Putting Youth on the Map supports data transparency and data democracy. On this page click the dropdown menu or scroll down to:
Please refer to the original data sources for questions about data accuracy.
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Click here for more on how we calculate the index.
The YVI assesses vulnerability to inadequate support for well-being. It combines four types of data, which we call “indicators,”— the rates at which youth became teen mothers, did not complete high school, were placed in foster care, and lived in households with very low family incomes. The YVI and related indicator data and maps are available by county for all youth, and by race/ethnicity and sex. Data for three of the four indicators are also provided for all youth across census county divisions (CCDs). These are sub-county regions that include one or more census tracts. Data for the fourth indicator was not available at this level of geography, so we were unable to calculate the YVI for CCDs.
Individual place-based indicator scores reflect comparison to a benchmark that could be considered excellent progress. For each indicator the benchmark is based on the rates experienced by 10% of all CA youth in the counties with the lowest rates. Each place receives a “score” for each indicator:
The YVI averages the four indicator scores for each county, and highlights places scoring relatively low (meaning lower vulnerability rates) and high (meaning higher vulnerability rates) across the four indicators. In counties where one or more indicators is based on an estimate that is potentially unreliable, the YVI is not calculated. Reliability concerns and thresholds are discussed in more detail below.
Many other conditions are associated with youth marginalization from potentially key institutional supports for well-being, including involvement in the juvenile justice system, teen fatherhood, identification as LGBTQ, homelessness and unauthorized immigration status. Unfortunately these data are either unavailable or unavailable in a form that can easily be mapped for the state at a sub-county level, so we are unable to include them in the index.
New Sub-Group Analysis for YVIThe YVI includes analyses by race, ethnicity and sex wherever there was an adequate population size to do so. At the county level, the YVI and its indicators are now provided for females, males, and populations identified as White, Hispanic/Latino, Black/African American, Asian, Native American/Alaskan Native, and Two or More racial/ethnic backgrounds. Due to small youth populations in many CCDs, subgroups are not analyzed separately at this geographic level. Data are provided for only three of the indicators for CCDs because foster care entry rates are not adequately geo-referenced at the sub-county level. Therefore the YVI is not calculated for CCDs.
To learn more about YVI indicator data, data sources and their limitations, click the links below.
Teen mothers are more likely to not graduate from high school and/or pursue postsecondary education, experience more rapid repeat pregnancy, face the challenges of single parenting and grapple with unemployment and poverty, all of which can result in inadequate support for well-being. Teen Birth Rate maps show the number of births per 1,000 females ages 15-19. It also compares these rates to a benchmark. This benchmark is based on the teen birth rate experienced by 10% of all CA females ages 15-19 in the counties with the lowest teen birth rates. Maps by race/ethnicity are available by county only because the very small numbers of births in many CCDs make subgroup analyses unreliable.
The maps are based on estimates that are subject to uncertainty based on small population sizes or rare events. In places where estimates do not meet a certain threshold of reliability (explained below), we display an asterisk and urge you to interpret these estimates with caution as they are less likely to be accurate.
This teen birth rate map represents the number of 15 to 19 year olds in each area who gave birth during the years 2009-2011, divided by the total number of females in the same age group over the same time period, multiplied by 1,000. Population totals are U.S. Census population estimates for intercensal years.
If a mother’s age or race/ethnic group was not specified, the record was dropped. For the county-level analysis, if the mother’s race was listed as “other,” the record was dropped. We used mother’s county of residence as specified in the birth records to calculate the number of births per county. For CCDs, we used mother’s residential address, geocoded to the census tract, to count the number of births to teen mothers in all census tracts within each CCD. Births that could not be geocoded were excluded (6,133 out of 128,756 births to 15 to 19 year olds, or 4.8%).
The potential for error in the estimated teen birth rate for each location depends primarily on the number of teen births. The number of teen births is expected to follow a Poisson distribution, which expresses the probability that a given number of events will occur within a fixed interval of time and space. Because there is some random temporal and spatial variability in births, the estimated birth rate may vary across geographic units or years, even when the underlying average rate is the same. A statistical measure called margin of error accounts for this naturally occurring variability, and expresses the degree of uncertainty that an estimate is close to the true value. When the margin of error is large relative to the estimate, the estimate is less reliable and should be interpreted with caution. We denote places where the margin of error is 35% the size of the estimate or larger with an asterisk. For a Poisson variable, the margin of error is equal to one divided by the square root of the number of events, multiplied by 1.645. When the number of teen births is 23 or higher, the relative margin of error falls below 35%. Therefore, the estimated birth rate in places with fewer than 23 teen births is considered unreliable and should be used with caution as the true value may differ.
For the purposes of estimating reliability, we assume that the population denominators have no error. “While this assumption is technically correct only for denominators based on the census that occurs every 10 years, the error in intercensal population estimates is usually small, difficult to measure, and therefore not considered.” (National Vital Statistics Reports, Vol. 51, No. 12, August 4, 2003, page 90 https://www.cdc.gov/nchs/data/nvsr/nvsr51/nvsr51_12.pdf).
It should be noted that population estimates for areas or groups with small populations are likely to have more variability, and teen birth estimates in these places should be used with considerable caution.
Where did we get the data?Teen birth data were obtained (after receiving approval from the Committee for the Protection of Human Subjects) from the California Department of Public Health's Health Information and Research Section, using the Birth Statistical Master Files for 2009-11. County-level female population data came from the U.S. Census postcensal (2009) and intercensal (2010 and 2011) population estimates. Postcensal population estimates are produced by updating the resident population enumerated in a decennial census with estimates of population change (births, deaths, migration) in the years before the next decennial census. Intercensal estimates update the postcensal estimates with information from the next decennial census. We added the population estimates for 2009, 2010, and 2011 to get an estimate of the total number of females ages 15-19 for the race/ethnic groups of interest in each county. Intercensal population estimates are not available for sub-county regions, so we used data from the U.S. Census Bureau’s American Community Survey, table B11001, 5-year estimates for 2011 to get numerator data for the CCD-level teen birth rate indicator. We multiplied the ACS estimate by three to obtain an estimate of the total number of females ages 15 to 19 in in each CCD over the years 2009 to 2011.
Youth who enter the foster care system tend to be vulnerable to inadequate support, which can lead to poor educational, mental and physical health, social developmental and economic outcomes. This map shows foster care entry rates per 1,000 youth age 0 to 17, for the years 2009-11. The map also compares this rate to a benchmark. This benchmark is based on the foster care entry rates experienced 10% of all CA 0-17 year olds in the counties with the lowest rates in 2009-11. Maps and data are available by county only.
The maps are based on estimates that are subject to uncertainty based on small population sizes or rare events. In places where estimates do not meet a certain threshold of reliability (explained below), we display an asterisk and urge you to interpret these estimates with caution as they are less likely to be accurate.
How did we calculate the foster care entry rate?Foster care entry rates are the number of 0 to 17 year olds in each county who entered foster care between 2009 and 2011, divided by the number of 0-17 year olds in 2009-11, multiplied by 1,000. Foster care data could not be reliably geo-located to sub-county regions, so foster care data are not presented for CCDs.
The potential for unreliability in the foster care entry rate was assessed based on the number of foster care entries. The number of entries is expected to follow a Poisson distribution, which expresses the probability that a given number of events will occur within a fixed interval of time and space. Because there is some random temporal and spatial variability in events, the estimated foster care entry rate may vary across geographic units or years, even when the underlying average rate is the same. A statistical measure called margin of error accounts for this naturally occurring variability, and expresses the degree of uncertainty that an estimate is close to the true value. When the margin of error is large relative to the estimate, the estimate is less reliable and should be interpreted with caution. We denote places where the margin of error is 35% the size of the estimate or larger with an asterisk. For a Poisson variable, the margin of error is equal to one divided by the square root of the number of events, multiplied by 1.645. When the number of foster care entries is 23 or higher, the relative margin of error falls below 35%. Therefore, the estimated foster care entry rate in counties with fewer than 23 foster care entries is considered unreliable and should be used with caution as the true value may differ.
Where did we get the data?Raw foster care entry data were collected from the Child Welfare Dynamic Reporting System (https://cssr.berkeley.edu/ucb_childwelfare/GeoData.aspx), a California Department of Social Services / UC Berkeley collaboration. The use agreement for these data included non-redistribution of the data. For that reason, we only allow download of the foster care entry rate rank.
Population estimates were obtained from the CA Department of Finance Demographic Unit, Report P-3: Population Projections by Race/Ethnicity, Detailed Age, and Gender, 2010-2060. (https://www.dof.ca.gov/research/demographic/reports/projections/P-3/, downloaded January 30, 2013).
Very low family income is associated with decreased access to support for well-being. This map shows the rates at which 0 to 17 year olds are living in families with incomes that are below the federal poverty level. The map also compares this rate to a benchmark. This benchmark is based on the poverty rates experienced by 10% of all CA 0-17 year olds in the counties with the lowest poverty rates in 2010. Maps are available by sex and race/ethnicity for counties and by CCD.
The maps are based on estimates that are subject to uncertainty based on small population sizes or rare events. In places where estimates do not meet a certain threshold of reliability (explained below), we display an asterisk and urge you to interpret these estimates with caution as they are less likely to be accurate.
How did we calculate very low family income rate?In this map we show the percentage of 0 to 17 year olds living in families with incomes that are below the federal poverty level. To generate analyses based on the benchmark, data were collected from the U.S. Census Bureau's American Community Survey 5-year estimates for 2011, using Table B17001 "Poverty Status in the Past 12 Months by Sex and Age.” Since the American Community Survey (ACS) is not a complete census, a margin of error (MOE) for each data point is given with the raw data. The margin of error is a statistical measure that represents the degree of uncertainty that a given estimate is close to the true value being estimated. When the margin of error is large relative to the estimate, the estimate is less likely to be close to the true value being estimated. We denote places where the margin of error is 35% the size of the estimate or larger with an asterisk. Please beware of large MOEs because estimates in these places are less likely to be accurate and should be interpreted with caution.
Click here for more information about the MOE calculation.
Poverty data come from the U.S. Census Bureau’s American Community Survey table B17001, 5-year estimates.
Youth who do not complete high school are more likely to experience a variety of challenges to their well-being and less access to support. This indicator measures the rates at which young people ages 18 to 24 have less than a 9th grade education or entered 9th to 12th grades but received no diploma. The map also compares this rate to a benchmark. This benchmark is based on the lowest non-completion (or “dropout”) rates experienced by 10% of all CA 18-24 year olds. Data by sex and race/ethnicity are available for counties and by CCD.
The maps are based on estimates that are subject to uncertainty based on small population sizes or rare events. In places where estimates do not meet a certain threshold of reliability (explained below), we display an asterisk and urge you to interpret these estimates with caution as they are less likely to be accurate.
How did we calculate the high school non-completion (dropout) rate?This indicator was created by dividing the number individuals who did not complete high school by the total population of 18-24 year olds. Data were collected from the U.S. Census Bureau's American Community Survey 5-year estimates for 2007-11, using Table B15001, "Sex by Age by Educational Attainment for the Population 18 years and Over."
Since the American Community Survey (ACS) is not a complete census, a margin of error (MOE) for each data point is given with the raw data. The margin of error is a statistical measure that represents the degree of uncertainty that a given estimate is close to the true value being estimated. When the margin of error is large relative to the estimate, the estimate is less likely to be close to the true value being estimated. We denote places where the margin of error is 35% the size of the estimate or larger with an asterisk. Please beware of large MOEs because estimates in these places are less likely to be accurate and should be interpreted with caution.
Click here for more information
Where did we get the data?To search the U.S. Census website for data, please see the American Fact Finder query tool at: https://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.
As a partner in Putting Youth on the Map, the Center for Regional Change’s California Civic Engagement Project has provided these analyses of youth (18-24 year olds) voting. Click each for detailed information about the data and analysis.
The percent of California registered voters who were youth in the 2010 general election is calculated by dividing the number of registered voters age 18-23 in that election by the total number of registered voters for each by county.
We used official registration records from each California county’s Office of the Registrar of Voters (compiled and dissaggregated by the Statewide Database). These are actual registration records and not representative samples. For more information on SWDB data collection methods, click here (https://statewidedatabase.org/data.html. Note: The Statewide Database's 2002-2012 voter data files posted on their website are currently mislabeled by age. SWDB's voter file data is actually calculated for the following age groups: ages 18-23, 24-33, 34-43, 44-53, 54-63 and 64+.
We calculated youth registered voter turnout for the 2010 general election by dividing the number of actual voters age 18-23 in that election by the number of registered voters age 18-23 for each California county and census tract.
We used official registration and voter records for all registrants from each California county’s office of the registrar of voters (compiled and disaggregated by the Statewide Database). These data are the actual voter records and not representative samples.
The percent of California voters who were youth in the 2010 general election is calculated by dividing the number of voters age 18-23 in that election by the total number of voters for each by county and census tract. We used voter data from the official voters records from each California county’s office of the registrar of voters (compiled and disaggregated by the Statewide Database). These data are the actual voter records and not representative samples.
2010 Citizen voting age population data (those citizens age 18 and older) is from the California Department of Finance. These data estimates are the only published source of current CVAP data by age at a county level. For more information on these data, click here (https://www.dof.ca.gov/research/demographic/).
Youth population data by census tract came from the 2010 decennial census form SF1. Data downloaded at https://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.
Percentage of the Population Age 15-19Youth population data by census tract came from the 2010 decennial census form SF1. Data downloaded at https://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.
Percentage of the Population Age 10-19Youth population data by census tract came from the 2010 decennial census form SF1. Data downloaded at https://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.
The percentage of 0 to 17 year olds who are of Latino/Hispanic origin as a percentage of the entire youth population within a county. We classified Latino/Hispanic origin based on U.S. Census data from the 2010 decennial census form SF1, P12H report. Raw data (from U.S census SF1, QTP1 report) downloaded at: https://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.
Percentage of White YouthThe U.S. Census uses categories-- White, Black or African American, Asian, American Indian and Alaskan Native, Hawaiian and Other Pacific Islander-- to describe the racial make-up of California's young people. This map shows by county the percentage of 0 to 17 year olds that are White. County pop-ups provide a racial breakdown. Raw data from the 2010 decennial census form SF1 downloaded at https://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.
Percentage of Black YouthThe U.S. Census uses categories-- White, Black or African American, Asian, American Indian and Alaskan Native, Hawaiian and Other Pacific Islander-- to describe the racial make-up of California's young people. This map shows by county the percentage of 0 to 17 year olds that are Black (including African Americans and other Black populations). County pop-ups provide a racial breakdown. Data came from the 2010 decennial census form SF1. Raw data from the 2010 decennial census form SF1 downloaded at: https://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.
Percentage of Asian YouthThe U.S. Census uses categories-- White, Black or African American, Asian, American Indian and Alaskan Native, Hawaiian and Other Pacific Islander-- to describe the racial make-up of California's young people. This map shows by county the percentage of 0 to 17 year olds that are Asian. County pop-ups provide a racial breakdown. Data came from the 2010 decennial census form SF1. Raw data from the 2010 decennial census form SF1 downloaded at: https://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.
Percentage of American Indian and Alaskan Native YouthThe U.S. Census uses categories-- White, Black or African American, Asian, American Indian and Alaskan Native, Hawaiian and Other Pacific Islander-- to describe the racial make-up of California's young people. This map shows by county the percentage of 0 to 17 year olds that are American Indian and Alaskan Native. County pop-ups provide a racial breakdown. Data came from the 2010 decennial census form SF1. Raw data from the 2010 decennial census form SF1 downloaded at: https://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.
Percentage of Hawaiian and Other Pacific Islander YouthThe U.S. Census uses categories-- White, Black or African American, Asian, American Indian and Alaskan Native, Hawaiian and Other Pacific Islander-- to describe the racial make-up of California's young people. This map shows by county the percentage of 0 to 17 year olds that are Hawaiian and Other Pacific Islander. County pop-ups provide a racial breakdown. Data came from the 2010 decennial census form SF1. Raw data from the 2010 decennial census form SF1 downloaded at: https://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml.
Click links below to see data source information and to download data.
This map shows the percentage of youth ages 12-17 growing up in households earning adequate income to meet basic needs. Our threshold of "adequate" household resources draws on cost of living estimates associated with a "living wage" for families with children in California, generated by the "Living Wage Calculator" (Downloaded at https://www.livingwage.geog.psu.edu/states/06 on 3/23/11, based on the latest data available as of November 2008.); across various family configurations these estimates are approximately three times the federal poverty level. We therefore map % 12-17 year olds in households with income at least 300% federal poverty line (ACS 2012-2016 5 year estimates, Table B17024).
The California Department of Public Health describes gonorrhea as "a common sexually transmitted infection caused by a bacterium called Neisseria gonorrhoeae. Infection can cause serious reproductive health problems, such as pelvic inflammatory disease (PID) and infertility. Gonorrhea also can cause infections in newborn babies. Tests and effective treatments are available. See below for calculation descriptions, data sources and data downloads.
Gonorrhea Rate Females Age 10-14Here we report 2011 gonorrhea rates for females age 10 to 14 per 100,000 population. Raw data obtained from https://www.cdph.ca.gov/data/statistics/Documents/STD-Data-LHJ-DataSummaries-All.pdf.
Gonorrhea Rate Females Age 15-19Here we report 2011 gonorrhea rates for females age 15 to 19 per 100,000 population. Raw data were obtained from https://www.cdph.ca.gov/data/statistics/Documents/STD-Data-LHJ-DataSummaries-All.pdf.
Gonorrhea Rate Males Age 10-14Here we report 2011 gonorrhea rates for males age 10 to 14 per 100,000 population. Raw data obtained from https://www.cdph.ca.gov/data/statistics/Documents/STD-Data-LHJ-DataSummaries-All.pdf.
Gonorrhea Rate Males Age 15-19Here we report 2011 gonorrhea rates for males age 15 to 19 per 100,000 population. Raw data obtained from https://www.cdph.ca.gov/data/statistics/Documents/STD-Data-LHJ-DataSummaries-All.pdf.
The California Department of Public Health describes chlamydia as "a common sexually transmitted infection caused by a bacterium called Chlamydia trachomatis. Infection is often without symptoms, and if not treated, can cause serious reproductive health problems such as pelvic inflammatory disease (PID) and infertility. Chlamydia also can cause infections in newborn babies. Tests and effective treatments are available." See below for calculation descriptions, data sources and data downloads.
Chlamydia Rate Females Age 10-14Here we report 2011 chlamydia rates for females age 10 to 14 per 100,000 population. Raw data were obtained from https://www.cdph.ca.gov/data/statistics/Documents/STD-Data-LHJ-DataSummaries-All.pdf.
The dataset notation -99 means the number of cases was suppressed for confidentiality. Chlamydia Rate Females Age 15-19Here we report 2011 chlamydia rates for females age 15 to 19 per 100,000 population. Raw data were obtained from https://www.cdph.ca.gov/data/statistics/Documents/STD-Data-LHJ-DataSummaries-All.pdf.
The dataset notation -99 means the number of cases was suppressed for confidentiality. Chlamydia Rate Males Age 10-14Here we report 2011 chlamydia rates for males age 10 to 14 per 100,000 population. Raw data were obtained from https://www.cdph.ca.gov/data/statistics/Documents/STD-Data-LHJ-DataSummaries-All.pdf.
The dataset notation -99 means the number of cases was suppressed for confidentiality. Chlamydia Rate Males Age 15-19Here we report 2011 chlamydia rates for males age 15 to 19 per 100,000 population. Raw data were obtained from https://www.cdph.ca.gov/data/statistics/Documents/STD-Data-LHJ-DataSummaries-All.pdf.
The dataset notation -99 means the number of cases was suppressed for confidentiality.Young people who are out of work and out of school often lack access to support for well-being. This map shows the “out of school/out of work” rates of 20-24 year olds for geographical areas that contain a census population of at least 100,000 people.
ACS 2006-2010 5-year estimates of 20-24 year olds who were not in school, not employed and not in the labor force were downloaded from the University of Minnesota IPUMS Program* (Integrated Public Use Microdata Series, https://usa.ipums.org/usa/index.shtml) at the PUMA level. A PUMA is a statistical geographical area that contains a census population of at least 100,000. In this analysis we used their EMPSTAT variable (which included the above selection criteria) weighted using the PERWT variable (a U.S. population weight used when doing person-level analysis).
*Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota, 2010.
Truancy rates in California schools for 2012-2013. CDE calculates truancy rates as the number of students with unexcused absence or tardiness on 3 or more days divided by the total enrollment (see https://www.cde.ca.gov/ls/ai/tr/ for more information about truancy). This map does NOT include truancy rates for school programs for incarcerated youth and home schools. All other school sites were geocoded using school addresses available through CDE when data were downloaded on 5/20/14.
The Healthy Food Financing Initiative (HFFI) Working Group has defined a food desert as, “a low-income census tract where a substantial number or share of residents has low access to a supermarket or large grocery store (see https:// www.acf.hhs.gov/programs/ocs/resource/healthy-food-financing-initiative-0). Our food access maps show the percentage and number of children ages 0 to 17 who have low access to grocery stores. Low access is defined as living more than 1/2 mile from a supermarket or large grocery store in urban tracts, and more than 10 miles from a supermarket or large grocery store in rural tracts. Low-income tracts are marked with a triangle.
Food access data were downloaded from https://www.ers.usda.gov/data-products/food-access-research-atlas/download-the-data.aspx in October 2018. For background on the food desert analysis click here. A complete data dictionary (the source of background provided) and more information on other food access analyses can be found at: https://www.ers.usda.gov/data-products/food-access-research-atlas/documentation.aspx
Distance to Transit Stops This map shows the distance in meters from the population-weighted centroid of census block groups to the nearest transit stop. Data (Variable D4a) were provided by The Smart Location Database, a free data product and service provided by the U.S. EPA Smart Growth Program.(see http://www.epa.gov/smartgrowth/smartlocationdatabase.htm for original data and detailed metadata). They were not verified by UC Davis.
The U.S. EPA collected transit stop information from the General Transit Feed Specifications (https://developers.google.com/transit/gtfs/ ) in December 2012 and January 2013, and from the Transit Oriented Development Database (http://toddata.cnt.org/) , where data were updated as of 2011. EPA obtained Census block group boundaries from 2010 Census TIGER/Line shapefiles and combined them into a single national ArcGIS feature class. EPA also obtained 2010 block group “centers of population”7 from the Census. These centroids were used in geoprocessing the distance to transit stops.
Percentage of Households with 0, 1 and 2 or more cars Vehicle ownership analyses were generated by the U.S. EPA and made available through the Smart Location Database. Auto ownership fields were derived from the ACS 2006-2010 table B25044. Percent auto ownership fields were calculated as the share of all households having zero cars (Pct_AO0), one car (Pct_AO1), or two or more cars (Pct_AO2p) with respect to total households reported in the ACS table (adapted from Ramsey, K.R. & Bell, A. (2014). Smart Location Database Version 2.0 User Guide (March 14, 2014). Washington D.C.: U.S. EPA.). EPA obtained CBG boundaries from 2010 Census TIGER/Line shapefiles and combined them into a single national ArcGIS feature class. TIGER2010_bg10 is the basic geographic dataset to which all SLD variables are appended. U.C. Davis extracted analyses from the U.S. EPA Smart Location Database in July 2014).
Transit Service Frequency EPA analyzed GTFS data to calculate the frequency of service for each transit route between 4:00 and 7:00 PM on a weekday. Then, for each block group, EPA identified transit routes with service that stops within 0.4 km (0.25 miles). Finally EPA summed total aggregate service frequency by block group. Values for this metric are expressed as service frequency per hour of service. All block groups in areas where GTFS service data are unavailable are given the value -99999. (Adapted from Ramsey, K.R. & Bell, A. (2014). Smart Location Database Version 2.0 User Guide (March 14, 2014). Washington D.C.: U.S. EPA, p.25. For additional information on regions for which data are available, see Appendix A.)
The boundary files were downloaded from Cal-Atlas Geospatial Clearinghouse (https://atlas.ca.gov/).
Old Congressional Districts 2001These congressional district boundaries were enacted on September 13, 2001, and signed into law on September 26, 2001 by the Governor of California. The boundary files were downloaded from Cal-Atlas Geospatial Clearinghouse (https://atlas.ca.gov/). Please note: redistricting was performed in 2011. See the New Congressional Districts 2011 map layer for changes. We do not provide a download of this boundary file.
Old State Senate Districts 2001These state senate district boundaries were enacted on September 13, 2001, and signed into law on September 26, 2001 by the Governor of California. The boundary files were downloaded from Cal-Atlas Geospatial Clearinghouse (https://atlas.ca.gov/). Please note: redistricting was performed in 2011. See the New State Senate Districts 2011 map layer for changes. We do not provide a download of this boundary file.
Old State Assembly Districts 2001These state assembly district boundaries were enacted on September 13, 2001, and signed into law on September 26, 2001 by the Governor of California. The boundary files were downloaded from Cal-Atlas Geospatial Clearinghouse (https://atlas.ca.gov/). Please note: redistricting was performed in 2011. See the New State Assembly Districts 2011 map layer for changes. We do not provide a download of this boundary file.
New Congressional Districts 2011These congressional district boundaries were developed by the California Citizens Redistricting Commission (created in 2008 in response to the passage of California Proposition 11), following the passage of California Proposition 20 in 2010. They were downloaded from https://wedrawthelines.ca.gov/. Please note: We do not provide a download of this boundary file.
New State Senate Districts 2011These state senate district boundaries were developed by the California Citizens Redistricting Commission (created in 2008 in response to the passage of California Proposition 11), following the passage of California Proposition 20 in 2010. They were downloaded from https://wedrawthelines.ca.gov/. Please note: We do not provide a download of this boundary file.
New State Assembly Districts 2011These state assembly district boundaries were developed by the California Citizens Redistricting Commission (created in 2008 in response to the passage of California Proposition 11), following the passage of California Proposition 20 in 2010. They were downloaded from https://wedrawthelines.ca.gov/. Please note: We do not provide a download of this boundary file.
The Rural-Urban Commuting Area Codes (RUCAs) were developed using census definitions of rural or urban status combined with work commuting information.
This map uses US Census categories to depict census tracts as part of Urbanized Areas (at least 50,000 people), Urban Clusters (2500-49,999 people), and Rural (0-2499 people).
An urban area includes a densely settled core of census tracts and/or census blocks that meet minimum population density requirements, along with adjacent territory containing non-residential urban land uses and territory with low population density included to link outlying densely settled territory with the densely settled core. To qualify as an urban area, the territory must encompass at least 2,500 people, at least 1,500 of which reside outside institutional group quarters (e.g. prisons). The U.S. Census Bureau identifies two types of urban areas:
“Rural” encompasses all population, housing, and territory not included within an urban area. For more information see https://www.census.gov/geo/reference/ua/urban-rural-2010.html. Data downloaded May 2014.
Download the data by clicking on the links below. Data and data dictionaries are provided in Excel Workbooks. If you are interested in spatial data, please see the KML Files section below.
KML (Keyhole Markup Language) is file format used to display geographic data in web-based online maps or an Earth browser such as Google Earth, Google Maps, and Google Maps for mobile. We currently provide KML files for the Youth Well-Being Index and will add more files in the future or as needed. If you have interest in obtaining a KML file not listed below, or have any questions regarding KML use, please Contact the CRC .
| Dataset Year | Name | Description | Size | Download |
|---|---|---|---|---|
| 2013 | Youth Well-Being Index: All data/General population | This KML provides data for all 12 indices and indicators of the Youth Well-Being Index for the "All" population category only. | 52 MB | PYOM_ywi_AllDataGenPop2013.zip |
| 2013 | Youth Well-Being Index: Index/Race and gender breakdown | This KML provides data for the Youth Well-Being Index only, for all race and gender categories. | 48 MB | PYOM_ywi_Index2013.zip |
The Youth Well-Being Index (YWI) and Youth Vulnerability Index (YVI) maps show index scores and indicator rates by sex and race/ethnicity for specific places. This page provides statewide analyses of the YWI and YVI. For the YWI we provide analyses by sex, race/ethnicity and community type (e.g. rural, suburban, urban). For the YVI we provide analyses by sex and race/ethnicity. These analyses use the same data and calculations as the maps; for more information about them please see the Metadata page.
The YWI presents a holistic measure of youth well-being based on an optimal score of 100%. The YWI includes measures for four “domains:” education, health, social relationships and community involvement.
Available data allow us to calculate the YWI using the categories “male” and “female.” The chart below shows that females score slightly higher than males on the YWI overall. While females and males have the same score for the “Community Context” dimension, which looks at local household incomes and civic participation, females have higher scores in Health, Social Relationships, and especially Education. Males and females both have the highest scores in Social Relationships and the lowest in Education.
Calculating the YWI by race/ethnicity is challenging because different data sources that make up the index use varying racial/ethnic categories. We did the best we could to specify groups, although are still unable to differentiate between many meaningful racial/ethnic categories (for example, breaking out all national/ethnic groups within the “Asian” and “Black” categories, breaking out racial and ethnic groups in the “Hispanic” category, knowing who makes up the group that identified with “two or more” racial/ethnic groups).
The chart below shows statewide YWI scores for each racial/ethnic category. The YWI score for all of California is shown at the far right.
The Youth Well-Being Index includes 4 elements or “domains” of well-being: health, education, social relationships, and community contexts (see more on this on the “About Data” page). The charts below show the scores in each of these areas by race/ethnicity.
Health (including measures of physical fitness, substance use avoidance and feeling safe)
Education (including measures of high school completion and college readiness)
Social Relationships (including measures of having positive relationships with school peers and adults in and out of school)
Community Involvement (including measures of youth civic and extracurricular activity participation)
For this analysis we use district/community-type categories that are based on U.S. Census data and are assigned to districts by the National Center for Education Statistics (NCES). See the categories, their definitions, and the number of districts in each category as of 2012 here.
This chart shows the mean YWI scores for each community/district type. The mean score for all districts, 63% out of a potential 100%, is on the right. This comparison shows that youth well-being scores are somewhat higher than the state mean in small cities and large suburbs, while they are somewhat lower in distant towns.
The Youth Well-Being Index includes 4 elements or “domains” of well-being: health, education, social relationships, and community involvement (see more on this on the “Data” page). The charts below show the mean scores in each of these domains by community/district type.
Health (including measures of physical fitness, substance use avoidance and feeling safe)
Statewide the mean Health score is 64% out of a possible 100%. Remote rural localities (58%), distant rural areas (61%), and remote towns (61%) stand out as having somewhat lower scores. In contrast, small cities (66%), large suburbs (66%), mid-size cities (65%) and mid-size suburbs (65%) have higher scores.
Education (including measures of high school completion and college readiness)
Statewide the mean Education score is 58% out of a possible 100%. Distant towns (55%) and remote rural areas (56%) have somewhat lower mean scores, while small cities (64%) and large suburbs (63%) have higher mean scores.
Social Relationships (including measures of having positive relationships with school peers and adults in and out of school)
Statewide the mean Social Relationships score is 68% out of a possible 100%. Large cities (66%) and mid-size cities (67%) and distant towns (67%) have somewhat lower scores, while remote towns (70%), and distant (69%) and remote rural (69%) communities have the highest scores.
Community Involvement (including measures of youth civic and activity participation)
Statewide the mean Community Involvement score is 63% out of a possible 100%. Populations in remote rural areas (67%), remote towns (67%) and large suburbs (66%) have the highest mean scores, while large cities (61%) and distant towns (62%) have the lowest.
The YVI assesses potential for isolation from adequate support for well-being, combining four indicators: the rates at which youth became teen mothers, did not complete high school, were placed in foster care, and lived in households with very low incomes. In each place indicators are ranked on a scale of 1-4:
The YVI is the average of these four scores in a given location. Counties with a very low YVI score (1 or close to 1) have low rates across all indicators, and therefore lower levels of vulnerability. Counties with a very high YVI scores (4 or close to 4) have high rates across all indicators, and therefore relatively high levels of vulnerability.
The YVI maps show YVI scores and related indicator data by county for all youth and for specific sub-groups. But what do YVI scores and indicator data look like by sub-group in the state as a whole? Here we share some analyses in chart form for females, males, and six racial/ethnic categories: White, Asian, Black, Hispanic/Latino, American Indian/Native Alaskan, and Two or More(*1).
For the data sources please see the “Data” section of this website. Consider doing your own research and/or using other research to add to analyses we offer here.
(*1) These analyses employ statewide datasets for each population. They do not reflect averages across counties.
Available data allow us to calculate the YVI using the categories “male” and “female.” The chart below shows that statewide females scored slightly higher than males on the YVI overall. However, there were some differences across individual indicators.
Foster Care Entry (number of children and youth ages 0-17 per 1000, 2009-2011)
Females entered foster care at slightly higher rates than males (3.46 females per 1000 versus 3.19 males per 1000).
High School Non-Completion (number of youth ages 20-24 per 100)
Among young males ages 20 to 24, 21.06 per 100 did not complete high school, in comparison with 14.66 per 100 young women.
Very Low Family Income (number of youth ages 12 to 17 per 100)
Females lived in families with incomes below the federal poverty line at almost the same rates than males (19.19 females ages 0-17 per 1000 versus 18.93 males ages 0-17 per 1000).
Teen Birth Rates (number of births to females ages 15-19 per 1000)
Unfortunately there are no data available on births to teen fathers, so we report here only on births to teen mothers. Statewide from 2009-2011, 30.22 females ages 15-19 per 1000 gave birth.
Calculating the YVI by race/ethnicity is challenging because different data sources that make up the index use varying racial/ethnic categories. We used categories that could be applied across all data sets. Unfortunately these categories do not differentiate between many meaningful racial/ethnic categories (for example, breaking out all national/ethnic groups within the “Asian” and “Black” categories, breaking out racial and ethnic groups in the “Hispanic” category, knowing who makes up the group that identified with “two or more” racial/ethnic groups). The chart below shows statewide YVI scores for each racial/ethnic category (AIAN stands for “American Indian/Alaskan Native). The YVI score for all of California is shown at the far right.
This chart shows that statewide young people identified as Black and young people identified as American Indian/Alaskan Native are experiencing at high rates all four conditions associated with isolation from support for well-being. Young people identified as Hispanic/Latino or identified with two or more racial/ethnic categories are also experiencing most of these conditions at high rates. White youth experience these conditions at the lowest rates in the state.
The Youth Vulnerability Index includes 4 indicators of isolation from adequate support for well-being: the rates at which young people were placed in foster care, did not complete high school, lived in households with very low incomes, and became teen mothers. (see more on this on the “About Data” page). The charts below show the actual statewide rates by race/ethnicity.
Foster Care Entry (number of children and youth ages 0-17 per 1000)
Statewide children and youth identified as Black and children and youth identified as Native American/Alaskan Native entered foster care at the highest rates from 2009-2011. Children identified as Asian entered foster care at the lowest rates.
High School Non-Completion (number of youth ages 20-24 per 100)
Statewide just over 28 of every 100 young people identified as Hispanic had not completed high school at the highest rates. American Indian/Alaskan Native and Black youth had also not completed high school at high rates. Asian and White youth were least likely to have not completed high school.
Very Low Family Income (number of youth ages 12 to 17 per 100)
Statewide the families of more than 25 of every 100 young people identified as Black, Hispanic or American Indian/Alaskan Native earned less than the federal poverty line. The families of approximately 8 of every 100 young people identified as White experienced these economic circumstances.
Teen Birth Rates (number of births to females ages 15-19 per 1000)
Statewide female teens identified as Hispanic had the highest birth rates from 2009-2011, followed by Black and American Indian/Alaskan Native female teens. Young women identified as Asian had the lowest birth rates.
