Graph and download economic data for Employment Level for California (EMPLOYCA) from Q4 2003 to Q3 2024 about labor underutilization, labor force, 16 years +, labor, CA, household survey, employment, and USA.

In 2021, there were about **** million children under the age of 18 years old in California -- the most out of any state. In that same year, Texas, Florida, New York, and Illinois rounded out the top five states with the most children under 18.

The U.S. Constitution mandates that the federal government count all persons living in the United States every ten years. The census is critical to states because its results are used to reapportion seats in the U.S. House of Representatives; guide redistricting; and form the basis for allocating federal funds, such as those used for schools, health services, child care, highways, and emergency services.

In response to long-standing concerns about the accuracy of census data and about a possible undercount, a group of researchers conducted the California Neighborhoods Count (CNC) — the first-ever independent, survey-based enumeration to directly evaluate the accuracy of the U.S. Census Bureau's population totals for a subset of California census blocks.

This 2020 research was intended to produce parallel estimates of the 2020 Census population and housing unit totals at the census block level, employing the same items as the census and using enhanced data collection strategies and exploration of imputation methods. Although the CNC was intended to largely replicate census data collection processes, there were a few methodological differences: For example, much of the address canvassing for the 2020 Census was done in-office, whereas the CNC team undertook a complete in-person address-listing operation that included interviews with residents and door-to-door verification of each structure.

In this report, the researchers detail their methodology and present the enumeration results. They compare the 2020 Census counts with the CNC estimates, describe limitations of their data collection effort, and offer considerations for future data collection.

About this dataset

Context

The dataset tabulates the California household income by age. The dataset can be utilized to understand the age-based income distribution of California income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• California annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of California household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of California income distribution by age. You can refer the same here

As of July 2024, the largest age group among the United States population were adults aged 30 to 34 years old. There were 11.9 million males and some 12.1 million females in this age cohort. The total population of the country was estimated to be 340.1 million Which U.S. state has the largest population? The United States is the third most populous country in the world. It is preceded by China and India, and followed by Indonesia in terms of national population. The gender distribution in the U.S. has remained consistent for many years, with the number of females narrowly outnumbering males. In terms of where the residents are located, California was the state with the largest population. The U.S. population by race and ethnicity The United States poses an ethnically diverse population. In 2023, the number of Black or African American individuals was estimated to be 45.76 million, which represented an increase of over four million since the 2010 census. The number of Asian residents has increased at a similar rate during the same time period and the Hispanic population in the U.S. has also continued to grow.

Graph and download economic data for Civilian Labor Force for California (CIVLFCA) from Q4 2003 to Q3 2024 about labor underutilization, civilian, labor force, 16 years +, labor, CA, household survey, and USA.

Summary File 3 contains sample data, which is the information compiled from the questions asked of a sample of all people and housing units in the United States. Population items include basic population totals as well as counts for the following characteristics: urban and rural, households and families, marital status, grandparents as caregivers, language and ability to speak English, ancestry, place of birth, citizenship status, year of entry, migration, place of work, journey to work (commuting), school enrollment and educational attainment, veteran status, disability, employment status, industry, occupation, class of worker, income, and poverty status. Housing items include basic housing totals and counts for urban and rural, number of rooms, number of bedrooms, year moved into unit, household size and occupants per room, units in structure, year structure built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, and monthly rent and shelter costs. The Summary File 3 population tables are identified with a "P" prefix and the housing tables are identified with an "H," followed by a sequential number. The "P" and "H" tables are shown for the block group and higher level geography, while the "PCT" and "HCT" tables are shown for the census tract and higher level geography. There are 16 "P" tables, 15 "PCT" tables, and 20 "HCT" tables that bear an alphabetic suffix on the table number, indicating that they are repeated for nine major race and Hispanic or Latino groups. There are 484 population tables and 329 housing tables for a total of 813 unique tables.

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.

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. This table displays the percentage of adults meeting Aerobic Physical Activity guidelines in California. It contains data for California only. The data are from the California Behavioral Risk Factor Surveillance Survey (BRFSS). The California BRFSS is an annual cross-sectional health-related telephone survey that collects data about California residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS is conducted by the Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. The column percentages are weighted to the 2010 California Department of Finance (DOF) population statistics. Population estimates were obtained from the CA DOF for age, race/ethnicity, and sex. Values may therefore differ from what has been published in the national BRFSS data tables by the Centers for Disease Control and Prevention (CDC) or other federal agencies.

Graph and download economic data for Unemployment Level for California (UNEMPLOYCA) from Q4 2003 to Q3 2024 about labor underutilization, labor force, 16 years +, labor, CA, household survey, unemployment, and USA.

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.
• ...

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.

Building Climates Zones of California Climate Zone Descriptions for New Buildings - California is divided into 16 climatic boundaries or climate zones, which is incorporated into the Energy Efficiency Standards (Energy Code). Each Climate zone has a unique climatic condition that dictates which minimum efficiency requirements are needed for that specific climate zone. The numbers used in the climate zone map don't have a title or legend. The California climate zones shown in this map are not the same as what we commonly call climate areas such as "desert" or "alpine" climates. The climate zones are based on energy use, temperature, weather and other factors.This is explained in the Title 24 energy efficiency standards glossary section:"The Energy Commission established 16 climate zones that represent a geographic area for which an energy budget is established. These energy budgets are the basis for the standards...." "(An) energy budget is the maximum amount of energy that a building, or portion of a building...can be designed to consume per year.""The Energy Commission originally developed weather data for each climate zone by using unmodified (but error-screened) data for a representative city and weather year (representative months from various years). The Energy Commission analyzed weather data from weather stations selected for (1) reliability of data, (2) currency of data, (3) proximity to population centers, and (4) non-duplication of stations within a climate zone."Using this information, they created representative temperature data for each zone. The remainder of the weather data for each zone is still that of the representative city." The representative city for each climate zone (CZ) is:CZ 1: ArcataCZ 2: Santa RosaCZ 3: OaklandCZ 4: San Jose-ReidCZ 5: Santa MariaCZ 6: TorranceCZ 7: San Diego-LindberghCZ 8: FullertonCZ 9: Burbank-GlendaleCZ10: RiversideCZ11: Red BluffCZ12: SacramentoCZ13: FresnoCZ14: PalmdaleCZ15: Palm Spring-IntlCZ16: Blue Canyon

1. Zip code boundaries change, in ways small and large - these are not a stable analysis unit. Data you received keyed to zip codes may have used an earlier and very different boundary for your zip codes of interest.
2. Historically, the United States Postal Service has not published zip code boundaries, and instead, boundary datasets are compiled by third party vendors from address data. That means that the boundary data are not authoritative, and any data you have keyed to zip codes may use a different, vendor-specific method for generating boundaries from the data here.
3. Zip codes are designed to optimize mail delivery, not social, environmental, or demographic characteristics. Analysis using zip codes is subject to create issues with the Modifiable Areal Unit Problem that will bias any results because your units of analysis aren't designed for the data being studied.

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

California was the state with the highest resident population in the United States in 2024, with 39.43 million people. Wyoming had the lowest population with about 590,000 residents. Living the American Dream Ever since the opening of the West in the United States, California has represented the American Dream for both Americans and immigrants to the U.S. The warm weather, appeal of Hollywood and Silicon Valley, as well as cities that stick in the imagination such as San Francisco and Los Angeles, help to encourage people to move to California. Californian demographics California is an extremely diverse state, as no one ethnicity is in the majority. Additionally, it has the highest percentage of foreign-born residents in the United States. By 2040, the population of California is expected to increase by almost 10 million residents, which goes to show that its appeal, both in reality and the imagination, is going nowhere fast.

US Census American Community Survey (ACS) 2016, 5-year estimates of the key economic characteristics of Census Tracts geographic level in Orange County, California. The data contains 397 fields for the variable groups E01: Employment status (universe: population 16 years and over, table X23, 7 fields); E02: Work status by age of worker (universe: population 16 years and over, table X23, 36 fields); E03: Commuting to work (universe: workers 16 years and over, table X8, 8 fields); E04: Travel time to work (universe: workers 16 years and over who did not work at home, table X8, 14 fields); E05: Number of vehicles available for workers (universe: workers 16 years and over in households, table X8, 8 fields); E06: Median age by means of transportation to work (universe: median age, workers 16 years and over, table X8, 7 fields); E07: Means of transportation to work by race (universe: workers 16 years and over, table X8, 64 fields); E08: Occupation (universe: civilian employed population 16 years and over, table X24, 53 fields); E09: Industry (universe: civilian employed population 16 years and over, table X24, 43 fields); E10: Class of worker (universe: civilian employed population 16 years and over, table X24, 19 fields); E11: Household income and earnings in the past 12 months (universe: total households, table X19, 37 fields); E12: Income and earnings in dollars (universe: inflation-adjusted dollars, tables X19-X20, 31 fields); E13: Family income in dollars (universe: total families, table X19, 17 fields); E14: Health insurance coverage (universe: total families, table X19, 17 fields); E15: Ratio of income to Poverty level (universe: total population for whom Poverty level is determined, table X17, 8 fields); E16: Poverty in population in the past 12 months (universe: total population for whom Poverty level is determined, table X17, 7 fields); E17: Poverty in households in the past 12 months (universe: total households, table X17, 9 fields); E18: Percentage of families and people whose income in the past 12 months is below the poverty level (universe: families, population, table X17, 8 fields), and; X19: Poverty and income deficit (dollars) in the past 12 months for families (universe: families with income below Poverty level in the past 12 months, table X17, 4 fields). The US Census geodemographic data are based on the 2016 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

US Census American Community Survey (ACS) 2021, 5-year estimates of the key economic characteristics of Public Use Microdata Areas (PUMA) geographic level in Orange County, California. The data contains 397 fields for the variable groups E01: Employment status (universe: population 16 years and over, table X23, 7 fields); E02: Work status by age of worker (universe: population 16 years and over, table X23, 36 fields); E03: Commuting to work (universe: workers 16 years and over, table X8, 8 fields); E04: Travel time to work (universe: workers 16 years and over who did not work at home, table X8, 14 fields); E05: Number of vehicles available for workers (universe: workers 16 years and over in households, table X8, 8 fields); E06: Median age by means of transportation to work (universe: median age, workers 16 years and over, table X8, 7 fields); E07: Means of transportation to work by race (universe: workers 16 years and over, table X8, 64 fields); E08: Occupation (universe: civilian employed population 16 years and over, table X24, 53 fields); E09: Industry (universe: civilian employed population 16 years and over, table X24, 43 fields); E10: Class of worker (universe: civilian employed population 16 years and over, table X24, 19 fields); E11: Household income and earnings in the past 12 months (universe: total households, table X19, 37 fields); E12: Income and earnings in dollars (universe: inflation-adjusted dollars, tables X19-X20, 31 fields); E13: Family income in dollars (universe: total families, table X19, 17 fields); E14: Health insurance coverage (universe: total families, table X19, 17 fields); E15: Ratio of income to Poverty level (universe: total population for whom Poverty level is determined, table X17, 8 fields); E16: Poverty in population in the past 12 months (universe: total population for whom Poverty level is determined, table X17, 7 fields); E17: Poverty in households in the past 12 months (universe: total households, table X17, 9 fields); E18: Percentage of families and people whose income in the past 12 months is below the poverty level (universe: families, population, table X17, 8 fields), and; X19: Poverty and income deficit (dollars) in the past 12 months for families (universe: families with income below Poverty level in the past 12 months, table X17, 4 fields). The US Census geodemographic data are based on the 2021 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project GitHub page (https://github.com/ktalexan/OCACS-Geodemographics).

Graph and download economic data for Employed Involuntary Part-Time for California (INVOLPTEMPCA) from Q4 2003 to Q3 2024 about part-time, labor underutilization, 16 years +, labor force, labor, CA, household survey, employment, and USA.

Aim: Land use change, climate change, and shifts to disturbance regimes make successful wildlife management challenging, particularly when ongoing urbanization constrains habitat and movement. Preserving and maintaining landscape connectivity is a potential strategy to support wildlife responding to these stressors. Using a novel model framework, we determined the population-level benefit of a set of identified potential corridors for spotted owl population viability. Location: Southern California, United States. Methods: Combining habitat suitability and dynamic metapopulation models, we compared the benefit of corridors to the Southern California spotted owl population, measured as the increase in the expected minimum abundance, both now and under a future climate. Our approach considered key corridor characteristics important to conservation decisions, namely, corridor irreplaceability and local population network benefit. Results: We identified two corridors likely to increase Southern California spotted owl expected minimum abundance under current climate conditions. At the regional scale, of the 16 corridors evaluated, one corridor was irreplaceable (i.e. no other corridors in the network could provide a similar increase in abundance when the irreplaceable corridor was removed) and one corridor was identified as redundant (i.e. remaining corridors in the network can provide some of the increases in abundance offered by the removed corridor). Both putative corridors connected two large, populous, and similarly-sized patches. Additionally, we identified two more corridors at the local scale. We found that, under climate change, population declines may limit the benefit of connectivity for a range-restricted species like the spotted owl. Main Conclusions: Our analytical approach highlights important criteria for corridor identification and prioritization, namely, irreplaceability versus redundancy, local versus regional benefit, and corridor impact in a changing landscape. With the capability of incorporating estimated functional connectivity into population dynamics, our modeling framework advances connectivity decision making for other species of conservation concern and archetypal taxa within ecological communities.