In 2023, the resident population of California was ***** million. This is a slight decrease from the previous year, with ***** million people in 2022. This makes it the most populous state in the U.S. Californian demographics Along with an increase in population, California’s gross domestic product (GDP) has also been increasing, from *** trillion U.S. dollars in 2000 to **** trillion U.S. dollars in 2023. In the same time period, the per-capita personal income has almost doubled, from ****** U.S. dollars in 2000 to ****** U.S. dollars in 2022. In 2023, the majority of California’s resident population was Hispanic or Latino, although the number of white residents followed as a close second, with Asian residents making up the third-largest demographic in the state. The dark side of the Golden State While California is one of the most well-known states in the U.S., is home to Silicon Valley, and one of the states where personal income has been increasing over the past 20 years, not everyone in California is so lucky: In 2023, the poverty rate in California was about ** percent, and the state had the fifth-highest rate of homelessness in the country during that same year, with an estimated ** homeless people per 10,000 of the population.

Chart and table of population level and growth rate for the state of California from 1900 to 2024.

About this dataset

Context

The dataset tabulates the California population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of California across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2024, the population of California was 39.43 million, a 0.59% increase year-by-year from 2023. Previously, in 2023, California population was 39.2 million, an increase of 0.14% compared to a population of 39.14 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of California increased by 5.44 million. In this period, the peak population was 39.52 million in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the California is shown in this column.
• Year on Year Change: This column displays the change in California population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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/.

Recommended for further research

This dataset is a part of the main dataset for California Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the California, KY population pyramid, which represents the California population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for California, KY, is 6.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for California, KY, is 11.4.
• Total dependency ratio for California, KY is 18.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for California, KY is 8.8.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the California population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the California for the selected age group is shown in the following column.
• Population (Female): The female population in the California for the selected age group is shown in the following column.
• Total Population: The total population of the California for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for California Population by Age. You can refer the same here

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

Graph and download economic data for Resident Population in California (CAPOP) from 1900 to 2024 about residents, CA, population, and USA.

Data Source: CA Department of Finance

Data: Population estimates for January 1, 2011, through January 1, 2020. The population estimates benchmark for April 1, 2010 is also provided.

Citation: State of California, Department of Finance, E-4 Population Estimates for Cities, Counties, and the State, 2011-2020, with 2010 Census Benchmark. Sacramento, California, May 2022.

For detailed information on methodology and other data considerations, visit: https://dof.ca.gov/Forecasting/Demographics/Estimates/e-4-population-estimates-for-cities-counties-and-the-state-2011-2020-with-2010-census-benchmark-new/

About this dataset

Context

The dataset tabulates the California City population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of California City across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of California City was 15,127, a 0.65% increase year-by-year from 2021. Previously, in 2021, California City population was 15,029, a decline of 0.15% compared to a population of 15,051 in 2020. Over the last 20 plus years, between 2000 and 2022, population of California City increased by 6,717. In this period, the peak population was 15,127 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the California City is shown in this column.
• Year on Year Change: This column displays the change in California City population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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/.

Recommended for further research

This dataset is a part of the main dataset for California City Population by Year. You can refer the same here

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

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. To learn more about this effort, please visit the report home page at https://ceo.lacounty.gov/ardi/sbla/. For more information about the purpose of this data, please contact CEO-ARDI. For more information about the configuration of this data, please contact ISD-Enterprise GIS. table_name indicator_name Universe source timeframe source_url

life_expectancy_countyhealthrankings_2020 Life Expectancy Total Population County Health Rankings 2018-2020 https://www.countyhealthrankings.org/app/california/2022/measure/outcomes/147/data

obese_est_adult_lachs_2018 Obese Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

obese_perc_adult_lachs_2018 Obese Percent (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

overweight_est_adult_lachs_2018 Overweight Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

overweight_perc_adult_lachs_2018 Overweight Percent (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

diabetes_est_adult_lachs_2018 Ever Diagnosed with Diabetes Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

diabetes_perc_adult_lachs_2018 Ever Diagnosed with Diabetes Percent (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

regular_source_of_care_est_adult_lachs_2018 Reported Having a Regular Source of Health Care Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

regular_source_of_care_perc_adult_lachs_2018 Reported Having a Regular Source of Health Care Percent (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

depression_est_adult_lachs_2018 Ever Diagnosed with Depression Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

depression_perc_adult_lachs_2018 Ever Diagnosed with Depression Percent (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

perceived_safe_est_adult_lachs_2018 Perceived Their Neighborhood to Be Safe from Crime Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

perceived_safe_perc_adult_lachs_2018 Perceived Their Neighborhood to Be Safe from Crime Estimate (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

dental_care_est_child_lachs_2018 Had Dental Care within the past Year Estimate (#) Children (Ages 17 Years and Younger) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

dental_care_perc_child_lachs_2018 Had Dental Care within the past Year Percent (%) Children (Ages 17 Years and Younger) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

no_usual_source_est_chis_2020 No usual source of care Estimate (#) Total Population California Health Interview Survey 2020 https://ask.chis.ucla.edu/AskCHIS/tools/_layouts/AskChisTool/home.aspx

no_usual_source_perc_chis_2020 No usual source of care Percent (%) Total Population California Health Interview Survey 2020 https://ask.chis.ucla.edu/AskCHIS/tools/_layouts/AskChisTool/home.aspx

delayed_care_est_chis_2020 Delayed or didn't get medical care last year Estimate (#) Total Population California Health Interview Survey 2020 https://ask.chis.ucla.edu/AskCHIS/tools/_layouts/AskChisTool/home.aspx

delayed_care_est_chis_2020 Delayed or didn't get medical care last year Percent (%) Total Population California Health Interview Survey 2020 https://ask.chis.ucla.edu/AskCHIS/tools/_layouts/AskChisTool/home.aspx

covid_vax_one_or_more_est_2022 COVID-19 Vaccination 1+ Dose Estimate (#) Population 6 months and older LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_perc_2022 COVID-19 Vaccination 1+ Dose Percent (%) Population 6 months and older LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_full_est_2022 COVID-19 Fully Vaccinated Estimate (#) Population 6 months and older LAC DPH Sep-22publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_full_perc_2022 COVID-19 Fully Vaccinated Percent (%) Population 6 months and older LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_children_est_2022 COVID-19 Vaccination 1+ Dose - Children under 5 Estimate (#) Population older than 6 months and under 5 years LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_children_perc_2022 COVID-19 Vaccination 1+ Dose Children under 5 Percent (%) Population older than 6 months and under 5 years LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_youth_est_2022 COVID-19 Vaccination 1+ Dose - Youth 5-17 Estimate (#) Population 5-17 years LAC DPH Sep-22publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_youth_perc_2022 COVID-19 Vaccination 1+ Dose Youth 5-17 Percent (%) Population 5-17 years LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_adults_est_2022 COVID-19 Vaccination 1+ Dose - Adults Estimate (#) Population 18 and older LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_adults_perc_2022 COVID-19 Vaccination 1+ Dose Adults Percent (%) Population 18 and older LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

insured_pop_est_acs_2020 Insured population # Civilian noninstitutionalized population 2016-2020 ACS - S2701 https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSST5Y2020.S2701

insured_pop_perc_acs_2020 Insured population % Civilian noninstitutionalized population 2016-2020 ACS - S2701 https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSST5Y2020.S2701

mch_indicators_2019 Babies Born with Positive MCH Indicators Babies born in time frame Strong Start Index 2016-2019 https://infogram.com/1pj576jwy166z1s6ywvk32l5lkammrym3wy?live

current_asthma Percent of Adults (Ages 18 Years and Older) with Current Asthma Adults Los Angeles County Health Survey 2018 https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

no_med_insurance Percent of Insured Adults (Ages 18 Years and Older) Who Reported a Time Without Medical Insurance in the past 12 Months. Adults Los Angeles County Health Survey 2011 https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

transportation_problems Percent of Adults (Ages 18 Years and Older) Who Reported That Transportation Problems Kept Them from Obtaining Needed Medical Care in the past Year. Adults Los Angeles County Health Survey 2007 https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

These data are the result of an intersection between a surface representing the delta-finite rate of population change and another surface representing Greater Sage-grouse abundance and space-use. It was used to rank candidate sites according to greatest potential impact to Greater Sage-grouse populations resulting from the presence of geothermal energy activity. In 2022, candidate geothermal sites were identified in Nevada and eastern California, then buffered by 10 kilometers. While the extent of the overall raster layer encompasses a very large swath of the western US, data values are limited to fall within these 10 kilometer buffers.

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.

About this dataset

Context

The dataset tabulates the California population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for California. The dataset can be utilized to understand the population distribution of California by age. For example, using this dataset, we can identify the largest age group in California.

Key observations

The largest age group in California, KY was for the group of age 60 to 64 years years with a population of 17 (32.69%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in California, KY was the 5 to 9 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the California is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of California total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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

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Recommended for further research

This dataset is a part of the main dataset for California Population by Age. You can refer the same here

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals may increase or decrease. Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups. B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health. Data on the population characteristics of COVID-19 deaths are from: Case reports Medical records Electronic lab reports Death certificates Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths. To protect resident privacy, we summarize COVID-19 data by only one population characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more. Data notes on select population characteristic types are listed below. Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. Gender * The City collects information on gender identity using these guidelines. C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week. Dataset will not update on the business day following any federal holiday. D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a dataset based on the San Francisco Population and Demographic Census dataset.These population estimates are from the 2018-2022 5-year American Community Survey (ACS). This dataset includes several characteristic types. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cumulative deaths. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed. To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset. E. CHANGE LOG

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.

As of July 2nd, 2024 the COVID-19 Deaths by Population Characteristics Over Time dataset has been retired. This dataset is archived and will no longer update. We will be publishing a cumulative deaths by population characteristics dataset that will update moving forward.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date.

New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

This data may not be immediately available for more recent deaths. Data updates as more information becomes available.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - on this date, we began using an updated definition of a COVID-19 death to align with the California Department of Public Health. This change was applied to COVID-19 deaths retrospectively beginning on 1/1/2023. More information about the recommendation by the Council of State and Territorial Epidemiologists that motivated this change can be found https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">here.
• 6/6/2023 - data on deaths by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on deaths by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 1/31/2023 - column “population_estimate” added.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.

Daily Miles Traveled (T14)

FULL MEASURE NAME

Total vehicle miles traveled

LAST UPDATED

August 2022

DESCRIPTION

Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for total vehicle miles traveled.

DATA SOURCE

California Department of Transportation: California Public Road Data/Highway Performance Monitoring System - http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php

2001-2020

Federal Highway Administration: Highway Statistics - https://www.fhwa.dot.gov/policyinformation/statistics/2020/hm71.cfm

2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/

2001-2020

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html

2020

CONTACT INFORMATION

vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)

Vehicle miles traveled (VMT) reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examines county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas for which the majority of its population falls within a given metropolitan area (9-county region for the San Francisco Bay Area and the primary metropolitan statistical area (MSA) for all others). For the metro analysis, no VMT data is available in rural areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

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

VITAL SIGNS INDICATOR
Housing Production (LU4)

FULL MEASURE NAME
Produced housing units by unit type

LAST UPDATED
February 2023

DESCRIPTION
Housing production is measured in terms of the number of units that local jurisdictions produces throughout a given year. The annual production count captures housing units added by new construction and annexations, subtracts demolitions and destruction from natural disasters, and adjusts for units lost or gained by conversions.

DATA SOURCE
California Department of Finance, Form E-8 - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/
1990-2010

California Department of Finance, Form E-5 - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/
2011-2022

U.S. Census Bureau Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2000-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Single-family housing units include single detached units and single attached units. Multi-family housing includes two to four units and five plus or apartment units.

Housing production data for the region, counties, and cities for each year is the difference of annual housing unit estimates from the California Department of Finance. Housing production data for metropolitan areas for each year is the difference of annual housing unit estimates from the Census Bureau’s Population Estimates Program. CA Department of Finance data uses an annual cycle between January 1 and December 31, whereas U.S. Census Bureau data uses an annual cycle from April 1 to March 31 of the following year.

This dataset contains the Local Area Unemployment Statistics (LAUS), annual averages from 1990 to 2024.

The Local Area Unemployment Statistics (LAUS) program is a Federal-State cooperative effort in which monthly estimates of total employment and unemployment are prepared for approximately 7,600 areas, including counties, cities and metropolitan statistical areas. These estimates are key indicators of local economic conditions.

The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

Estimates for counties are produced through a building-block approach known as the "Handbook method." This procedure also uses data from several sources, including the CPS, the CES program, state UI systems, and the Census Bureau's American Community Survey (ACS), to create estimates that are adjusted to the statewide measures of employment and unemployment. Estimates for cities are prepared using disaggregation techniques based on inputs from the ACS, annual population estimates, and current UI data.