Mobility Metrics for Philadelphia City, Pennsylvania
These mobility metrics data tables are designed to help local leaders in every county and over 450 cities in the United States measure the status of and progress toward increasing upward mobility and equity in their communities.
The Urban Institute’s Upward Mobility Framework identifies five essential pillars that support mobility from poverty and a set of evidence-based predictors that are strongly correlated with the likelihood that a community can create conditions to boost the economic and social mobility of its residents while narrowing racial and ethnic inequities. These predictors were identified by an interdisciplinary group of experts and refined through testing with cross-sector partners. They cover diverse aspects of community, such as affordable housing, living-wage jobs, and political participation, and can be influenced by state and local policy.
Communities can use this suite of metrics along with the Planning Guide for Local Action as they work to develop a strategic plan for upward mobility and monitor progress over time.
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Pillar: Opportunity-Rich & Inclusive Neighborhoods
Predictor: Housing Affordability
| Metric: Ratio of affordable housing units (per 100 households) with low-, very low-, and extremely low-income levels | |
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| Source: US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2021; US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021) |
| Metric: Ratio of affordable housing units (per 100 households) with low-, very low-, and extremely low-income levels+ | |
|---|---|
| Source: US Department of Housing and Urban Development Office of Policy Development and Research Fair Market Rents and Income Limits, FY 2021; US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021) | |
| Notes: 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 100 households 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 100 suggest that on this basis the affordable stock is insufficient to meet the need. The affordable housing stock includes both vacant and occupied units. The Confidence Interval for this metric is not applicable. |
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Predictor: Housing stability
| Metric: Number and share of public-school children who are ever homeless during the school year | |
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| Source: US Department of Education Local Education Agency data, SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time period: School Year 2019-20) |
| Metric: Number and share of public-school children who are ever homeless during the school year | |
|---|---|
| Source: US Department of Education Local Education Agency data, SY 2019-20 (via EDFacts Homeless Students Enrolled). (Time period: School Year 2019-20) | |
| Notes: 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. |
| Metric: Number and share of public-school children who are ever homeless during the school year | ||
|---|---|---|
| Source: 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: 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. |
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Predictor: Economic inclusion
| Metric: Share of people experiencing poverty who live in high-poverty neighborhoods | |
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| Source: US Census Bureau’s 2021 5-Year American Community Survey; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-2021) |
| Metric: Share of people experiencing poverty who live in high-poverty neighborhoods+ | |
|---|---|
| Source: US Census Bureau’s 2021 5-Year American Community Survey; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-2021) | |
| Notes: 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. The Confidence Interval for this metric is not applicable. |
| Metric: Share of people experiencing poverty who live in high-poverty neighborhoods+ | |||
|---|---|---|---|
| Source: US Census Bureau’s 2021 5-Year American Community Survey; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-2021) | |||
| Notes: 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. The Confidence Interval for this metric is not applicable. |
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Predictor: Racial diversity
| Metric: Index of people’s exposure to neighbors of different races and ethnicities | |
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| Source: US Census Bureau’s 2021 5-Year American Community Survey; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21) |
| Metric: Index of people’s exposure to neighbors of different races and ethnicities+ | |
|---|---|
| Source: US Census Bureau’s 2021 5-Year American Community Survey; Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-21) | |
| Notes: 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. The Confidence Interval for this metric is not applicable. |
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Predictor: Transportation access
This metric is not currently available for cities. If you would like to explore this metric further, please refer to its corresponding county.
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Pillar: High-Quality Education
Predictor: Access to preschool
| Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool | |
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| Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021) |
| Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool+ | |
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| Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021) | |
| Notes: The share of a community's children aged three to four who are enrolled in nursery or preschool. The Confidence Interval for this metric is not applicable. |
| Metric: Share of (3- to 4-year-old) children enrolled in nursery school or preschool+ | |||
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| Source: 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: The share of a community's children aged three to four who are enrolled in nursery or preschool. The Confidence Interval for this metric is not applicable. |
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Predictor: Effective public education
| Metric: Average per grade change in English Language Arts achievement between third and eighth grades | |
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| Source: Stanford Education Data Archive, SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974). (Time period: School Year 2017-18) |
| Metric: Average per grade change in English Language Arts achievement between third and eighth grades | |
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| Source: Stanford Education Data Archive, SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974). (Time period: School Year 2017-18) | |
| Notes: The average per year improvement in English/language arts (reading comprehension and written expression) among public school students between the third and eighth grades. Assessments are normalized such that a typical learning growth is roughly 1 grade level per year. '1' indicates a community is learning at an average rate; below 1 is slower than average, and above 1 is faster than average. |
| Metric: Average per grade change in English Language Arts achievement between third and eighth grades | |||
|---|---|---|---|
| Source: Stanford Education Data Archive, SY 2016-17 & SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974). (Time period: School Years 2016-17 & 2017-18) | |||
| Notes: The average per year improvement in English/language arts (reading comprehension and written expression) among public school students between the third and eighth grades. Assessments are normalized such that a typical learning growth is roughly 1 grade level per year. '1' indicates a community is learning at an average rate; below 1 is slower than average, and above 1 is faster than average. Research suggests that annual improvement in English for Hispanic children will exceed those of White, Non-Hispanic children because Hispanic children, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. Research suggests that annual improvement in English for students in low-income or economically disadvantaged households will exceed those of non-economically disadvantaged households because students in less advantaged households, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. 'Low-income' means students are determined to be eligible for their schools' free and reduced-price meals under the National School Lunch Program. |
| Metric: Average per grade change in English Language Arts achievement between third and eighth grades | |||
|---|---|---|---|
| Source: Stanford Education Data Archive, SY 2016-17 & SY 2017-18 (Version 4.1; Reardon, S. F. et al. 2021; retrieved from http://purl.stanford.edu/db586ns4974). (Time period: School Years 2016-17 & 2017-18) | |||
| Notes: The average per year improvement in English/language arts (reading comprehension and written expression) among public school students between the third and eighth grades. Assessments are normalized such that a typical learning growth is roughly 1 grade level per year. '1' indicates a community is learning at an average rate; below 1 is slower than average, and above 1 is faster than average. Research suggests that annual improvement in English for Hispanic children will exceed those of White, Non-Hispanic children because Hispanic children, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. Research suggests that annual improvement in English for students in low-income or economically disadvantaged households will exceed those of non-economically disadvantaged households because students in less advantaged households, on average, start with lower levels of English language skills and can improve more quickly than children with higher baseline skills. 'Low-income' means students are determined to be eligible for their schools' free and reduced-price meals under the National School Lunch Program. |
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Predictor: School economic diversity
| Metric: Share of students attending high-poverty schools, by student race/ethnicity | |
|---|---|
| Source: National Center for Education Statistics Common Core of Data, SY 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 period: School Year 2018-19) |
| Metric: Share of students attending high-poverty schools, by student race/ethnicity+ | |
|---|---|
| Source: National Center for Education Statistics Common Core of Data, SY 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 period: School Year 2018-19) | |
| Notes: 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. The Confidence Interval for this metric is not applicable. |
| Metric: Share of students attending high-poverty schools, by student race/ethnicity+ | ||
|---|---|---|
| Source: 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: 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. The Confidence Interval for this metric is not applicable. |
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Predictor: Preparation for college
| Metric: Share of 19- and 20-year-olds with a high school degree | |
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| Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021) |
| Metric: Share of 19- and 20-year-olds with a high school degree+ | |
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| Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021) | |
| Notes: The share of 19- and 20-year-olds in a community who have a high school degree. The Confidence Interval for this metric is not applicable. |
| Metric: Share of 19- and 20-year-olds with a high school degree+ | |||
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| Source: 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: The share of 19- and 20-year-olds in a community who have a high school degree. The Confidence Interval for this metric is not applicable. |
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Predictor: Digital access
| Metric: Share of people in households with broadband access in the home | |
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| Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021) |
| Metric: Share of people in households with broadband access in the home* | |
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| Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021) | |
| Notes: This metric represents the share of people in households with access to broadband in their home. The Confidence Interval for this metric is not available at this time. |
| Metric: Share of people in households with broadband access in the home* | |||
|---|---|---|---|
| Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-2021) | |||
| Notes: This metric represents the share of people in households with access to broadband in their home. The Confidence Interval for this metric is not available at this time. |
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Pillar: Rewarding Work
Predictor: Employment Opportunities
| Metric: Employment-to-population ratio for adults ages 25 to 54 | |
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| Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (PUMS) (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021) |
| Metric: Employment-to-population ratio for adults ages 25 to 54+ | |
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| Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (PUMS) (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021) | |
| Notes: The share of adults between the ages of 25 and 54 in a given community who are employed. The Confidence Interval for this metric is not applicable. |
| Metric: Employment-to-population ratio for adults ages 25 to 54+ | |||
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| Source: 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: The share of adults between the ages of 25 and 54 in a given community who are employed. The Confidence Interval for this metric is not applicable. |
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Predictor: Access to jobs paying a living wage
This metric is not available for cities. If you would like to explore this metric further, please refer to its corresponding county.
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Predictor: Opportunities for Income
| Metric: Household income at the 20th, 50th, and 80th percentiles | |
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| Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Period: 2021) |
| Metric: Household income at the 20th, 50th, and 80th percentiles* | |
|---|---|
| Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time Period: 2021) | |
| Notes: 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. The Confidence Interval for this metric is not available at this time. |
| Metric: Household income at the 20th, 50th, and 80th percentiles* | |||
|---|---|---|---|
| Source: 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: 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. The Confidence Interval for this metric is not available at this time. |
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Predictor: Financial security
| Metric: Share with debt in collections | |
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| Source: August 2021 credit bureau data, from Urban Institute’s Financial Health and Wealth Dashboard. (Time period: August 2021) |
| Metric: Share with debt in collections+ | |
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| Source: August 2021 credit bureau data, from Urban Institute’s Financial Health and Wealth Dashboard. (Time period: August 2021) | |
| Notes: 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. The Confidence Interval for this metric is not applicable. |
| Metric: Share with debt in collections+ | |||
|---|---|---|---|
| Source: August 2021 credit bureau data, from Urban Institute’s Financial Health and Wealth Dashboard. (Time period: August 2021) | |||
| Notes: 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. The Confidence Interval for this metric is not applicable. |
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Predictor: Wealth-Building Opportunities
| Metric: Ratio of the share of a community’s housing wealth held by a racial or ethnic group to the share of households of the same group | |
|---|---|
| Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021) |
| Metric: Ratio of the share of a community’s housing wealth held by a racial or ethnic group to the share of households of the same group+ | |
|---|---|
| Source: US Census Bureau’s 2021 1-Year American Community Survey Public Use Microdata Sample (via IPUMS); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2021) | |
| Notes: The percentage to the left of the colon for a given racial group reflects their share of primary-residence housing wealth in a community, and the percentage to the right of the colon reflects the number of households who are headed by a member of that racial group as a share of the community’s total number of households. If the percentage on the left side of the colon is smaller than the percentage on the right side, then that group has less proportionate housing wealth compared to their presence in the community. The greater the gap between these percentages, the more inequality in housing wealth in the community. 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. The Confidence Interval for this metric is not applicable. |
| Metric: Ratio of the share of a community’s housing wealth held by a racial or ethnic group to the share of households of the same group+ | ||
|---|---|---|
| Source: 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: The percentage to the left of the colon for a given racial group reflects their share of primary-residence housing wealth in a community, and the percentage to the right of the colon reflects the number of households who are headed by a member of that racial group as a share of the community’s total number of households. If the percentage on the left side of the colon is smaller than the percentage on the right side, then that group has less proportionate housing wealth compared to their presence in the community. The greater the gap between these percentages, the more inequality in housing wealth in the community. 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. The Confidence Interval for this metric is not applicable. |
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Pillar: Healthy Environment and Access to Good Healthcare
Predictor: Access to health services
This metric is not available for cities. If you would like to explore this metric further, please refer to its corresponding county.
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Predictor: Neonatal health
This metric is not available for cities. If you would like to explore this metric further, please refer to its corresponding county.
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Predictor: Environmental quality
| Metric: Air quality index | |
|---|---|
| Source: US Environmental Protection Agency’s AirToxScreen data, 2018 (based on 2017 National Emissions Inventory data); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-18) |
| Metric: Air quality index+ | |
|---|---|
| Source: US Environmental Protection Agency’s AirToxScreen data, 2018 (based on 2017 National Emissions Inventory data); Missouri Census Data Center Geocorr 2022: Geographic Correspondence Engine. (Time period: 2017-18) | |
| Notes: 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. The Confidence Interval for this metric is not applicable. |
| Metric: Air quality index+ | |||
|---|---|---|---|
| Source: 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: 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. The Confidence Interval for this metric is not applicable. |
| Metric: Air quality index+ | |||
|---|---|---|---|
| Source: 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: 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. The Confidence Interval for this metric is not applicable. |
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Predictor: Safety from Trauma
This metric is not available for cities. If you would like to explore this metric further, please refer to its corresponding county.
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Pillar: Responsible and Just Governance
Predictor: Political participation
| Metric: Share of the voting-age population who turn out to vote | |
|---|---|
| Source: 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) |
| Metric: Share of the voting-age population who turn out to vote+ | |
|---|---|
| Source: 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: This metric measures the share of the citizen voting-age population that voted in the most recent presidential election. The Confidence Interval for this metric is not applicable. |
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Predictor: Descriptive Representation
| Metric: Ratio 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. Part of this metric is shown. See the notes for information on finalizing this metric.+ | |
|---|---|
| Source: US Census Bureau’s 2021 5-Year American Community Survey. (Time period: 2017-21) | |
| Notes: Shown are the share of that racial or ethnic group in your community. The community will need to calculate the missing percentages in order to complete the descriptive representation metric. See the Planning Guide (pg. 27) on how to calculate the missing percentage. Say that of your 10 elected officials, nine are White, non-Hispanic and your community’s population is half White, non-Hispanic, the metric will read as “90.0%:50.0%.” If the share of local officials is higher than the share of people in the community, then this group is over-represented. If the share of local officials is lower than the share of people in the community, then this group is under-represented. We are presenting this as a ratio of percentages because it provides important context. The quality index reflects the data quality only of the given value. The Confidence Interval for this metric is not applicable. |
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Predictor: Safety from Crime
| Metric: Reported property crimes per 100,000 people and reported violent crimes per 100,000 people | |
|---|---|
| Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021) |
| Metric: Reported property crimes per 100,000 people and reported violent crimes per 100,000 people+ | |
|---|---|
| Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021) | |
| Notes: Rates are calculated as the number of reported crimes against proprty or people per 100,000 people. Although these are the best national data source, communities should use their local data if they are available. The FBI cautions against using NIBRS data to rank or compare locales because there are many factors that cause the nature and type of crime to vary from place to place. The Confidence Interval for this metric is not applicable. |
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Predictor: Just policing
| Metric: Juvenile arrests per 100,000 juveniles | |
|---|---|
| Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021) |
| Metric: Juvenile arrests per 100,000 juveniles+ | |
|---|---|
| Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021) | |
| Notes: The number of arrests of people aged 10 to 17, for any crime or status offense, per 100,000 people of that age. Because people can be arrested multiple times, the data reports the number of arrests and not people. Although these are the best national data source, communities should use their local data if it is available. The FBI cautions against using NIBRS data to rank or compare locales because there are many factors that cause the nature and type of crime to vary from place to place. The Confidence Interval for this metric is not applicable. |
| Metric: Juvenile arrests per 100,000 juveniles+ | |||
|---|---|---|---|
| Source: Federal Bureau of Investigations (FBI) National Incident Based Reporting System (via Kaplan J (2021). National Incident-Based Reporting System (NIBRS) Data. https://nibrsbook.com/); US Census Bureau’s 2021 1-Year American Community Survey. (Time period: 2021) | |||
| Notes: The number of arrests of people aged 10 to 17, for any crime or status offense, per 100,000 people of that age. Because people can be arrested multiple times, the data reports the number of arrests and not people. Although these are the best national data source, communities should use their local data if it is available. The FBI cautions against using NIBRS data to rank or compare locales because there are many factors that cause the nature and type of crime to vary from place to place. Ethnicity is inconsistently collected and often missing in the data. Those of multiple races are only included in 'Other Races.' The Confidence Interval for this metric is not applicable. |
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Additional Notes on Data
Data Quality
“Strong” indicates that the metric is measured with adequate accuracy and sample size.
“Marginal” indicates that there are known shortcomings of the data for this metric for this community, and the metric should be used with caution.
“Weak” indicates that although the metric could be computed for this community, we have serious concerns about how accurately it is measured for this community and do not recommend its use. Instead, we recommend seeking more local data sources for this metric.
“NA” indicates that the metric value may be suppressed or unavailable and the quality is not applicable.
Confidence Intervals
Confidence intervals shown are 95 percent.
* This confidence interval is not available at this time.
+ A confidence interval is not applicable.
Lower/Upper bound: The data used to construct this metric do not lend themselves to conventional confidence intervals. The value of the metric shown represents are best estimate; the lower and upper bounds represent alternative estimates of the metric under different assumptions about missing data.
“NC” in fields for confidence intervals or lower/upper bounds means that we are not able to calculate this because the underlying data lack variation.
Missing and Suppressed Values
“NA” in fields for metric values and data quality values indicates that the data are suppressed due to sample sizes or because that element is not applicable to that community (e.g., no zip code in the city is majority non-white).
Version: 2023-06-29 23:04:17