The state of California has two Permanency Innovation Initiative (PII) grantees; they operate the California Partners for Permanency (CAPP) and Recognize. Intervene. Support. Empower (RISE) projects. These presentation provides an overview of their strategies to reduce the number of children and youth in long-term foster care.

Metadata-only record linking to the original dataset. Open original dataset below.

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.

Center for Mental Health Services (CMHS) Uniform Reporting System (URS) Output Tables for 2012. The Uniform Reporting System (URS) is a state and national reporting system collected annually to support the Community Mental Health Services Block Grant program. State Mental Health Authorities report on National Outcome Measures (NOMS), evidence based practices, and utilization measures providing an overview of state mental health delivery systems.

The FY2016 State Summaries provide an overview of benefits, services, demographics and population of Veterans analyzed by state.

The Office of Data Governance and Analysis (DGA) creates statistical data for various Veteran related projects. This table displays the count and percent, by county, of Veterans who are farmers and/or dairymen comparative for the entire state's population of Veteran farmers or dairymen in California for 2015. The data was created from our administrative database U.S. Veterans Eligibility Trends and Statistics (USVETS), for the recent event Apps for Ag Hackathon. The U.S. Veterans Eligibility Trends and Statistics (USVETS) is the single integrated dataset of Veteran demographic and socioeconomic data. It provides the most comprehensive picture of the Veteran population possible to support statistical, trend and longitudinal analysis. USVETS has both a static dataset, represents a single authoritative record of all living and deceased Veterans, and fiscal year datasets, represents a snapshot of a Veteran for each fiscal year. USVETS consists mainly of data sources from the Veterans Benefit Administration, the Veterans Health Administration, the Department of Defense’s Defense Manpower Data Center, and other data sources including commercial data sources. This dataset contains information about individual Veterans including demographics, details of military service, VA benefit usage, and more. The dataset contains one record per Veteran. It includes all living and deceased Veterans. USVETS data includes Veterans residing in states, US territories and foreign countries. VA uses this database to conduct statistical analytics, predictive modeling, and other data reporting. USVETS includes the software, hardware, and the associated processes that produce various VA work products and related files for Veteran analytics.

12011 population data for individuals 18 years and older in Canada was obtained from Statistics Canada [36].22010 population data for individuals 18 years and older in the US was obtained from the US Census Bureau [38].3Regions were: Midwest (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin); Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont); South (Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia); West (Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming).42006 education data for individuals 20 years and over in Canada (most current and available data) [35].52010 education data for individuals 18 years and over in the US [37].*p

National Center for Veterans Analysis and Statistics, Contact: www.va.gov/vetdataSources: VA Veteran Population Projection Model, VA Geographic Distribution of Expenditures, VA Annual Benefits Report, U.S. Census Bureau, American Community Survey

The National Consortium for Assessing Drug Control Initiatives, funded by the Bureau of Justice Assistance and coordinated by the Criminal Justice Statistics Association, collected drug offender processing data from eight states: Alaska, California, Iowa, Minnesota, Nebraska, New York, North Carolina, and Virginia. The purpose of the project was to track adult drug offenders from the point of entry into the criminal justice system (typically by arrest) through final court disposition, regardless of whether the offender was released without trial, acquitted, or convicted. These data allow researchers to examine how the criminal justice system processes drug offenders, to measure the changing volume of drug offenders moving through the different segments of the criminal justice system, to calculate processing time intervals between major decision-making events, and to assess the changing structure of the drug offender population. For purposes of this project, a drug offender was defined as any person who had been charged with a felony drug offense. The data are structured into six segments pertaining to (1) record identification, (2) the offender (date of birth, sex, race, ethnic origin), (3) arrest information (date of arrest, age at arrest, arrest charge code), (4) prosecution information (filed offense code and level, prosecution disposition and date), (5) court disposition information (disposition offense and level, court disposition, final disposition date, final pleading, type of trial), and (6) sentencing information (sentence and sentence date, sentence minimum and maximum). Also included are elapsed time variables. The unit of analysis is the felony drug offender.

The FY2017 State Summaries provide an overview of benefits, services, demographics and population of Veterans analyzed by state.

Reported numbers of SARS-CoV-2 infections by age group (5–11, 12–17, 18–49, 50–64, ≥65 years of age) from 22 U.S. jurisdictions (AR, AZ, CA, CO, CT, DC, FL, GA, IN, KS, MI, MA, MN, NC, NE, NJ, NM, NYC, PHL, TN, UT, WI ); ~53% of the U.S. population) with routine linkages between COVID-19 case surveillance and immunization information system (IIS) data reported to CDC during January 16, 2022 – May 28, 2022. Vaccine administration (coverage) data reported to CDC were aggregated by U.S. reporting jurisdiction, MMWR week of vaccination (≥14 days after completing the primary vaccine series), FDA-approved vaccine products, and age group (5–11, 12–17, 18–49, 50–64, ≥65 years). Vaccination status: A person vaccinated with at least a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing BNT162b2 (Pfizer-BioNTech) primary series. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. To estimate the number of unvaccinated persons in each MMWR week, the 2019 U.S. Census population estimates by jurisdiction and age group were used (except for California, where State Department of Finance 2021 population projections were determined to be more accurate). The number of unvaccinated persons each MMWR week was estimated by subtracting the cumulative number of vaccinated (all products) and partially vaccinated persons (all products) from the respective population totals for each jurisdiction and age group. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent rates from growing unrealistically large due to potential overestimates of vaccination coverage.

Offender Based Transaction Statistics (OBTS) studies are designed to collect information by tracking adult offenders from the point of entry into the criminal justice system (typically by arrest) through final disposition, regardless of whether the offender is convicted or acquitted. Information is provided on arrest, police action, prosecutor action, level of charges, charges filed by the prosecutor, type of counsel, pretrial status, type of trial, sentence type, and sentence length. This allows researchers to examine how the criminal justice system processes offenders, to measure the changing volume of offenders moving through the different segments of the criminal justice system, to calculate processing time intervals between major decision-making events, and to assess the changing structure of the offender population.

This point shapefile represents all air monitoring stations active in California from 2001 until 2003. The data within the shapefile was obtained from the Air Resources Board's Ambient Air Quality Data Summaries (ADAM) database.Developed for the California Air Resources Board (ARB) GIS data library.

This polygon shapefile contains federal one-hour ozone area designations and their classifications as per 40CFR81.305, part of the National Ambient Air Quality Standard (NAAQS) which monitors pollutants in accordance with the Clean Air Act. There are three designation categories: nonattainment, unclassifiable and attainment areas. Non-attainment areas are those which are in violation of the standard. Nonattainment areas have various classifications. Each designation and classification area has an associated date field that represents the "effective" date of the action as published in 40CFR81.305. This data layer is current as of November 2003. Projection: Teale Albers, NAD83This shapefile can be used to identify designation areas for ozone pollution attainment defined pursuant to the corresponding federal National Ambient Air Quality Standard for each pollutant as per the Clean Air Act.Read More

Every 10 years, the number of seats a state has in the U.S. House of Representatives, and therefore the Electoral College, changes based on population. While many states experienced no change in representation due to the 2020 Census, a few states gained or lost seats. Texas notably gained *** seats due to an increase in population, while New York, Michigan, California, West Virginia, Pennsylvania, Ohio, and Illinois all lost *** seat.

This change will stay in place until 2030, when the next Census is conducted in the United States.

In 2023, there were around 1.11 million active duty U.S. Armed Forces personnel stationed within the United States. In that year, there were 156,418 U.S. Armed Forces personnel stationed in California, the most of any state.

This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of California. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of California. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for California. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7JD4TSG

The table VA-Demographic-2025-09-17 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 5980442 rows across 698 variables.

In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household income in of 136,692 U.S. dollars. In that year, San Jose in California was ranked second, and Seattle, Washington third.

Following a fall after the great recession, median household income in the United States has been increasing in recent years. As of 2022, median household income by state was highest in Maryland, Washington, D.C., Utah, and Massachusetts. It was lowest in Mississippi, West Virginia, and Arkansas. Families with an annual income of 25,000 and 49,999 U.S. dollars made up the largest income bracket in America, with about 25.26 million households.

Data on median household income can be compared to statistics on personal income in the U.S. released by the Bureau of Economic Analysis. Personal income rose to around 21.8 trillion U.S. dollars in 2022, the highest value recorded. Personal income is a measure of the total income received by persons from all sources, while median household income is “the amount with divides the income distribution into two equal groups,” according to the U.S. Census Bureau. Half of the population in question lives above median income and half lives below. Though total personal income has increased in recent years, this wealth is not distributed throughout the population. In practical terms, income of most households has decreased. One additional statistic illustrates this disparity: for the lowest quintile of workers, mean household income has remained more or less steady for the past decade at about 13 to 16 thousand constant U.S. dollars annually. Meanwhile, income for the top five percent of workers has actually risen from about 285,000 U.S. dollars in 1990 to about 499,900 U.S. dollars in 2020.

The table VA-Voter-History-2025-07-22 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 5971527 rows across 458 variables.

Collection details1, and estimates of mitochondrial genetic variation within individual samples and associated topographically-bounded populations (in bold); sample size (N), total number of haplotypes (H), number of private haplotypes (Hp), haplotype diversity (Hd), and average number of pairwise differences between sequences (k).1These populations were collected primarily by SJS and ADG with assistance from a variety of cooperators including: D.E. Bright, T.W. Coleman, P.L. Dallara, S.M. Hishinuma, C.L. Jorgensen, C.A. Leslie, A.S. Munson, C. Parker, L. Pederson, D. Reboletti, N.A. Tisserat, and many others from agencies such as the New Mexico, Ohio, Tennessee, and Virginia Departments of Agriculture, Bernalillo County Extension, NM and University of California Cooperative Extension.[W] Population sample surveyed for infection with the α-proteobacterium Wolbachia.Characterization of United States populations of the walnut twig beetle, Pityophthorus juglandis.