Predictors of Upward Mobility for Fresno, California

Fresno Demographics:
Inclusive Recovery Indices
Economic Development:
Human Development:
Neighborhood Development:

Fresno Demographics:

Race and Ethnicity Overview

Click the image above to expand

Click the image above to expand

Comparing Fresno Race and Ethnicities over Time

Race and Ethnicity in Fresno (county), CA
Population estimates, 2010 - 2020
Race and Ethnicity in Fresno (county), CA
Population estimates for smaller populations, 2010 - 2020
Mexican population in Fresno (county), CA
Population Estimates for race and ethnicity subgroups, 2010 - 2020
Asian Indian population in Fresno (county), CA
Population Estimates for race and ethnicity subgroups, 2010 - 2020
Hmong population in Fresno (county), CA
Population Estimates for race and ethnicity subgroups, 2010 - 2020
Race and Ethnicity in Fresno (city), CA
Population estimates, 2010 - 2020
Race and Ethnicity in Fresno (city), CA
Population estimates for smaller populations, 2010 - 2020
Mexican population in Fresno (city), CA
Population Estimates for race and ethnicity subgroups, 2010 - 2020
Asian Indian population in Fresno (city), CA
Population Estimates for race and ethnicity subgroups, 2010 - 2020
Hmong population in Fresno (city), CA
Population Estimates for race and ethnicity subgroups, 2010 - 2020

Source: 5-year 2018-2022 American Community Survey data (treemap plots) and 5-year 2008-2012, 2013-2017, and 2018-2022 American Community Survey data (line charts).

Notes: The charts above show the racial and ethnic breakdown of Fresno City and Fresno County from the 2008-2012, 2013-2017, and 2018-2022 5-year American Community Survey data. 2018-2022 data is the most recently available data at the time of the dashboard’s creation. The subgroups shown are identified by the Census. We include Hmong, Asian Indian, and Mexican because they compose a large share of Fresno’s population and because they are reliable (see below for more details).

The Boosting Upward Mobility framework includes a “Descriptive Representation” predictor which is defined as the “[r]atio of the share of local elected officials of a racial or ethnic group to the share of residents of the same racial or ethnic group.” Instead of collecting demographic information from elected officials, this dashboard shows a more detailed racial and ethnic breakdown of Fresno. You can a list see Fresno (county)’s elected officials here and learn more about them at the county registrar website.

There is inherent unreliability in American Community Survey data. To determine whether or not to visualize racial or ethnic subgroups, we use the Coefficient of Variation (CV) where \(CV = standard error/ estimate\). While there is not a hard and fast rule, academic literature suggests that data with a CV below .12 can be considered “highly reliable,” and a CV between .12 and .40 can be considered to have “medium reliability.” In general, all data plotted is of “high reliability,” but two exceptions to this are made. First, the county-level Hispanic data from the American Community Survey data pulled is missing margin of error columns. However, the Fresno city-level Hispanic data has a CV below .12, and the county is a larger geography. This suggests that the CV would also fall below .12. Second, the Some Other Race data at the county scale and the Some Other Race, American Indian or Alaska Native, and Native Hawaiian or Other Pacific Islander data tend to have “medium reliability.” Consequently, for these charts, error bars are also plotted. Even so, incorporating these populations into the dashboard is important so that all people viewing the it can see themselves to at least some extent in these charts. Lastly, note that the Census Bureau reports that interpreting changes over time can be challenging due to changes in weighting methodology and the tendency of multi-year estimates to smooth out sudden changes.

Inclusive Recovery Indices:

Fresno Rank on Overall Inclusion Among 274 Large American Cities
Where higher ranks indicate less inclusive cities
Fresno Rank on Racial Inclusion Among 274 Large American Cities
Where higher ranks indicate less inclusive cities
Fresno Rank on Economic Inclusion Among 274 Large American Cities
Where higher ranks indicate less inclusive cities

Source: The Inclusive Recovery Indices were developed by the Urban Institute. The data presented here is a compilation of metrics previously developed for the years 1980, 1990, 2000, 2013, and 2016. That data can be downloaded here. Index results from 2020 were developed specifically for this project.

Notes: The economic inclusion index is a combination of four variables capturing the following concepts: income segregation, rent burden, working poor, and percent 16- to 19-year-olds who are not enrolled in school and are not high school graduates. The racial inclusion index is composed of variables capturing the following five concepts: racial segregation, percentage people of color, poverty gap between Non-Hispanic White people and people of color, racial education gap between Non-Hispanic White people and people of color, and homeownership gap between Non-Hispanic White people and people of color. The overall inclusion index is a combination of the four economic inclusion indices and the five racial inclusion indices. The 274 cities were selected because they had populations greater than 100,000 in any decade between 1970 and 2010. The cities also were required to be “incorporated,” meaning that they have municipal governments. To read more about the creation of these metrics, please see this Urban Institute report.

Economic Development:

Employment opportunities: Employment-to-population ratio for adults ages 25 to 54

Employment-to-population ratio for adults ages 25 to 54
Employment-to-population ratio for adults ages 25 to 54

Source (County): US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)

Notes (County): The share of adults between the ages of 25 and 54 in a given community who are employed.

Source (City): US Census Bureau’s 2018 & 2021 1-Year American Community Survey and 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)

Notes (City): The share of adults between the ages of 25 and 54 in a given community who are employed.

Jobs paying living wages: Ratio of pay on average job to the cost of living

Ratio of pay on average job to the cost of living

Source (County): US Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW) data, 2018 & 2021; Massachusetts Institute of Technology Living Wage Calculator, 2018 & 2022. (Time period: 2018 & 2021)

Notes (County): What an average job pays relative to the cost of living in a particular area. The metric is computed by dividing the average earnings for a job in an area by the cost of meeting a family of three’s (for a 1 adult and 2 child household) basic expenses in that area. Ratio values greater than 1 indicate that the average job pays more than the cost of living, while values less than 1 suggest the average job pays less than the cost of living.For the 2021 metric, we were only able to access the 2022 Living Wage data. We deflated the 2022 data to 2021 using the consumer price index (for all urban consumers), for a correct comparison with the 2021 QCEW.

Income: Household income at 20th, 50th, and 80th percentiles

20th percentile
Household Income at the 20th Percentile
50th percentile
Household Income at the 50th Percentile
80th percentile
Household Income at the 80th Percentile
20th percentile
Household Income at the 20th Percentile
50th percentile
Household Income at the 50th Percentile
80th percentile
Household Income at the 80th Percentile
20th percentile
50th percentile
80th percentile

Do you want to see related data visualized spatially? You can see maps showing Fresno tract-level income at the 20th, 40th, 60th, and 80th percentiles on the Map page.

Source (County): US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Periods: 2014-18 & 2017-21)

Notes (County): To identify income percentiles, all households are ranked by income from lowest to highest. The income level threshold for the poorest 20 percent of households is the value at the 20th percentile. The 50th percentile income threshold indicates the median, with half of households earning less and half of households earning more. The income level threshold for the richest 20 percent of households is the value at the 80th percentile. The difference in income between households at the 20th percentile and the 80th percentile illustrates the level of local economic inequality.

Source (City): US Census Bureau’s 2018 & 2021 1-Year American Community Survey and 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Periods: 2014-18 & 2017-21)

Notes (City): To identify income percentiles, all households are ranked by income from lowest to highest. The income level threshold for the poorest 20 percent of households is the value at the 20th percentile. The 50th percentile income threshold indicates the median, with half of households earning less and half of households earning more. The income level threshold for the richest 20 percent of households is the value at the 80th percentile. The difference in income between households at the 20th percentile and the 80th percentile illustrates the level of local economic inequality.

Additional Interpretation: For example, household income is the lowest for Black, Non-Hispanic households in New Orleans, LA and Detroit, MI and for Hispanic households in Buffalo, NY (<$20,000, found in the 20th percentile graph). Household income is the highest for White, Non-Hispanic households in San Francisco, CA (>$300,000, found in the 80th percentile graph). The largest racial disparities for this metric among the cities and counties shown in the dashboard exist between White, Non-Hispanic and Black, Non-Hispanic households for the 20th percentile (>$40,000 difference) and 50th percentile graphs (>$110,000 difference). Fresno’s (city and county) average income across all races and ethnicities is below an estimate of the average income across the state of California.

Financial Security: Share of households with debt in collections

Share of households with debt in collections
Share of households with debt in collections

Source (County): August 2018 credit bureau data from Urban Institute’s Debt in America feature. (Time periods: August 2018)

Notes (County): The county-level measure captures the share of adults in an area with a credit bureau record with debt sent to collections. For county-level August 2018 data, “majority” means that at least 60% of residents in a zip code are members of the specified population group.

Source (City): August 2021 credit bureau data, from Urban Institute’s Financial Health and Wealth Dashboard. (Time period: August 2021)

Notes (City): The city-level measure captures the share of adults in an area with a credit bureau record with any derogatory debt, which is primarily debt in collections. For city-level August 2021 data, ‘majority’ means that at least 50% of residents in a zip code are members of the specified population group.

Wealth-building opportunities: Percentage point difference in the share of a community’s home values held by a racial or ethnic group to the share of households of the same group

Difference in share of community’s home values and share of population by racial and ethnic group
Difference in share of community’s home values and share of population by racial and ethnic group

Do you want to see related data visualized spatially? You can see a map of Fresno showing the share of households of a given race or ethnic group who own their homes on the Map page.

Source (County): US Census Bureau’s 2018 & 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time periods: 2018 & 2021)

Notes (County): The estimate shown in the chart reflects the share of primary-residence housing wealth for a given race and ethnic group subtracted by the share of households who are headed by a member of that race and ethnic group. If the difference is greater than 0, then that group controls a higher proportion of the total value of homes relative to their presence in the county. This metric is based on self-reported housing value, does not account for the extent of mortgage debt, and does not account for other important demographic variations such as differences in age composition across race and ethnic groups, and as such this metric may not fully reflect the size of the actual housing wealth gap. Even so, this metric is useful in determining whether there are racial disparities in terms of a community’s home values and how drastic those disparities are.

Source (City): US Census Bureau’s 2018 & 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time periods: 2018 & 2021)

Notes (City): The estimate shown in the chart reflects the share of primary-residence housing wealth for a given race and ethnic group subtracted by the share of households who are headed by a member of that race and ethnic group. If the difference is greater than 0, then that group controls a higher proportion of the total value of homes relative to their presence in the city. This metric is based on self-reported housing value, does not account for the extent of mortgage debt, and does not account for other important demographic variations such as differences in age composition across race and ethnic groups, and as such this metric may not fully reflect the size of the actual housing wealth gap. Even so, this metric is useful in determining whether there are racial disparities in terms of a community’s home values and how drastic those disparities are.

Please note that the city-level data quality is considered “Marginal.” For more details, see the discussion of data quality on the Use the Data page.

Additional Interpretation: For example, if there were no racial disparities, all racial dots (green, blue, yellow, and pink) would fall on the 0% line. This would mean that each racial or ethnic group’s share of their community’s home values would equal the share of the community’s households headed by that racial or ethnic group. In reality, in Fresno County, CA, Hispanic households’ share of housing wealth is 14 percentage points less than their ethnic representation among households in the community.

Economic Inclusion: Share of people experiencing poverty who live in high-poverty neighborhoods

Share of people experiencing poverty who live in high-poverty neighborhoods
Share of people experiencing poverty who live in high-poverty neighborhoods

Do you want to see related data visualized spatially? You can see a map showing poverty levels by census tract and race and ethnicity in Fresno on the Map page.

Source (County): US Census Bureau’s 2018 & 2021 5-Year American Community Survey. (Time periods: 2014-18 & 2017-21)

Notes (County): The share of a city’s or county’s residents living in poverty who also live in high-poverty neighborhoods (defined as census tracts). A high-poverty neighborhood is one in which over 40 percent of the residents live in poverty. People and families are classified as being in poverty if their income (before taxes and excluding capital gains or noncash benefits) is less than their poverty threshold, as defined by the US Census Bureau. Poverty thresholds vary by the size of the family and age of its members and are updated for inflation, but do not vary geographically. ’Black’ includes Black Hispanics. ‘Other Races and Ethnicities’ includes those of races not explicitly listed and those of multiple races. Those who identify as other race or multiple races and Hispanic are counted in both the ‘Hispanic’ and ‘Other Races and Ethnicities’ categories.

Source (City): US Census Bureau’s 2021 5-Year American Community Survey; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-2021)

Notes (City): The share of a city’s or county’s residents living in poverty who also live in high-poverty neighborhoods (defined as census tracts). A high-poverty neighborhood is one in which over 40 percent of the residents live in poverty. People and families are classified as being in poverty if their income (before taxes and excluding capital gains or noncash benefits) is less than their poverty threshold, as defined by the US Census Bureau. Poverty thresholds vary by the size of the family and age of its members and are updated for inflation, but do not vary geographically.

‘Black’ includes Black Hispanics. ‘Other Races and Ethnicities’ includes those of races not explicitly listed and those of multiple races. Those who identify as other race or multiple races and Hispanic are counted in both the ‘Hispanic’ and ‘Other Races and Ethnicities’ categories.

Digital Access: Share of households with a computer and broadband internet subscription in the home

Share of households with a computer and broadband internet subscription in the home

Do you want to see related data visualized spatially? You can see maps showing Fresno tract-level estimates of households’ access to a computer and broadband by race and ethnicity on the Maps page.

Source (County): US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021)

Notes (County): The chart shows for each racial or ethnic group the percentage of households of that group that have access to broadband internet (including cable, fiber optic, DSL, or cellular data) and a computer (including desktop, laptop, and cell phone).

Human Development:

Access to preschool: Share of children enrolled in nursery school or preschool

Percentage Of Children Ages 3 and 4 in Preschool
Percentage Of Children Ages 3 and 4 in Preschool

Source (County): US Census Bureau’s 2018 & 2021 5-Year American Community Survey (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)

Notes (County): The share of a community’s children aged three to four who are enrolled in nursery or preschool.

Please note that the data quality for Black, Non-Hispanic children is considered “Weak.” For more details, see the discussion of data quality on the Use the Data page.

Source (City): US Census Bureau’s 2018 & 2021 1-Year American Community Survey and 5-Year American Community Survey (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)

Notes (City): The share of a community’s children aged three to four who are enrolled in nursery or preschool.

Educational achievement: Mathematics

Percentage Of Students Passing CAASPP Math Assessment
Percentage Of Students Who Passed CAASPP Math Assessment
Percentage Of Students Who Passed CAASPP Math Assessment

Source: California Assessment of Student Performance and Progress (CAASPP) Smarter Balanced Assessments for students grades 3 - 11 (all available). The charts show the percentage of students who met or exceeded standards Data collected from years 2015 - 2023 and accessed via the California Department of Education website.

Notes: This dataset was not included in original Upward Mobility dataset. It provides additional context on California students’ educational performance. No data is available in the 2019-2020 school year due to the COVID-19 pandemic. Data was collected from the “statewide” files and subsequently filtered to include only counties and the state of California as a whole. The data and documentation do not clarify whether racial groups are Hispanic. For example, the “White” group in the data is not said to be either “Non-Hispanic White” or to include Hispanic Whites.

“AIAN” stands for “American Indians and Alaska Natives.” “NHPI” stands for “Native Hawaiians and Pacific Islanders.”

Educational achievement: English language arts

Percentage Of Students Passing CAASPP English Assessment
Percentage Of Students Who Passed CAASPP English Assessment
Percentage Of Students Who Passed CAASPP English Assessment

Source: California Assessment of Student Performance and Progress (CAASPP) Smarter Balanced Assessments for students grades 3 - 11 (all available). The charts show the percentage of students who met or exceeded standards. Data collected from years 2015 - 2023 and accessed via the California Department of Education website.

Notes: This dataset was not included in original Upward Mobility dataset. It provides additional context on California students’ educational performance. No data is available in the 2019-2020 school year due to the COVID-19 pandemic. Data was collected from the “statewide” files and subsequently filtered to include only counties and the state of California as a whole. The data and documentation do not clarify whether racial groups are Hispanic. For example, the “White” group in the data is not said to be either “Non-Hispanic White” or to include Hispanic Whites.

“AIAN” stands for “American Indians and Alaska Natives.” “NHPI” stands for “Native Hawaiians and Pacific Islanders.”

School Economic Diversity: Share of students attending high-poverty schools, by student race or ethnicity

Share of students attending high-poverty schools
Share of students attending high-poverty schools

Source (County): National Center for Education Statistics Common Core of Data, SY 2017-18 & 2018-19; Urban Institute’s Modeled Estimates of Poverty in Schools (via Education Data Portal v. 0.17.0, Urban Institute, under ODC Attribution License). (Time periods: School Years 2017-18 & 2018-19)

Notes (County): This set of metrics is constructed separately for each racial/ethnic group and reports the share of students attending schools in which over 20 percent of students come from households earning at or below 100% of the Federal Poverty Level.

Source (City): National Center for Education Statistics Common Core of Data, SY 2017-18 & 2018-19; Urban Institute’s Modeled Estimates of Poverty in Schools (via Education Data Portal v. 0.17.0, Urban Institute, under ODC Attribution License). (Time periods: School Years 2017-18 & 2018-19)

Notes (City): This set of metrics is constructed separately for each racial/ethnic group and reports the share of students attending schools in which over 20 percent of students come from households earning at or below 100% of the Federal Poverty Level.

Preparation for College: Share of 19- and 20-year-olds with a high school degree

Share of 19- and 20-year-olds with a high school degree
Share of 19- and 20-year-olds with a high school degree

Source (County): US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)

Notes (County): The share of 19- and 20-year-olds in a community who have a high school degree.

Please note that the data quality for Black, Non-Hispanic students is considered “Weak.” For more details, see the discussion of data quality on the Use the Data page.

Source (City): US Census Bureau’s 2018 & 2021 1-Year American Community Survey and 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2014-18 & 2017-21)

Notes (City): The share of 19- and 20-year-olds in a community who have a high school degree.

Access to Health Services: Ratio of population per primary care physician

Number of Residents Per Primary Care Physician

Source (County): US Department of Health and Human Services, Health Resources and Services Administration, Area Health Resources File, 2020-21 (via County Health Rankings, 2022). (Time period: 2019)

Notes (County): The ratio represents the number of people served by one primary care physician in a county. It assumes the population is equally distributed across physicians and does not account for actual physician patient load. Missing values are reported for counties with population greater than 2,000 and 0 primary care physicians. The metric does not include nurse practitioners, physician assistants, or other primary care providers who are not physicians.

Neonatal Health: Share of low-weight births

Share with low birth weight
Share with low birth weight

Source (County): Centers for Disease Control and Prevention National Center for Health Statistics, Division of Vital Statistics, Natality data, 2018 & 2020 (via CDC WONDER). (Time period: 2018 & 2020)

Notes (County): The share of babies born weighing less than 5 pounds 8 ounces (<2,500 grams) out of all births with available birthweight information. Race and ethnicity is based on the mother’s characteristics.

Neonatal Health: Share of pre-term births

Share of Preterm Births

Source: California Department of Public Health (2013-2022)

Notes: The California Department of Public Health (CDPH) defines preterm births as “births delivered at less than 37 completed weeks of gestation, based on the obstetric estimate of gestation.” The denominator of this rate is defined as all live births to California resident mothers/parents who give birth. This dashboard follows the CDPH’s recommendation of using 3-year aggregated data because single-year data estimates are unreliable at the county scale. We report the middle year of the 3-year data, so, the 2015 data comes from 2014-2016, for example.

“AIAN” stands for “American Indians and Alaska Natives.” “NHPI” stands for “Native Hawaiians and Pacific Islanders.”

Suggested Citation: California Department of Public Health, Center for Family Health, Maternal, Child and Adolescent Health Division, Preterm Birth Dashboard, Last Modified February 2024. go.cdph.ca.gov/Preterm-Birth-Dashboard

Political participation: Share of voting-age population who turn out to vote

Election Turnout
Election Turnout

Source (County): Massachusetts Institute of Technology Election Data and Science Lab, 2016 & 2020; US Census Bureau’s 2016 & 2020 5-Year American Community Survey Citizen Voting Age Population Special Tabulation. (Time periods: 2012-16 & 2016-20)

Notes (County): This metric measures the share of the citizen voting-age population that voted in the most recent presidential election.

Source (City): Voting and Election Science Team, Precinct-Level Election Results 2020 (via Harvard Dataverse); US Census Bureau’s 2020 5-Year American Community Survey Citizen Voting Age Population Special Tabulation; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2016-20)

Notes (City): This metric measures the share of the citizen voting-age population that voted in the most recent presidential election.

Safety from Trauma: Number of deaths caused by injury per 100,000 people

Deaths due to injury per 100,000 people

Source (County): National Center for Health Statistics, 2016-20, drawn from the National Vital Statistics System (via County Health Rankings, 2022). (Time period: 2016-20)

Notes (County): Injury deaths is the number of deaths from planned (e.g., homicide or suicide) and unplanned (e.g., motor vehicle deaths) injuries per 100,000 people. Deaths are counted in the county of residence for the person who died, rather than the county where the death occurred. A missing value is reported for counties with fewer than 10 injury deaths in the time frame.

Social Capital: Number of membership associations per 10,000 people

Number of membership associations per 10,000 people
Number of membership associations per 10,000 people

Source (County): US Census Bureau’s County Business Patterns Survey, 2020 and Population Estimation Program, 2016-20; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2016-20)

Notes (County): This metric measures the number of membership associations (as self-reported by businesses and organizations) per 10,000 people in a given community.

Source (City): US Census Bureau’s County Business Patterns Survey, 2020 and Population Estimation Program, 2016-20; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2016-20)

Notes (City): This metric measures the number of membership associations (as self-reported by businesses and organizations) per 10,000 people in a given community.

Social Capital: Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status

Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status
Ratio of Facebook friends with higher socioeconomic status to Facebook friends with lower socioeconomic status

Source (County): Opportunity Insights’ Social Capital Atlas, 2022. (Time period: 2022)

Notes (County): This measures the interconnectivity, by location, between people from different economic backgrounds to estimate “economic connectedness.” Specifically, the metric is twice the average share of high-socioeconomic status (SES) friends (e.g., individuals from households ranked in the top half of all income-earning households) among low-SES individuals (e.g., individuals from households ranked in the lower half of all US households based on income) in a given community. A metric value of 1 represents a community that is perfectly integrated across socioeconomic status, with half of all low-SES individuals’ friends being of high-SES.

Source (City): Opportunity Insights’ Social Capital Atlas, 2022. (Time period: 2022)

Notes (City): This measures the interconnectivity, by location, between people from different economic backgrounds to estimate ‘economic connectedness.’ Specifically, the metric is twice the average share of high-socioeconomic status (SES) friends (e.g., individuals from households ranked in the top half of all income-earning households) among low-SES individuals (e.g., individuals from households ranked in the lower half of all US households based on income) in a given community. A metric value of 1 represents a community that is perfectly integrated across socioeconomic status, with half of all low-SES individuals’ friends being of high-SES.

Additional Interpretation: Only one city from our tool (Oakland) is perfectly integrated across socioeconomic status, with half of all low socioeconomic status individuals’ friends being of high socioeconomic status.

Please note that the city-level data quality is considered “Marginal.” For more details, see the discussion of data quality on the Use the Data page.

Neighborhood Development:

Housing Affordability: Ratio of affordable to available housing units for households with 30%, 50%, and 80% of Area Median Income (AMI)

Ratio of affordable housing units to households at 30%, 50%, and 80% AMI
Ratio of affordable housing units to households at 30%, 50%, and 80% AMI
Ratio of affordable housing units to households at 30%, 50%, and 80% AMI

Source (County): US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2018 & FY 2021; US Census Bureau’s 2018 & 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time periods: 2018 & 2021)

Notes (County): This metric reports the number of housing units affordable for households with low-incomes (below 80 percent of area median income, or AMI), very low-incomes (below 50 percent of AMI), and extremely low-incomes (below 30 percent of AMI) relative to every household with these income levels. Income groups are defined for a local family of 4. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household’s income. Values above 1.0 suggest that there are more affordable housing units than households with those income levels. Affordability addresses whether sufficient housing units would exist if allocated solely on the basis of cost, regardless of whether they are currently occupied by a household that could afford the unit. Ratios below 1.0 suggest that on this basis the affordable stock is insufficient to meet the need. The affordable housing stock includes both vacant and occupied units.

Source (City): US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2018 & FY 2021; US Census Bureau’s 2018 & 2021 5-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time periods: 2014-18 & 2017-21)

Notes (City): This metric reports the number of housing units affordable for households with low-incomes (below 80 percent of area median income, or AMI), very low-incomes (below 50 percent of AMI), and extremely low-incomes (below 30 percent of AMI) relative to every household with these income levels. Income groups are defined for a local family of 4. Housing units are defined as affordable if the monthly costs do not exceed 30 percent of a household’s income. Affordability addresses whether sufficient housing units would exist if allocated solely on the basis of cost, regardless of whether they are currently occupied by a household that could afford the unit. Values below 1.0 suggest that on this basis the affordable stock is insufficient to meet the need. The affordable housing stock includes both vacant and occupied units.

Additional Interpretation: For example, there are fewer affordable housing units in Fresno (city) per 30% AMI (extremely low-income) household and 50% AMI (very low-income) households. In Fresno (county) there is less than one affordable housing unit per 30% AMI household.

Housing Stability: Share of public-school children who are ever homeless during the school year

Share of Students who are Ever Homeless
Share of Students who are Ever Homeless
Share of Students who are Ever Homeless
Share of Students who are Ever Homeless

Source (County): US Department of Education Local Education Agency data, SY 2018-19 & SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time periods: School Years 2018-19 & 2019-20)

Notes (County): The number of homeless students is based on the number of children (age 3 through 12th grade) who are enrolled in public schools and whose primary nighttime residence at any time during a school year was a shelter, transitional housing, or awaiting foster care placement; unsheltered (e.g., a car, park, campground, temporary trailer, or abandoned building); a hotel or motel because of the lack of alternative adequate accommodations; or in housing of other people because of loss of housing, economic hardship, or a similar reason. The share is the percent of public-school students who are experiencing homelessness out of all public-school students. Data disaggregated by race/ethnicity became available for the first time in SY 2019-20.

Source (City): US Department of Education Local Education Agency data, SY 2018-19 & SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time periods: School Years 2018-19 & 2019-20)

Notes (City): The number of homeless students is based on the number of children (age 3 through 12th grade) who are enrolled in public schools and whose primary nighttime residence at any time during a school year was a shelter, transitional housing, or awaiting foster care placement; unsheltered (e.g., a car, park, campground, temporary trailer, or abandoned building); a hotel or motel because of the lack of alternative adequate accommodations; or in housing of other people because of loss of housing, economic hardship, or a similar reason. The share is the percent of public-school students who are experiencing homelessness out of all public-school students. Data disaggregated by race/ethnicity became available for the first time in SY 2019-20.

Racial Diversity: Neighborhood exposure index, or the share of a person’s neighbors who are people of other races and ethnicities

Index of people’s exposure to neighbors of different races and ethnicities
Index of people’s exposure to neighbors of different races and ethnicities
Index of people’s exposure to neighbors of different races and ethnicities

Do you want to see related data visualized spatially? You can see a map showing Fresno tract-level estimates of residents’ exposure to neighbors of different races and ethnicities on the Map page.

Source (County): US Census Bureau’s 2018 & 2021 5-Year American Community Survey. (Time periods: 2014-18 & 2017-21)

Notes (County): A set of metrics constructed separately for each racial/ethnic group and reports the average share of that group’s neighbors who are members of other racial/ethnic groups. This is a type of exposure index. For example, an exposure index of 90.0% in the ‘% for Black, Non-Hispanic’ row means that the average Black, non-Hispanic resident has 90.0% of their neighbors within a census tract who have a different race/ethnicity than them. The higher the value, the more exposed to people of different races/ethnicities.

Source (City): US Census Bureau’s 2021 5-Year American Community Survey; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21)

Notes (City): A set of metrics constructed separately for each racial/ethnic group and reports the average share of that group’s neighbors who are members of other racial/ethnic groups. This is a type of exposure index. For example, an exposure index of 90.0% in the ‘% for Black, Non-Hispanic’ row means that the average Black, non-Hispanic resident has 90.0% of their neighbors within a census tract who have a different race/ethnicity than them. The higher the value, the more exposed to people of different races/ethnicities.

Environmental Quality: Air quality index

Air Quality Index
Air Quality Index

Source (County): Environmental Protection Agency’s National Air Toxics Assessment data, 2014 and AirToxScreen data, 2018 (based on 2014 & 2017 National Emissions Inventory data); US Census Bureau’s 2014 & 2018 5-Year American Community Survey. (Time periods: 2010-14 & 2014-18)

Notes (County): The index is a linear combination of standardized EPA estimates of air quality carcinogenic, respiratory, and neurological hazards measured at the census tract level. Values are inverted and percentile ranked nationally and range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health. ‘Majority’ means that at least 60% of residents in a census tract are members of the specified group. ‘High poverty’ means that 40% or more of people in a census tract live in families with incomes below the federal poverty line.

Source (City): Environmental Protection Agency’s National Air Toxics Assessment data, 2014 and AirToxScreen data, 2018 (based on 2014 & 2017 National Emissions Inventory data); US Census Bureau’s 2014 & 2018 5-Year American Community Survey; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time periods: 2010-14 & 2014-18)

Notes (City): The index is a linear combination of standardized EPA estimates of air quality carcinogenic, respiratory, and neurological hazards measured at the census tract level. Values are inverted and percentile ranked nationally and range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health.

‘Majority’ means that at least 60% of residents in a census tract are members of the specified group. ‘High poverty’ means that 40% or more of people in a census tract live in families with incomes below the federal poverty line.

Water Quality: Drinking water contaminants

Drinking Water Contaminants Index

Source (County): CalEnviroScreen V4 (2021) which include 5-year American Community Survey Data from 2015-2019 and water contaminant data from 2011 - 2019.

Notes (County): The data shown comes from the tract-level percentile rank of CalEnviroScreen’s drinking water contaminants index. The higher the value, the higher the level of water contamination relative to California regions.

While this data was released in 2021, water contaminant data incorporated into the creation of this metric were collected from 2011 to 2019. To calculate the county-level metric, we generated a population-weighted average of the census tracts within the county (or across all of California, for the state-level measure). We use the 2015-2019 5-year American Community Survey data incorporated into the CalEnviroScreen data for these calculations. This weighted average approach is similar to that taken to aggregate the Transit trips index.

While there are previous CalEnviroScreen versions, the most recently published version, Version 4, made methodological changes to the calculation of the drinking water hazards. Consequently, we do not make temporal comparisons with earlier versions of the data.

Just Policing: Rate of juvenile justice arrests

Juvenile Arrests per 100,000 Juveniles
Juvenile Arrests per 100,000 Juveniles

Source: California Department of Justice Arrests data from years 2014 - 2022 and 5-year American Community Survey data from 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2017-2021 and 2018-2022.

Notes: This dataset was not included in the original Upward Mobility dataset. The Upward Mobility metrics include a metric showing the number of juvenile arrests per 100,000 juveniles. However, that data was not available for the state of California. This dashboard shows that same metric using California-specific data. Where possible, we matched the year of juvenile arrest data with the “middle year” of a 5-year ACS range. For example, for 2017 juvenile arrest data, we matched that data with 2015 - 2019 5-year ACS data on the number of juveniles by county. For years 2020 - 2022, we used the 2018 - 2022 ACS data because that is the most recently available data (as the time of the release of the dashboard). To calculate the number of juvenile arrests per 100,000 juveniles, we took the count of juvenile arrests of a given group, divided by the total number of juveniles of that group, and then multiplied by 100,000.

Transportation Access: Transit trip index

Transit trips index

Source (County): 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)

Notes (County): The number of public transit trips taken annually by a three-person single-parent family with income at 50 percent of the Area Median Income for renters. Values are percentile ranked nationally, with values ranging from 0 to 100 for each census tract. To get a value for the community, we generate a population-weighted average of census tracts within the community. The higher the value, the more likely residents utilize public transit in the community. ‘Majority’ means that at least 60% of residents in a census tract are members of the specified group.

Transportation access: Transportation cost index

Transportation cost index

Source (County): 2016 Location Affordability Index data based on 2013-15 Illinois vehicle miles traveled data; Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics data, 2013 & 2014; US Census Bureau’s 2016 5-Year American Community Survey (via HUD AFFH data). (Time period: 2012-16)

Notes (County): Reflects local transportation costs as a share of renters’ incomes. It accounts for both transit and cars. This index is based on estimates of transportation costs for a family that meets the following description: a three-person, single-parent family with income at 50 percent of the median income for renters for the region (i.e., core-based statistical area). Values are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the value, the lower the cost of transportation in that neighborhood. ’Majority’ means that at least 60% of residents in a census tract are members of the specified group.