from the 2020 Census Redistricting Data along with a block level geographic crosswalk (2020 to 2010 blocks) from NHGIS. See the methodology page for other relevant notes.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• High poverty neighborhoods are those with poverty rates of at least 30 percent.
• Data for 2010 and 2020 represent 2006-2010 and 2016-2020 averages, respectively.

Summary: The number and percentage of employed people age 16 or older by occupation. Data represent a 2018-2022 average.

Data Source(s): Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 2022 American Community Survey 5-year sample.

Universe: The employed civilian noninstitutionalized population age 16 or older.

Methods: The number and percentage of workers age 16 or older within occupational groups and detailed occupations was calculated by race/ethnicity, gender, and nativity. Occupational groups are defined based on the first two digits of the IPUMS USA variable OCCSOC while detailed occupations are defined based on the full six-digit OCCSOC code. The names of detailed occupations were abbreviated in many cases for aesthetic purposes. See the methodology page for other relevant notes.

Notes:

• Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
• Data represent a 2018-2022 average.