24 datasets found
  1. c

    20 Richest Counties in California

    • california-demographics.com
    Updated Jun 20, 2024
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    Kristen Carney (2024). 20 Richest Counties in California [Dataset]. https://www.california-demographics.com/counties_by_population
    Explore at:
    Dataset updated
    Jun 20, 2024
    Dataset provided by
    Cubit Planning, Inc.
    Authors
    Kristen Carney
    License

    https://www.california-demographics.com/terms_and_conditionshttps://www.california-demographics.com/terms_and_conditions

    Area covered
    California
    Description

    A dataset listing California counties by population for 2024.

  2. N

    counties in California Ranked by Hispanic White Population // 2025 Edition

    • neilsberg.com
    csv, json
    Updated Feb 13, 2025
    + more versions
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    Neilsberg Research (2025). counties in California Ranked by Hispanic White Population // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lists/counties-in-california-by-hispanic-white-population/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 13, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    California
    Variables measured
    Hispanic White Population, Hispanic White Population as Percent of Total Population of counties in California, Hispanic White Population as Percent of Total Hispanic White Population of California
    Measurement technique
    To measure the rank and respective trends, we initially gathered data from the five most recent American Community Survey (ACS) 5-Year Estimates. We then analyzed and categorized the data for each of the racial categories identified by the U.S. Census Bureau. Based on the required racial category classification, we calculated the rank. For geographies with no population reported for the chosen race, we did not assign a rank and excluded them from the list. It is possible that a small population exists but was not reported or captured due to limitations or variations in Census data collection and reporting. We ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories and do not rely on any ethnicity classification, unless explicitly required.For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    This list ranks the 55 counties in the California by Hispanic White population, as estimated by the United States Census Bureau. It also highlights population changes in each counties over the past five years.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

    • 2019-2023 American Community Survey 5-Year Estimates
    • 2018-2022 American Community Survey 5-Year Estimates
    • 2017-2021 American Community Survey 5-Year Estimates
    • 2016-2020 American Community Survey 5-Year Estimates
    • 2015-2019 American Community Survey 5-Year Estimates

    Variables / Data Columns

    • Rank by Hispanic White Population: This column displays the rank of counties in the California by their Hispanic White population, using the most recent ACS data available.
    • counties: The counties for which the rank is shown in the previous column.
    • Hispanic White Population: The Hispanic White population of the counties is shown in this column.
    • % of Total counties Population: This shows what percentage of the total counties population identifies as Hispanic White. Please note that the sum of all percentages may not equal one due to rounding of values.
    • % of Total California Hispanic White Population: This tells us how much of the entire California Hispanic White population lives in that counties. Please note that the sum of all percentages may not equal one due to rounding of values.
    • 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

  3. Largest counties in the U.S. 2022

    • statista.com
    Updated Jul 5, 2024
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    Statista (2024). Largest counties in the U.S. 2022 [Dataset]. https://www.statista.com/statistics/241702/largest-counties-in-the-us/
    Explore at:
    Dataset updated
    Jul 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    United States
    Description

    This statistic shows the 25 largest counties in the United States in 2022, by population. In 2022, about 9.72 million people were estimated to be living in Los Angeles County, California.

    Additional information on urbanization in the United States

    Urbanization is defined as the process by which cities grow or by which societies become more urban. Rural to urban migration in the United States, and around the world, is often undertaken in the search for employment or to enjoy greater access to services such as healthcare. The largest cities in the United States are steadily growing. Given their size, incremental increases yield considerable numerical gains as seen by New York increasing by 69,777 people in 2011, the most of any city. However in terms of percentage growth, smaller cities outside the main centers are growing the fastest, such as Georgetown city and Leander city in Texas.

    Urbanization has increased slowly in the United States, rising from 80.77 percent of the population living in urban areas in 2010 to 82.66 percent in 2020. In 2018, the United States ranked 14th in a ranking of countries based on their degree of urbanization. Unlike fully urbanized countries such as Singapore and Hong Kong, the United States maintains a sizeable agricultural industry. Although technological developments have reduced demands for rural labor, labor in the industry and supporting services are still required.

  4. N

    counties in California Ranked by Non-Hispanic Pacific Islander Population //...

    • neilsberg.com
    csv, json
    Updated Feb 13, 2025
    + more versions
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    Neilsberg Research (2025). counties in California Ranked by Non-Hispanic Pacific Islander Population // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lists/counties-in-california-by-non-hispanic-pacific-islander-population/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 13, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    California
    Variables measured
    Non-Hispanic Pacific Islander Population, Non-Hispanic Pacific Islander Population as Percent of Total Population of counties in California, Non-Hispanic Pacific Islander Population as Percent of Total Non-Hispanic Pacific Islander Population of California
    Measurement technique
    To measure the rank and respective trends, we initially gathered data from the five most recent American Community Survey (ACS) 5-Year Estimates. We then analyzed and categorized the data for each of the racial categories identified by the U.S. Census Bureau. Based on the required racial category classification, we calculated the rank. For geographies with no population reported for the chosen race, we did not assign a rank and excluded them from the list. It is possible that a small population exists but was not reported or captured due to limitations or variations in Census data collection and reporting. We ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories and do not rely on any ethnicity classification, unless explicitly required.For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    This list ranks the 55 counties in the California by Non-Hispanic Native Hawaiian and Other Pacific Islander (NHPI) population, as estimated by the United States Census Bureau. It also highlights population changes in each counties over the past five years.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

    • 2019-2023 American Community Survey 5-Year Estimates
    • 2018-2022 American Community Survey 5-Year Estimates
    • 2017-2021 American Community Survey 5-Year Estimates
    • 2016-2020 American Community Survey 5-Year Estimates
    • 2015-2019 American Community Survey 5-Year Estimates

    Variables / Data Columns

    • Rank by Non-Hispanic Pacific Islander Population: This column displays the rank of counties in the California by their Non-Hispanic Native Hawaiian and Other Pacific Islander (NHPI) population, using the most recent ACS data available.
    • counties: The counties for which the rank is shown in the previous column.
    • Non-Hispanic Pacific Islander Population: The Non-Hispanic Pacific Islander population of the counties is shown in this column.
    • % of Total counties Population: This shows what percentage of the total counties population identifies as Non-Hispanic Pacific Islander. Please note that the sum of all percentages may not equal one due to rounding of values.
    • % of Total California Non-Hispanic Pacific Islander Population: This tells us how much of the entire California Non-Hispanic Pacific Islander population lives in that counties. Please note that the sum of all percentages may not equal one due to rounding of values.
    • 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

  5. N

    counties in California Ranked by Non-Hispanic Asian Population // 2025...

    • neilsberg.com
    csv, json
    Updated Feb 13, 2025
    + more versions
    Share
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    Neilsberg Research (2025). counties in California Ranked by Non-Hispanic Asian Population // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lists/counties-in-california-by-non-hispanic-asian-population/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 13, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    California
    Variables measured
    Non-Hispanic Asian Population, Non-Hispanic Asian Population as Percent of Total Population of counties in California, Non-Hispanic Asian Population as Percent of Total Non-Hispanic Asian Population of California
    Measurement technique
    To measure the rank and respective trends, we initially gathered data from the five most recent American Community Survey (ACS) 5-Year Estimates. We then analyzed and categorized the data for each of the racial categories identified by the U.S. Census Bureau. Based on the required racial category classification, we calculated the rank. For geographies with no population reported for the chosen race, we did not assign a rank and excluded them from the list. It is possible that a small population exists but was not reported or captured due to limitations or variations in Census data collection and reporting. We ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories and do not rely on any ethnicity classification, unless explicitly required.For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    This list ranks the 55 counties in the California by Non-Hispanic Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each counties over the past five years.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

    • 2019-2023 American Community Survey 5-Year Estimates
    • 2018-2022 American Community Survey 5-Year Estimates
    • 2017-2021 American Community Survey 5-Year Estimates
    • 2016-2020 American Community Survey 5-Year Estimates
    • 2015-2019 American Community Survey 5-Year Estimates

    Variables / Data Columns

    • Rank by Non-Hispanic Asian Population: This column displays the rank of counties in the California by their Non-Hispanic Asian population, using the most recent ACS data available.
    • counties: The counties for which the rank is shown in the previous column.
    • Non-Hispanic Asian Population: The Non-Hispanic Asian population of the counties is shown in this column.
    • % of Total counties Population: This shows what percentage of the total counties population identifies as Non-Hispanic Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
    • % of Total California Non-Hispanic Asian Population: This tells us how much of the entire California Non-Hispanic Asian population lives in that counties. Please note that the sum of all percentages may not equal one due to rounding of values.
    • 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

  6. T

    Vital Signs: Population – by region shares (updated)

    • data.bayareametro.gov
    application/rdfxml +5
    Updated Apr 13, 2020
    + more versions
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    California Department of Finance (2020). Vital Signs: Population – by region shares (updated) [Dataset]. https://data.bayareametro.gov/dataset/Vital-Signs-Population-by-region-shares-updated-/7m6i-as8d
    Explore at:
    application/rssxml, csv, json, xml, application/rdfxml, tsvAvailable download formats
    Dataset updated
    Apr 13, 2020
    Dataset authored and provided by
    California Department of Finance
    Description

    VITAL SIGNS INDICATOR Population (LU1)

    FULL MEASURE NAME Population estimates

    LAST UPDATED October 2019

    DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

    DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

    California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

    U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

    U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

    U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

    CONTACT INFORMATION vitalsigns.info@bayareametro.gov

    METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

    Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

    Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

    Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

    The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

  7. 2011 03: California Population Change 2000-2010

    • opendata.mtc.ca.gov
    • hub.arcgis.com
    Updated Mar 23, 2011
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    MTC/ABAG (2011). 2011 03: California Population Change 2000-2010 [Dataset]. https://opendata.mtc.ca.gov/documents/2011-03-california-population-change-2000-2010/about
    Explore at:
    Dataset updated
    Mar 23, 2011
    Dataset provided by
    Metropolitan Transportation Commission
    Authors
    MTC/ABAG
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    California
    Description

    The data for this map is based on the recently released 2010 census data, and shows that all counties within the state registered a population increase except Plumas, Sierra, and Alpine counties. Placer, Kern, and Riverside counties experienced the largest population growth with more than a 25% increase over the past 10 years.

  8. T

    Vital Signs: Population – by PDA (2022)

    • data.bayareametro.gov
    application/rdfxml +5
    Updated May 20, 2022
    + more versions
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    (2022). Vital Signs: Population – by PDA (2022) [Dataset]. https://data.bayareametro.gov/dataset/Vital-Signs-Population-by-PDA-2022-/pdk3-u57j
    Explore at:
    csv, json, application/rssxml, application/rdfxml, tsv, xmlAvailable download formats
    Dataset updated
    May 20, 2022
    Description

    VITAL SIGNS INDICATOR Population (LU1)

    FULL MEASURE NAME
    Population estimates

    LAST UPDATED
    February 2023

    DESCRIPTION
    Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

    DATA SOURCE
    California Department of Finance: Population and Housing Estimates - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/
    Table E-6: County Population Estimates (1960-1970)
    Table E-4: Population Estimates for Counties and State (1970-2021)
    Table E-8: Historical Population and Housing Estimates (1990-2010)
    Table E-5: Population and Housing Estimates (2010-2021)

    Bay Area Jurisdiction Centroids (2020) - https://data.bayareametro.gov/Boundaries/Bay-Area-Jurisdiction-Centroids-2020-/56ar-t6bs
    Computed using 2020 US Census TIGER boundaries

    U.S. Census Bureau: Decennial Census Population Estimates - http://www.s4.brown.edu/us2010/index.htm- via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University
    1970-2020

    U.S. Census Bureau: American Community Survey (5-year rolling average; tract) - https://data.census.gov/
    2011-2021
    Form B01003

    Priority Development Areas (Plan Bay Area 2050) - https://opendata.mtc.ca.gov/datasets/MTC::priority-development-areas-plan-bay-area-2050/about

    CONTACT INFORMATION
    vitalsigns.info@bayareametro.gov

    METHODOLOGY NOTES (across all datasets for this indicator)
    All historical data reported for Census geographies (metropolitan areas, county, city and tract) use current legal boundaries and names. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of December 2022.

    Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

    Population estimates for Bay Area tracts and PDAs are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Population estimates for PDAs are allocated from tract-level Census population counts using an area ratio. For example, if a quarter of a Census tract lies with in a PDA, a quarter of its population will be allocated to that PDA. Estimates of population density for PDAs use gross acres as the denominator. Note that the population densities between PDAs reported in previous iterations of Vital Signs are mostly not comparable due to minor differences and an updated set of PDAs (previous iterations reported Plan Bay Area 2040 PDAs, whereas current iterations report Plan Bay Area 2050 PDAs).

    The following is a list of cities and towns by geographical area:

    Big Three: San Jose, San Francisco, Oakland

    Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside

    Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville

    Unincorporated: all unincorporated towns

  9. O

    County Expenditures Per Capita

    • bythenumbers.sco.ca.gov
    • data.ca.gov
    • +1more
    application/rdfxml +5
    Updated Dec 28, 2017
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    California State Controller's Office (2017). County Expenditures Per Capita [Dataset]. https://bythenumbers.sco.ca.gov/Counties/County-Expenditures-Per-Capita/miui-wb29
    Explore at:
    csv, application/rdfxml, application/rssxml, json, xml, tsvAvailable download formats
    Dataset updated
    Dec 28, 2017
    Dataset authored and provided by
    California State Controller's Office
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    Per capita values are calculated by dividing the estimated population into total expenditures per county, per fiscal year.

  10. California: counties recording the most BEV sales in 2023

    • statista.com
    Updated Dec 11, 2024
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    Statista (2024). California: counties recording the most BEV sales in 2023 [Dataset]. https://www.statista.com/statistics/1378447/california-leading-counties-for-bev-sales/
    Explore at:
    Dataset updated
    Dec 11, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States, California
    Description

    Los Angeles, Orange, and Santa Clara were the Californian counties recording the most battery-electric vehicle sales in 2023. In Los Angeles, nearly 102,900 fully electric were sold to consumers that year, which was around 55,100 vehicles more than Orange County.

  11. d

    Annual point-in-time (PIT) estimates of homelessness reveal stark...

    • search.dataone.org
    Updated Nov 8, 2023
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    Baginski, Pamela (2023). Annual point-in-time (PIT) estimates of homelessness reveal stark differences among San Francisco Bay Area counties [Dataset]. http://doi.org/10.7910/DVN/YQZCNK
    Explore at:
    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Baginski, Pamela
    Area covered
    San Francisco Bay Area
    Description

    INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.

  12. C

    California Urban Area Delineations

    • data.ca.gov
    Updated Jul 2, 2025
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    California Department of Finance (2025). California Urban Area Delineations [Dataset]. https://data.ca.gov/dataset/california-urban-area-delineations
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    arcgis geoservices rest api, htmlAvailable download formats
    Dataset updated
    Jul 2, 2025
    Dataset provided by
    Calif. Dept. of Finance Demographic Research Unit
    Authors
    California Department of Finance
    Area covered
    California
    Description

    The Census Bureau released revised delineations for urban areas on December 29, 2022. The new criteria (contained in this Federal Register Notice) is based primarily on housing unit density measured at the census block level. The minimum qualifying threshold for inclusion as an urban area is an area that contains at least 2,000 housing units or has a population of at least 5,000 persons. It also eliminates the classification of areas as “urban clusters/urbanized areas”. This represents a change from 2010, where urban areas were defined as areas consisting of 50,000 people or more and urban clusters consisted of at least 2,500 people but less than 50,000 people with at least 1,500 people living outside of group quarters. Due to the new population thresholds for urban areas, 36 urban clusters in California are no longer considered urban areas, leaving California with 193 urban areas after the new criteria was implemented.

    The State of California experienced an increase of 1,885,884 in the total urban population, or 5.3%. However, the total urban area population as a percentage of the California total population went down from 95% to 94.2%. For more information about the mapped data, download the Excel spreadsheet here.

    Please note that some of the 2020 urban areas have different names or additional place names as a result of the inclusion of housing unit counts as secondary naming criteria.

    Please note there are four urban areas that cross state boundaries in Arizona and Nevada. For 2010, only the parts within California are displayed on the map; however, the population and housing estimates represent the entirety of the urban areas. For 2020, the population and housing unit estimates pertains to the areas within California only.

    Data for this web application was derived from the 2010 and 2020 Censuses (2010 and 2020 Census Blocks, 2020 Urban Areas, and Counties) and the 2016-2020 American Community Survey (2010 -Urban Areas) and can be found at data.census.gov.

    For more information about the urban area delineations, visit the Census Bureau's Urban and Rural webpage and FAQ.

    To view more data from the State of California Department of Finance, visit the Demographic Research Unit Data Hub.

  13. N

    counties in California Ranked by Multi-Racial Other Race Population // 2025...

    • neilsberg.com
    csv, json
    Updated Feb 13, 2025
    + more versions
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    Neilsberg Research (2025). counties in California Ranked by Multi-Racial Other Race Population // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/lists/counties-in-california-by-multi-racial-other-race-population/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 13, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    California
    Variables measured
    Multi-Racial Other Race Population, Multi-Racial Other Race Population as Percent of Total Population of counties in California, Multi-Racial Other Race Population as Percent of Total Multi-Racial Other Race Population of California
    Measurement technique
    To measure the rank and respective trends, we initially gathered data from the five most recent American Community Survey (ACS) 5-Year Estimates. We then analyzed and categorized the data for each of the racial categories identified by the U.S. Census Bureau. Based on the required racial category classification, we calculated the rank. For geographies with no population reported for the chosen race, we did not assign a rank and excluded them from the list. It is possible that a small population exists but was not reported or captured due to limitations or variations in Census data collection and reporting. We ensured that the population estimates used in this dataset pertain exclusively to the identified racial categories and do not rely on any ethnicity classification, unless explicitly required.For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    This list ranks the 55 counties in the California by Multi-Racial Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each counties over the past five years.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

    • 2019-2023 American Community Survey 5-Year Estimates
    • 2018-2022 American Community Survey 5-Year Estimates
    • 2017-2021 American Community Survey 5-Year Estimates
    • 2016-2020 American Community Survey 5-Year Estimates
    • 2015-2019 American Community Survey 5-Year Estimates

    Variables / Data Columns

    • Rank by Multi-Racial Other Race Population: This column displays the rank of counties in the California by their Multi-Racial Some Other Race (SOR) population, using the most recent ACS data available.
    • counties: The counties for which the rank is shown in the previous column.
    • Multi-Racial Other Race Population: The Multi-Racial Other Race population of the counties is shown in this column.
    • % of Total counties Population: This shows what percentage of the total counties population identifies as Multi-Racial Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
    • % of Total California Multi-Racial Other Race Population: This tells us how much of the entire California Multi-Racial Other Race population lives in that counties. Please note that the sum of all percentages may not equal one due to rounding of values.
    • 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

  14. K

    California 2050 Projected Urban Growth

    • koordinates.com
    csv, dwg, geodatabase +6
    Updated Oct 13, 2003
    + more versions
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    State of California (2003). California 2050 Projected Urban Growth [Dataset]. https://koordinates.com/layer/671-california-2050-projected-urban-growth/
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    dwg, geopackage / sqlite, geodatabase, kml, pdf, shapefile, mapinfo tab, mapinfo mif, csvAvailable download formats
    Dataset updated
    Oct 13, 2003
    Dataset authored and provided by
    State of California
    License

    https://koordinates.com/license/attribution-3-0/https://koordinates.com/license/attribution-3-0/

    Area covered
    Description

    50 year Projected Urban Growth scenarios. Base year is 2000. Projected year in this dataset is 2050.

    By 2020, most forecasters agree, California will be home to between 43 and 46 million residents-up from 35 million today. Beyond 2020 the size of California's population is less certain. Depending on the composition of the population, and future fertility and migration rates, California's 2050 population could be as little as 50 million or as much as 70 million. One hundred years from now, if present trends continue, California could conceivably have as many as 90 million residents. Where these future residents will live and work is unclear. For most of the 20th Century, two-thirds of Californians have lived south of the Tehachapi Mountains and west of the San Jacinto Mountains-in that part of the state commonly referred to as Southern California. Yet most of coastal Southern California is already highly urbanized, and there is relatively little vacant land available for new development. More recently, slow-growth policies in Northern California and declining developable land supplies in Southern California are squeezing ever more of the state's population growth into the San Joaquin Valley. How future Californians will occupy the landscape is also unclear. Over the last fifty years, the state's population has grown increasingly urban. Today, nearly 95 percent of Californians live in metropolitan areas, mostly at densities less than ten persons per acre. Recent growth patterns have strongly favored locations near freeways, most of which where built in the 1950s and 1960s. With few new freeways on the planning horizon, how will California's future growth organize itself in space? By national standards, California's large urban areas are already reasonably dense, and economic theory suggests that densities should increase further as California's urban regions continue to grow. In practice, densities have been rising in some urban counties, but falling in others.

    These are important issues as California plans its long-term future. Will California have enough land of the appropriate types and in the right locations to accommodate its projected population growth? Will future population growth consume ever-greater amounts of irreplaceable resource lands and habitat? Will jobs continue decentralizing, pushing out the boundaries of metropolitan areas? Will development densities be sufficient to support mass transit, or will future Californians be stuck in perpetual gridlock? Will urban and resort and recreational growth in the Sierra Nevada and Trinity Mountain regions lead to the over-fragmentation of precious natural habitat? How much water will be needed by California's future industries, farms, and residents, and where will that water be stored? Where should future highway, transit, and high-speed rail facilities and rights-of-way be located? Most of all, how much will all this growth cost, both economically, and in terms of changes in California's quality of life? Clearly, the more precise our current understanding of how and where California is likely to grow, the sooner and more inexpensively appropriate lands can be acquired for purposes of conservation, recreation, and future facility siting. Similarly, the more clearly future urbanization patterns can be anticipated, the greater our collective ability to undertake sound city, metropolitan, rural, and bioregional planning.

    Consider two scenarios for the year 2100. In the first, California's population would grow to 80 million persons and would occupy the landscape at an average density of eight persons per acre, the current statewide urban average. Under this scenario, and assuming that 10% percent of California's future population growth would occur through infill-that is, on existing urban land-California's expanding urban population would consume an additional 5.06 million acres of currently undeveloped land. As an alternative, assume the share of infill development were increased to 30%, and that new population were accommodated at a density of about 12 persons per acre-which is the current average density of the City of Los Angeles. Under this second scenario, California's urban population would consume an additional 2.6 million acres of currently undeveloped land. While both scenarios accommodate the same amount of population growth and generate large increments of additional urban development-indeed, some might say even the second scenario allows far too much growth and development-the second scenario is far kinder to California's unique natural landscape.

    This report presents the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2100. Presented in map and table form, these projections are based on extrapolations of current population trends and recent urban development trends. The next section, titled Approach, outlines the methodology and data used to develop the various projections. The following section, Baseline Scenario, reviews the projections themselves. A final section, entitled Baseline Impacts, quantitatively assesses the impacts of the baseline projections on wetland, hillside, farmland and habitat loss.

  15. California: counties recording the most PHEV sales in 2023

    • statista.com
    Updated Dec 11, 2024
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    Statista (2024). California: counties recording the most PHEV sales in 2023 [Dataset]. https://www.statista.com/statistics/1378455/california-leading-counties-for-plug-in-hybrid-electric-sales/
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    Dataset updated
    Dec 11, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States, California
    Description

    The Los Angeles county recorded nearly 21,000 sales of plug-in electric vehicles (PHEVs) to consumers in 2023, making it the largest market for PHEVs in California. This was close to 14,800 sales more than those recorded in Orange County, which was second in the ranking. Los Angeles was also the county with the largest battery-electric sales volume that year.

  16. C

    Medical Service Study Areas

    • data.chhs.ca.gov
    • data.ca.gov
    • +3more
    Updated Dec 6, 2024
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    Department of Health Care Access and Information (2024). Medical Service Study Areas [Dataset]. https://data.chhs.ca.gov/dataset/medical-service-study-areas
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    geojson, html, zip, arcgis geoservices rest api, kml, csvAvailable download formats
    Dataset updated
    Dec 6, 2024
    Dataset provided by
    CA Department of Health Care Access and Information
    Authors
    Department of Health Care Access and Information
    Description
    This is the current Medical Service Study Area. California Medical Service Study Areas are created by the California Department of Health Care Access and Information (HCAI).

    Check the Data Dictionary for field descriptions.


    Checkout the California Healthcare Atlas for more Medical Service Study Area information.

    This is an update to the MSSA geometries and demographics to reflect the new 2020 Census tract data. The Medical Service Study Area (MSSA) polygon layer represents the best fit mapping of all new 2020 California census tract boundaries to the original 2010 census tract boundaries used in the construction of the original 2010 MSSA file. Each of the state's new 9,129 census tracts was assigned to one of the previously established medical service study areas (excluding tracts with no land area), as identified in this data layer. The MSSA Census tract data is aggregated by HCAI, to create this MSSA data layer. This represents the final re-mapping of 2020 Census tracts to the original 2010 MSSA geometries. The 2010 MSSA were based on U.S. Census 2010 data and public meetings held throughout California.


    <a href="https://hcai.ca.gov/">https://hcai.ca.gov/</a>

    Source of update: American Community Survey 5-year 2006-2010 data for poverty. For source tables refer to InfoUSA update procedural documentation. The 2010 MSSA Detail layer was developed to update fields affected by population change. The American Community Survey 5-year 2006-2010 population data pertaining to total, in households, race, ethnicity, age, and poverty was used in the update. The 2010 MSSA Census Tract Detail map layer was developed to support geographic information systems (GIS) applications, representing 2010 census tract geography that is the foundation of 2010 medical service study area (MSSA) boundaries. ***This version is the finalized MSSA reconfiguration boundaries based on the US Census Bureau 2010 Census. In 1976 Garamendi Rural Health Services Act, required the development of a geographic framework for determining which parts of the state were rural and which were urban, and for determining which parts of counties and cities had adequate health care resources and which were "medically underserved". Thus, sub-city and sub-county geographic units called "medical service study areas [MSSAs]" were developed, using combinations of census-defined geographic units, established following General Rules promulgated by a statutory commission. After each subsequent census the MSSAs were revised. In the scheduled revisions that followed the 1990 census, community meetings of stakeholders (including county officials, and representatives of hospitals and community health centers) were held in larger metropolitan areas. The meetings were designed to develop consensus as how to draw the sub-city units so as to best display health care disparities. The importance of involving stakeholders was heightened in 1992 when the United States Department of Health and Human Services' Health and Resources Administration entered a formal agreement to recognize the state-determined MSSAs as "rational service areas" for federal recognition of "health professional shortage areas" and "medically underserved areas". After the 2000 census, two innovations transformed the process, and set the stage for GIS to emerge as a major factor in health care resource planning in California. First, the Office of Statewide Health Planning and Development [OSHPD], which organizes the community stakeholder meetings and provides the staff to administer the MSSAs, entered into an Enterprise GIS contract. Second, OSHPD authorized at least one community meeting to be held in each of the 58 counties, a significant number of which were wholly rural or frontier counties. For populous Los Angeles County, 11 community meetings were held. As a result, health resource data in California are collected and organized by 541 geographic units. The boundaries of these units were established by community healthcare experts, with the objective of maximizing their usefulness for needs assessment purposes. The most dramatic consequence was introducing a data simultaneously displayed in a GIS format. A two-person team, incorporating healthcare policy and GIS expertise, conducted the series of meetings, and supervised the development of the 2000-census configuration of the MSSAs.

    MSSA Configuration Guidelines (General Rules):- Each MSSA is composed of one or more complete census tracts.- As a general rule, MSSAs are deemed to be "rational service areas [RSAs]" for purposes of designating health professional shortage areas [HPSAs], medically underserved areas [MUAs] or medically underserved populations [MUPs].- MSSAs will not cross county lines.- To the extent practicable, all census-defined places within the MSSA are within 30 minutes travel time to the largest population center within the MSSA, except in those circumstances where meeting this criterion would require splitting a census tract.- To the extent practicable, areas that, standing alone, would meet both the definition of an MSSA and a Rural MSSA, should not be a part of an Urban MSSA.- Any Urban MSSA whose population exceeds 200,000 shall be divided into two or more Urban MSSA Subdivisions.- Urban MSSA Subdivisions should be within a population range of 75,000 to 125,000, but may not be smaller than five square miles in area. If removing any census tract on the perimeter of the Urban MSSA Subdivision would cause the area to fall below five square miles in area, then the population of the Urban MSSA may exceed 125,000. - To the extent practicable, Urban MSSA Subdivisions should reflect recognized community and neighborhood boundaries and take into account such demographic information as income level and ethnicity. Rural Definitions: A rural MSSA is an MSSA adopted by the Commission, which has a population density of less than 250 persons per square mile, and which has no census defined place within the area with a population in excess of 50,000. Only the population that is located within the MSSA is counted in determining the population of the census defined place. A frontier MSSA is a rural MSSA adopted by the Commission which has a population density of less than 11 persons per square mile. Any MSSA which is not a rural or frontier MSSA is an urban MSSA. Last updated December 6th 2024.
  17. g

    Veteran Farmer Counts and Percentages in California Counties (2015) |...

    • gimi9.com
    Updated Dec 26, 2018
    + more versions
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    (2018). Veteran Farmer Counts and Percentages in California Counties (2015) | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_veteran-farmer-counts-and-percentages-in-california-counties-2015/
    Explore at:
    Dataset updated
    Dec 26, 2018
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    California
    Description

    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.

  18. Park, Beach, Open Space, or Coastline Access

    • data.ca.gov
    • data.chhs.ca.gov
    • +4more
    csv, html, pdf, xlsx +1
    Updated Apr 21, 2025
    + more versions
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    California Department of Public Health (2025). Park, Beach, Open Space, or Coastline Access [Dataset]. https://data.ca.gov/dataset/park-beach-open-space-or-coastline-access
    Explore at:
    html, xlsx, zip, csv, pdfAvailable download formats
    Dataset updated
    Apr 21, 2025
    Dataset authored and provided by
    California Department of Public Healthhttps://www.cdph.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This table contains data on access to parks measured as the percent of population within ½ a mile of a parks, beach, open space or coastline for California, its regions, counties, county subdivisions, cities, towns, and census tracts. More information on the data table and a data dictionary can be found in the Data and Resources section. As communities become increasingly more urban, parks and the protection of green and open spaces within cities increase in importance. Parks and natural areas buffer pollutants and contribute to the quality of life by providing communities with social and psychological benefits such as leisure, play, sports, and contact with nature. Parks are critical to human health by providing spaces for health and wellness activities. The access to parks table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. The format of the access to parks table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

  19. Largest Mexican-American population groups in the United States, by county...

    • statista.com
    Updated Jun 27, 2012
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    Statista (2012). Largest Mexican-American population groups in the United States, by county 2010 [Dataset]. https://www.statista.com/statistics/234910/us-mexican-population-by-county/
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    Dataset updated
    Jun 27, 2012
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2010
    Area covered
    United States
    Description

    This statistic depicts the largest Mexican-American population groups in different counties across the United States as of 2010. At this time there were 3,510,677 people of Mexican origin living in Los Angeles County in California.

  20. California: share of ZEV sales by largest light-duty vehicle markets 2023

    • statista.com
    Updated Dec 11, 2024
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    Statista (2024). California: share of ZEV sales by largest light-duty vehicle markets 2023 [Dataset]. https://www.statista.com/statistics/1378461/california-share-of-zev-sales-in-the-counties-with-the-largest-light-duty-vehicle-markets/
    Explore at:
    Dataset updated
    Dec 11, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    California
    Description

    Los Angeles, Orange, and San Diego were the counties with the largest light-duty vehicle market. In Los Angeles, nearly 492,400 new vehicles were registered in 2023, a quarter of which were zero-emission vehicles (ZEVs). Santa Clara and Alameda were the counties among the fifteen largest light vehicle markets that recorded the highest share of ZEV sales at 42.4 and 39.7 percent, respectively.

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Kristen Carney (2024). 20 Richest Counties in California [Dataset]. https://www.california-demographics.com/counties_by_population

20 Richest Counties in California

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16 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jun 20, 2024
Dataset provided by
Cubit Planning, Inc.
Authors
Kristen Carney
License

https://www.california-demographics.com/terms_and_conditionshttps://www.california-demographics.com/terms_and_conditions

Area covered
California
Description

A dataset listing California counties by population for 2024.

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