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 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 was for the group of age 30 to 34 years years with a population of 2.98 million (7.61%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in California was the 80 to 84 years years with a population of 680,447 (1.73%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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

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

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

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

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

This table contains data on the percent of population age 25 and up with a four-year college degree or higher for California, its regions, counties, county subdivisions, cities, towns, and census tracts. Greater educational attainment has been associated with health-promoting behaviors including consumption of fruits and vegetables and other aspects of healthy eating, engaging in regular physical activity, and refraining from excessive consumption of alcohol and from smoking. Completion of formal education (e.g., high school) is a key pathway to employment and access to healthier and higher paying jobs that can provide food, housing, transportation, health insurance, and other basic necessities for a healthy life. Education is linked with social and psychological factors, including sense of control, social standing and social support. These factors can improve health through reducing stress, influencing health-related behaviors and providing practical and emotional support. More information on the data table and a data dictionary can be found in the Data and Resources section. The educational attainment 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. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf

The format of the educational attainment 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.

Comprehensive demographic dataset for California, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Source

This is the dataset used in this book: https://github.com/ageron/handson-ml/tree/master/datasets/housing to illustrate a sample end-to-end ML project workflow (pipeline). This is a great book - I highly recommend!

The data is based on California Census in 1990.

About the Data (from the book):

"This dataset is a modified version of the California Housing dataset available from Luís Torgo's page (University of Porto). Luís Torgo obtained it from the StatLib repository (which is closed now). The dataset may also be downloaded from StatLib mirrors.

The following is the description from the book author:

This dataset appeared in a 1997 paper titled Sparse Spatial Autoregressions by Pace, R. Kelley and Ronald Barry, published in the Statistics and Probability Letters journal. They built it using the 1990 California census data. It contains one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).

The dataset in this directory is almost identical to the original, with two differences: 207 values were randomly removed from the total_bedrooms column, so we can discuss what to do with missing data. An additional categorical attribute called ocean_proximity was added, indicating (very roughly) whether each block group is near the ocean, near the Bay area, inland or on an island. This allows discussing what to do with categorical data. Note that the block groups are called "districts" in the Jupyter notebooks, simply because in some contexts the name "block group" was confusing."

About the Data (From Luís Torgo page):

http://www.dcc.fc.up.pt/%7Eltorgo/Regression/cal_housing.html

This is a dataset obtained from the StatLib repository. Here is the included description:

"We collected information on the variables using all the block groups in California from the 1990 Cens us. In this sample a block group on average includes 1425.5 individuals living in a geographically co mpact area. Naturally, the geographical area included varies inversely with the population density. W e computed distances among the centroids of each block group as measured in latitude and longitude. W e excluded all the block groups reporting zero entries for the independent and dependent variables. T he final data contained 20,640 observations on 9 variables. The dependent variable is ln(median house value)."

End-to-End ML Project Steps (Chapter 2 of the book)

1. Look at the big picture
2. Get the data
3. Discover and visualize the data to gain insights
4. Prepare the data for Machine Learning algorithms
5. Select a model and train it
6. Fine-tune your model
7. Present your solution
8. Launch, monitor, and maintain your system

The 10-Step Machine Learning Project Workflow (My Version)

1. Define business object
2. Make sense of the data from a high level
   • data types (number, text, object, etc.)
   • continuous/discrete
   • basic stats (min, max, std, median, etc.) using boxplot
   • frequency via histogram
   • scales and distributions of different features
3. Create the traning and test sets using proper sampling methods, e.g., random vs. stratified
4. Correlation analysis (pair-wise and attribute combinations)
5. Data cleaning (missing data, outliers, data errors)
6. Data transformation via pipelines (categorical text to number using one hot encoding, feature scaling via normalization/standardization, feature combinations)
7. Train and cross validate different models and select the most promising one (Linear Regression, Decision Tree, and Random Forest were tried in this tutorial)
8. Fine tune the model using trying different combinations of hyperparameters
9. Evaluate the model with best estimators in the test set
10. Launch, monitor, and refresh the model and system

More than 39 million people and 14.2 million households span more than 163,000 square miles of Californian’s urban, suburban and rural communities. California has the fifth largest economy in the world and is the most populous state in the nation, with nation-leading diversity in race, ethnicity, language and socioeconomic conditions. These characteristics make California amazingly unique amongst all 50 states, but also present significant challenges to counting every person and every household, no matter the census year. A complete and accurate count of a state’s population in a decennial census is essential. The results of the 2020 Census will inform decisions about allocating hundreds of billions of dollars in federal funding to communities across the country for hospitals, fire departments, school lunch programs and other critical programs and services. The data collected by the United States Census Bureau (referred hereafter as U.S. Census Bureau) also determines the number of seats each state has in the U.S. House of Representatives and will be used to redraw State Assembly and Senate boundaries. California launched a comprehensive Complete Count Census 2020 Campaign (referred to hereafter as the Campaign) to support an accurate and complete count of Californians in the 2020 Census. Due to the state’s unique diversity and with insights from past censuses, the Campaign placed special emphasis on the hardest-tocount Californians and those least likely to participate in the census. The California Complete Count – Census 2020 Office (referred to hereafter as the Census Office) coordinated the State’s operations to complement work done nationally by the U.S. Census Bureau to reach those households most likely to be missed because of barriers, operational or motivational, preventing people from filling out the census. The Campaign, which began in 2017, included key phases, titled Educate, Motivate and Activate. Each of these phases were designed to make sure all Californians knew about the census, how to respond, their information was safe and their participation would help their communities for the next 10 years.

Six metrics were used to determine Population Vulnerability: global population size, annual occurrence in the California Current System (CCS), percent of the population present in the CCS, threat status, breeding score, and annual adult survival. Global Population size (POP)—to determine population size estimates for each species we gathered information tabulated by American Bird Conservancy, Birdlife International, and other primary sources. Proportion of Population in CCS (CCSpop)—for each species, we generated the population size within the CCS by averaging region-wide population estimates, or by combining state estimates for California, Oregon, and Washington for each species (if estimates were not available for a region or state, “NA” was recorded in place of a value) and then dividing the CCSpop value by the estimated global population size (POP) to yield the percentage of the population occurring in the CCS. Annual Occurrence in the CCS (AO)—for each species, we estimated the number of months per year within the CCS and binned this estimate into three categories: 1–4 months, 5–8 months, or 9–12 months. Threat Status (TS)—for each species, we used the International Union for Conservation of Nature (IUCN) species threat status (IUCN 2014) and the U.S. Fish and Wildlife national threat status lists (USFWS 2014) to determine TS values for each species. If available, we also evaluated threat status values from state and international agencies. Breeding Score (BR)—we determined the degree to which a species breeds and feeds its young in the CCS according to 3 categories: breeds in the CCS, may breed in the CCS, or does not breed in the CCS. Adult Survival (AS)—for each species, we referenced information to estimate adult annual survival, because adult survival among marine birds in general is the most important demographic factor that can affect population growth rate and therefore inform vulnerability. These data support the following publication: Adams, J., Kelsey, E.C., Felis J.J., and Pereksta, D.M., 2016, Collision and displacement vulnerability among marine birds of the California Current System associated with offshore wind energy infrastructure: U.S. Geological Survey Open-File Report 2016-1154, 116 p., https://doi.org/10.3133/ofr20161154. These data were revisied in June 2017 and the revision published in August 2017. Please be advised to use CCS_vulnerability_FINAL_VERSION_v9_PV.csv

This dataset displays the locations of all the public libraries in the state of California. The data included is the name of the library, name of the library system, library's address, phone, and lat/lon coordinates. The data came from publiclibraries.com which is a updated directory of all the public libraries throughout the United States.

ECM Community Support Services tables for a Quarterly Implementation Report. Including the County and Plan Details for both ECM and Community Support.This Medi-Cal Enhanced Care Management (ECM) and Community Supports Calendar Year Quarterly Implementation Report provides a comprehensive overview of ECM and Community Supports implementation in the programs' first year. It includes data at the state, county, and plan levels on total members served, utilization, and provider networks.ECM is a statewide MCP benefit that provides person-centered, community-based care management to the highest need members. The Department of Health Care Services (DHCS) and its MCP partners began implementing ECM in phases by Populations of Focus (POFs), with the first three POFs launching statewide in CY 2022.Community Supports are services that address members’ health-related social needs and help them avoid higher, costlier levels of care. Although it is optional for MCPs to offer these services, every Medi-Cal MCP offered Community Supports in 2022, and at least two Community Supports services were offered and available in every county by the end of the year.

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Age-Adjusted Incidence Rate (AAIR)Age-adjustment is a statistical method that allows comparisons of incidence rates to be made between populations with different age distributions. This is important since the incidence of most cancers increases with age. An age-adjusted cancer incidence (or death) rate is defined as the number of new cancers (or deaths) per 100,000 population that would occur in a certain period of time if that population had a 'standard' age distribution. In the California Health Maps, incidence rates are age-adjusted using the U.S. 2000 Standard Population.Cancer incidence ratesIncidence rates were calculated using case counts from the California Cancer Registry. Population data from 2010 Census and SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators. Yearly SEER 2015 census tract estimates by race/origin (controlling to Vintage 2015) were used to estimate population denominators for 5-year incidence rates (2013-2017)According to California Department of Public Health guidelines, cancer incidence rates cannot be reported if based on <15 cancer cases and/or a population <10,000 to ensure confidentiality and stable statistical rates.Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

The 2023 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The cartographic boundary files include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The generalized boundaries of most incorporated places in this file are based on those as of January 1, 2023, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries of all CDPs are based on those delineated or updated as part of the the 2023 BAS or the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

The dataset ds181_Chinook_ln is a product of the CalFish Adult Salmonid Abundance Database. Data in this shapefile are collected from stream sections or reaches where Chinook population monitoring occurs and that are best represented by linear features. Some escapement monitoring locations are logically represented by point features, such as dams and hatcheries. See the companion point feature shapefile ds180_Chinook_pnts for information collected from point locations.The CalFish Abundance Database contains a comprehensive collection of anadromous fisheries abundance information. Beginning in 1998, the Pacific States Marine Fisheries Commission, the California Department of Fish and Game, and the National Marine Fisheries Service, began a cooperative project aimed at collecting, archiving, and entering into standardized electronic formats, the wealth of information generated by fisheries resource management agencies and tribes throughout California.The data format provides for sufficient detail to convey the relative accuracy of each population trend index record yet is simple and straight forward enough to be suited for public use. For those interested in more detail the database offers hyperlinks to digital copies of the original documents used to compile the information. In this way the database serves as an information hub directing the user to additional supporting information. This offers utility to field biologists and others interested in obtaining information for more in-depth analysis. Hyperlinks, built into the spatial data attribute tables used in the BIOS and CalFish I-map viewers, open the detailed index data archived in the on-line CalFish database application. The information can also be queried directly from the database via the CalFish Tabular Data Query. Once the detailed annual trend data are in view, another hyperlink opens a digital copy of the document used to compile each record.During 2010, as a part of the Central Valley Chinook Comprehensive Monitoring Plan, the CalFish Salmonid Abundance Database was reorganized and updated. CalFish provides a central location for sharing Central Valley Chinook salmon escapement estimates and annual monitoring reports to all stakeholders, including the public. Annual Chinook salmon in-river escapement indices that were, in many cases, eight to ten years behind are now current though 2009. In some cases, multiple datasets were consolidated into a single, more comprehensive, dataset to more closely reflect how data are reported in the California Department of Fish and Game standard index, Grandtab.Extensive data are currently available in the CalFish Abundance Database for California Chinook, coho, and steelhead. Major data categories include adult abundance population estimates, actual fish and/or carcass counts, counts of fish collected at dams, weirs, or traps, and redd counts. Harvest data has also been compiled for many streams.This CalFish Abundance Database shapefile was generated from fully routed 1:100,000 hydrography. In a few cases streams had to be added to the hydrography dataset in order to provide a means to create shapefiles to represent abundance data associated with them. Streams added were digitized at no more than 1:24,000 scale based on stream line images portrayed in 1:24,000 Digital Raster Graphics (DRG).The features in this layer represent the location for which abundance data records apply. In many cases there are multiple datasets associated with the same location, and so, features may overlap. Please view the associated datasets for detail regarding specific features. In CalFish these are accessed through the "link" field that is visible when performing an identify or query operation. A URL string is provided with each feature in the downloadable data which can also be used to access the underlying datasets.The Chinook data that is available from the CalFish website is actually mirrored from the StreamNet website where the CalFish Abundance Databases tabular data is currently stored. Additional information about StreamNet may be downloaded at http://www.streamnet.org. Complete documentation for the StreamNet database may be accessed at http://http://www.streamnet.org/def.html

The 2022 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The cartographic boundary files include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The generalized boundaries of most incorporated places in this file are based on those as of January 1, 2022, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries of all CDPs are based on those delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

Transportation to Work by Race/Ethnicity

Risk Factors and Inequalities in California

By Health [source]

About this dataset

This table contains important data on the mode of transportation used by California residents aged 16 years and older. This information is sourced from the U.S. Census Bureau Decennial Census and American Community Survey and given as part of a series of indicators as part of the Healthy Communities Data and Indicators Project created by the Office of Health Equity.

Commuting to work makes up a large portion - 19% -of overall travel miles in the United States, with automobiles being overwhelmingly preferred by commuters over other methods like walking or biking. Automobiles show an impressive level of personal mobility, however they are associated with certain hazards such as air pollution, car crashes, pedestrian injuries, sedentary lifestyles linked to stress-related health problems and more. Alternatives such as walking alone or combined with public transport offer physical activity which has been linked to lower rates for diseases like heart disease, stroke, diabetes colon cancer breast cancer dementia depression etc., however these forms do come with their own risks; urban areas especially feature higher collision risks seeking pedestrians due to increased vehicle density while bus/rail passengers face less risk than motorcyclists pedestrians or bicyclists.

But this isn't just any average statistic; certain disadvantaged minority communities bear a disproportionate share when it comes to pedestrian-car fatalities: Native American males have an astonishingly 4 times higher death rate compared to Whites or Asians whereas African-Americans & Latinos face double risk than their respective counterparts; factors like stereotypes regarding race based driving behavior can be partially responsible for this discrepancy further marching for more research into this area our part towards embracing greater equality for all races/ethnicities . As such this data acquired from HealthData & CHHS Open Data is presented in hopes that greater awareness can be generated on current situation leading ultimately towards improving safety & providing better mobility options uniformly across all communities

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How to use the dataset

This dataset contains information on the mode of transportation to work for California residents aged 16 and older by race/ethnicity. It provides an excellent opportunity to compare commute data across different regions, counties, geographies, and ethnicities. This dataset can be used in many ways and can give insights into how different communities utilize different modes of transportation.

To get started using this dataset, begin by filtering the data to narrow down the criteria you are looking for (e.g., region_code or county_fips). Once you have narrowed down your selection of data points, you can use a variety of visualizations to gain insights into population segments who use various means of transport. For example, you could create charts such as bar graphs, line graphs or pie charts that display population patterns across year groups within a given area or particular demographic groupings (race/ethnicity). Additionally, this information could be used for public policy related applications such as informing zones about allocating resources to increase accessibility or safety related concerns with certain modes etc.

By examining this dataset further it is also possible to make comparative analyses between several years which may shed light on social trends over time in regards to commuting behaviors which could potentially reveal potential opportunities when planning infrastructure projects or commuter-friendly services such as ridesharing groups etc., through identifying current commuting gaps in given areas relative two other nearby regions based on mode usage shifts throughout various timespans within the years included in this dataset's range (2000-2010).

In conclusion; whether studying historical trends or analyzing present activity –this Transportation To Work 2000-2006-2010 Dataset holds invaluable insight on travel trends among California’s populous providing great potential for expansive research endeavors as well as guiding decision makers from city councils toward more effective policies & projects delivering positive community impact & productivity benefits

Research Ideas

• Investigating the relationship between mode of transportation and health among different racial/ethnic groups in California and also comparisons across regions.
• ...

In early February 2024, we will be retiring the Mpox Vaccinations Given to SF Residents by Demographics dataset. This dataset will be archived and no longer update. A historic record of this data will remain available.

A. SUMMARY This dataset represents doses of mpox vaccine (JYNNEOS) administered in California to residents of San Francisco ages 18 years or older. This dataset only includes doses of the JYNNEOS vaccine given on or after 5/1/2022. All vaccines given to people who live in San Francisco are included, no matter where the vaccination took place. The data are broken down by multiple demographic stratifications.

B. HOW THE DATASET IS CREATED Information on doses administered to those who live in San Francisco is from the California Immunization Registry (CAIR2), run by the California Department of Public Health (CDPH). Information on individuals’ city of residence, age, race, ethnicity, and sex are recorded in CAIR2 and are self-reported at the time of vaccine administration. Because CAIR2 does not include information on sexual orientation, we pull information from the San Francisco Department of Public Health’s Epic Electronic Health Record (EHR). The populations represented in our Epic data and the CAIR2 data are different. Epic data only include vaccinations administered at SFDPH managed sites to SF residents.

Data notes for population characteristic types are listed below.

Age * Data only include individuals who are 18 years of age or older.

Race/ethnicity * The response option "Other Race" is categorized by the data source system, and the response option "Unknown" refers to a lack of data.

Sex * The response option "Other" is categorized by the source system, and the response option "Unknown" refers to a lack of data.

Sexual orientation * The response option “Unknown/Declined” refers to a lack of data or individuals who reported multiple different sexual orientations during their most recent interaction with SFDPH.

For convenience, we provide the 2020 5-year American Community Survey population estimates.

C. UPDATE PROCESS Updated daily via automated process.

D. HOW TO USE THIS DATASET This dataset includes many different types of demographic groups. Filter the “demographic_group” column to explore a topic area. Then, the “demographic_subgroup” column shows each group or category within that topic area and the total count of doses administered to that population subgroup.

E. CHANGE LOG

• UPDATE 1/3/2023: Due to low case numbers, this page will no longer include vaccinations after 12/31/2022.

This table contains data on the percent of adults (18 years or older) who are registered voters and the percent of adults who voted in general elections, for California, its regions, counties, cities/towns, and census tracts. Data is from the Statewide Database, University of California Berkeley Law, and the California Secretary of State, Elections Division. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Political participation can be associated with the health of a community through two possible mechanisms: through the implementation of social policies or as an indirect measure of social capital. Disparities in political participation across socioeconomic groups can influence political outcomes and the resulting policies could have an impact on the opportunities available to the poor to live a healthy life. Lower representation of poorer voters could result in reductions of social programs aimed toward supporting disadvantaged groups. Although there is no direct evidentiary connection between voter registration or participation and health, there is evidence that populations with higher levels of political participation also have greater social capital. Social capital is defined as resources accessed by individuals or groups through social networks that provide a mutual benefit. Several studies have shown a positive association between social capital and lower mortality rates, and higher self- assessed health ratings. There is also evidence of a cycle where lower levels of political participation are associated with poor self-reported health, and poor self-reported health hinders political participation. More information about the data table and a data dictionary can be found in the About/Attachments section.