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.

VITAL SIGNS INDICATOR Migration (EQ4)

FULL MEASURE NAME Migration flows

LAST UPDATED December 2018

DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables.

DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration.

Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23)

One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.

These datasets focus on patients leaving California hospitals against medical advice (AMA), which is defined as choosing to leave the hospital before the treating physician recommends discharge. Patients leaving AMA are exposed to higher risks due to inadequately treated medical issues, which may result in the need for readmission.

This dataset contains counts of live births for California as a whole based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Use data-dictionary-deaths-by-county.csv for detailed column descriptions

Terms of Use can be found here

Acknowlegement

Foto von Sandy Millar auf Unsplash

DEC. 22, 2022 – After a historically low rate of change between 2020 and 2021, the U.S. resident population increased by 0.4%, or 1,256,003, to 333,287,557 in 2022, according to the U.S. Census Bureau’s Vintage 2022 national and state population estimates and components of change released today.

Net international migration — the number of people moving in and out of the country — added 1,010,923 people between 2021 and 2022 and was the primary driver of growth. This represents 168.8% growth over 2021 totals of 376,029 – an indication that migration patterns are returning to pre-pandemic levels. Positive natural change (births minus deaths) increased the population by 245,080.

“There was a sizeable uptick in population growth last year compared to the prior year’s historically low increase,” said Kristie Wilder, a demographer in the Population Division at the Census Bureau. “A rebound in net international migration, coupled with the largest year-over-year increase in total births since 2007, is behind this increase.”

Regional Patterns The South, the most populous region with a resident population of 128,716,192, was the fastest-growing and the largest-gaining region last year, increasing by 1.1%, or 1,370,163. Positive net domestic migration (867,935) and net international migration (414,740) were the components with the largest contributions to this growth, adding a combined 1,282,675 residents.

The West was the only other region to experience growth in 2022, having gained 153,601 residents — an annual increase of 0.2% for a total resident population of 78,743,364 — despite losing 233,150 residents via net domestic migration (the difference between residents moving in and out of an area). Natural increase (154,405) largely accounted for the growth in the West.

The Northeast, with a population of 57,040,406, and the Midwest, with a population of 68,787,595, lost 218,851 (-0.4%) and 48,910 (-0.1%) residents, respectively. The declines in these regions were due to negative net domestic migration.

Changes in State Population Increasing by 470,708 people since July 2021, Texas was the largest-gaining state in the nation, reaching a total population of 30,029,572. By crossing the 30-million-population threshold this past year, Texas joins California as the only states with a resident population above 30 million. Growth in Texas last year was fueled by gains from all three components: net domestic migration (230,961), net international migration (118,614), and natural increase (118,159).

Florida was the fastest-growing state in 2022, with an annual population increase of 1.9%, resulting in a total resident population of 22,244,823.

“While Florida has often been among the largest-gaining states,” Wilder noted, “this was the first time since 1957 that Florida has been the state with the largest percent increase in population.”

It was also the second largest-gaining state behind Texas, with an increase of 416,754 residents. Net migration was the largest contributing component of change to Florida’s growth, adding 444,484 residents. New York had the largest annual numeric and percent population decline, decreasing by 180,341 (-0.9%). Net domestic migration (-299,557) was the largest contributing component to the state’s population decline.

Eighteen states experienced a population decline in 2022, compared to 15 and DC the prior year. California, with a population of 39,029,342, and Illinois, with a population of 12,582,032, also had six-figure decreases in resident population. Both states’ declining populations were largely due to net domestic outmigration, totaling 343,230 and 141,656, respectively.

Puerto Rico Population Changes In 2022, Puerto Rico’s population was 3,221,789. This reflects a decrease of 1.3%, or 40,904 people, between 2021 and 2022.

Puerto Rico’s population decline resulted from negative net international migration (-26,447) and negative natural change (-14,457), where deaths outnumber births.

                            **###Components of Change for States**

In 2022, 24 states experienced negative natural change, or natural decrease. Florida had the highest natural decrease at -40,216, followed by Pennsylvania (-23,021) and Ohio (-19,543). In 2021, 25 states had natural decrease.

Of the 26 states and the District of Columbia where births outnumbered deaths, Texas (118,159), California (106,155) and New York (35,611) had the highest natural increase.

All 50 states and the District of Columbia saw positive net international migration with California (125,715), Florida (125,629) and Texas (118,614) having the largest gains.

The biggest gains from net domestic migration last year were in Florida (318,855), Texas (230,961) and North Carolina (99,796), while the biggest losses were in California (-343,230), New York (-299,557) and Illinois...

This dataset contains annual average CES data for California statewide and areas from 1990 to 2024. The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States. CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services. 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.

This dataset includes the number of service connections, categorized by type and metering status, as submitted by public water systems via the electronic annual report (eAR) in sections 3 or 4 for calendar years 2013 through 2023 reporting year. It also includes facility public information details from the SDWIS database as metadata associated to the dataset. “Service connection” means the point of connection between the customer’s piping or constructed conveyance, and the water system’s meter, service pipe, or constructed conveyance. “Public water system” means a system for the provision of water for human consumption through pipes or other constructed conveyances that has 15 or more service connections or regularly serves at least 25 individuals daily at least 60 days out of the year. Public water systems submit critical water system information intended to assess the status of compliance with specific regulatory requirements such as source water capacity, provides updated contact and inventory information (such as population and number of service connections) to the to the Division of Drinking Water using the electronic Annual Report (eAR) submission process. The data in the datasets below are electronically reported annually by public water systems to the State Water Resources Control Board - Division of Drinking Water. The data contained herein are public water system reported data and do not include a determination of accuracy or validity by regulatory staff. For data validated by regulatory staff, refer to the public water system inventory dataset from the Safe Drinking Water Information System (SDWIS), available at: https://data.ca.gov/dataset/drinking-water-public-water-system-information. For more information about the eAR, visit the eAR Home Page: https://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/ear.html

This dataset includes the number of eligible individuals selected and enrolled in a Covered California Silver Plans by reporting period. Covered California offers four levels of Silver Plans: Silver 70 that covers standard benefits, and enhanced Silver 73, Silver 84, and Silver 94 that offer enhanced out-of-pocket savings through lower copays, coinsurance and deductibles to qualified individuals. Covered California reported data is from the California Healthcare Eligibility, Enrollment and Retention System (CalHEERS) and includes eligible individuals who selected and enrolled in a QHP, and paid their first premium. This dataset is part of public reporting requirements set forth by the California Welfare and Institutions Code 14102.5.

The Rise of Home Schooling

Data from The Post's analysis of home-schooling enrollment across the US

This repository shares data hand-collected by The Washington Post from individual school districts and states as a whole regarding home-school enrollment from 2017-18 through 2022-23. The data is what is behind this story published on Oct. 31, 2023 by Peter Jamison, Laura Meckler, Prayag Gordy, Clara Ence Morse and Chris Alcantara.

There are two separate data files, both of which cover the same time period: - home_school_district.csv - home_school_state.csv

There is also a data dictionary explaining each file.

Methodology

To measure the growth of home schooling during the pandemic, The Washington Post collected home-school student counts from 6,738 school districts. Together with students from The Washington Post Investigative Reporting Workshop practicum at American University, reporters trawled state websites, contacted education officials in all 50 states and the District of Columbia and submitted multiple public records requests for an annual count of home-schoolers from the 2017-18 school year through 2022-23. The Post ultimately collected data for all public school districts in 29 states and D.C. In all, The Post gathered data from states representing 61% of the American school-age population.

Three states — Pennsylvania, Rhode Island and Tennessee — have not published the number of home-schoolers in 2022-23, and Maine only shared district-level data starting with the 2020-21 school year. In seven states, The Post was unable to obtain usable home-school enrollment figures: In Arizona, Nevada and Oregon, only new home-school registrations are tracked annually at the district level; in North Carolina, home-school registration rolls are not regularly purged as students age out of the system; and in West Virginia, Utah and Alabama, annual enrollment data is unavailable. Eleven additional states do not require any notice when families decide to home-school their children, so enrollment figures in those states are also unavailable. Finally, Montana, Vermont and Nebraska collect data at a county level, not a district level, so there is no district data available - only statewide figures.

The Post made every effort to capture all legal ways to home-school, which vary by state. However, data on home schools established by certain methods, such as registering one’s home-school as a private school, are tracked by some states but not others. That means The Post’s tally is almost certainly an undercount, even in the states from which it gathered data. For instance, Wisconsin and Georgia only provided The Post with tallies of home-schoolers who had submitted required forms electronically. In Kentucky, some districts incorrectly reported zero or one home-schooled students in certain years, which a state education official attributed to an unclear form. The Post excluded those enrollment figures from its analysis. In California, which does not explicitly permit home schooling, many parents operate home-based private schools. The California Department of Education characterizes private schools with five or fewer students as home schools. In Louisiana, many home schools operate as nonpublic schools not seeking accreditation; The Post counted such schools with five or fewer students as home schools as well.

The statewide numbers are not always equivalent to the sum of all district totals in a state. Some states suppress district-level counts of home schoolers below a certain threshold. In Maine, the threshold is 5; in New Mexico, 6; in Mississippi, Ohio and Tennessee, 10; in Wisconsin before 2020-21, 5; and in Wisconsin from 2021-22 on, 20. The Post marked such suppressions as NA within its data. In addition, New Hampshire collects separate data on students who enter home schooling from schools run by the state department of education or from private schools; these additional students are reflected in state data but not district data.

The Post used a variety of methods to match each school district name to an NCES district id. However, this was not always possible. In Georgia, families self-report their school district on home-schooling forms; some report programs which are not school districts, and therefore have no corresponding NCES id. In California, families were only required to report county and school district beginning in 2020-21; in addition, district mergers and name changes mean that some districts could not be matched wi...

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States.

CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services.

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.

SummaryThis INCOMPLETE dataset illustrates California's Granted public trust lands. Parcel digitization is currently UNDER DEVELOPMENT and approximately 61% complete (as of 9/17/2019). See below description for explanation of Granted public trust lands.CompleteIncompleteAlbany, City ofAlameda, City ofBerkeley, City ofOakland, City and PortEmeryville, City ofMartinez, City ofPeralta Junior College DistrictEast Bay Regional Park DistrictAntioch, City ofEureka, City ofPittsburg, City ofHumboldt Bay Harbor Recreation and Conservation DistrictRichmond, City ofLong Beach, City ofCrescent CityLos Angeles, City and PortCrescent City Harbor DistrictSanta Monica, City ofArcata, City ofMarin, County ofTrinidad, City ofSan Rafael, City ofAvalon, City ofCarmel Sanitary DistrictHermosa Beach, City ofMonterey, City ofManhattan Beach, City ofMoss Landing Harbor DistrictPalos Verdes Estates, City ofPacific GroveRedondo Beach, City ofOrange, County ofMill Valley, City ofNewport Beach, City ofSausalito, City ofCoronado, City ofNoyo Harbor DistrictSan Diego, City and CountyLaguna Beach , City ofSan Diego Unified Port DistrictMetropolitan Water DistrictSan Francisco, City and CountyOceanside, City ofSan Franscico Port DistrictUniversity of California, ScrippsSan Mateo, City ofStockton, City ofSouth San Francisco, City ofPort San Luis Harbor DistrictBrisbane, City ofMorro Bay, City ofSanta Barbara, City ofSan Mateo, County ofCarpinteria, City ofSan Mateo Harbor DistrictSanta Cruz, City ofRedwood CitySanta Cruz Port DistrictSanta Barbara, City ofVallejo, City ofSanta Cruz, County ofU.S. Navy Mare Island Capitola, City ofSacramento, City ofBenicia, City ofPoint Reyes National SeashoreCalifornia Maritime Academy Treasure Island Development AuthoritySonoma, County ofVentura (San Buenaventura), City ofLake, County ofAbstractCalifornia acquired all right, title, and interest in tide and submerged lands and beds of navigable waterways within its borders when it became a state in 1850. These lands are sovereign, not proprietary, and have specific requirements on their management and use. Unlike proprietary lands, the California Constitution, California law and the common law Public Trust Doctrine prohibit the sale or alienation of sovereign lands except in limited circumstances. All sovereign lands are held in trust for the benefit of the people of California.The Legislature has enacted more than 300 statutes granting sovereign public trust lands to over 80 local municipalities (referred to as grantees or trustees) to manage in trust for the people of California. The terms and conditions of trust grants vary and are governed by the specific granting statute(s), the Public Trust Doctrine, the California Constitution, and case law. The specific uses permitted in each granting statute vary. Some trust grants authorize the construction of ports, harbors, airports, wharves, docks, piers, slips, quays and other structures necessary to facilitate commerce and navigation, while others allow only visitor serving recreational uses or open space. All grants reserve to the people the right to fish in the waters over the lands and the right to convenient access to those waters for that purpose.Revenues generated by a trustee arising out of the use or operation of their granted lands are State trust assets and must be reinvested back into the trust. These revenues must be kept separate from the local entity’s general fund and may not be used for any municipal purpose, or any purpose unconnected with the trust. Expenditures of trust funds by a trustee must be consistent with the Public Trust Doctrine and the statutory trust grant.While granted public trust lands and assets are managed locally, the Legislature delegated the State's residual and review authority for granted lands to the Commission. The Commission represents the statewide public interest to ensure that trustees operate their grants in conformance with the California Constitution, applicable granting statutes, and the Public Trust Doctrine. Public Resources Code section 6301 provides, among other things, "all jurisdiction and authority remaining in the State as to tidelands and submerged lands as to which grants have been or may be made is vested in the commission." This oversight has ranged from working cooperatively to assist trustees on issues involving boundary determinations, trust consistency determinations and land exchanges, to judicial confrontations involving billions of dollars of trust assets.Data Quality Rating DescriptionPoor. Text in legislation references locations which can't be determined. Relies mostly or entirely on non-survey sources.Moderate. Includes survey-quality locations generally, but may be some ambiguity in boundaries at some locations.Excellent. All points are survey quality. Symbology DefinitionsActive: Currently active grantsActive, Uncertain Boundary: Currently active grant parcel whose boundary is approximated using all available data.Repealed: Former grant, since repealed and no longer active, with land reverting back to the control of the CA State Lands Commission.

This dataset provides long-term occupational employment projections for the state of California across various industries. It offers insights into job growth, industry trends, and workforce demand over a 10-year horizon.

Why is this dataset useful? 1. Job Market Analysis – Identify which jobs and industries are expected to grow or decline. Workforce Planning – Helps businesses, policymakers, and educators align training programs with future job demand. 2. Predictive Modeling – Use this dataset for time-series forecasting, job demand predictions, and labor market analytics.

Data Details: - Timeframe: 2022-2032 - Geography: State of California - Industries Covered: Technology, Healthcare, Retail, Manufacturing, Finance, and more.

Columns: 1. Area Type – Specifies the geographic classification (e.g., state-level or regional). 2. Area Name – The name of the geographic region (e.g., California, specific labor market regions). 3. Period – The timeframe of the projection, typically from the base year to the projected year (e.g., 2022-2032). 4. SOC Level – The level of the Standard Occupational Classification (SOC) system used for job categorization. 5. Standard Occupational Classification (SOC) – A unique code representing a specific occupation based on the SOC system. 6. Occupational Title – The official job title corresponding to the SOC code. 7. Base Year Employment Estimate – The estimated number of jobs for the occupation in the base year (e.g., 2022). 8. Projected Year Employment Estimate – The expected number of jobs for the occupation in the projected year (e.g., 2032). 9. Numeric Change – The absolute difference in employment between the base year and projected year. 10. Percentage Change – The percentage increase or decrease in employment over the projection period. 11. Exits – Estimated number of workers leaving the occupation due to retirement or career changes. 12. Transfers – Estimated number of workers transferring into or out of an occupation. 13. Total Job Openings – The sum of exits, transfers, and new job creation, representing the total expected openings. 14. Median Hourly Wage – The median hourly wage for the occupation. 15. Median Annual Wage – The median annual wage for the occupation. 16. Entry Level Education – The typical minimum education required for the occupation (e.g., high school diploma, bachelor's degree). 17. Work Experience – The amount of prior work experience typically needed for the occupation. 18. Job Training – The type of on-the-job training required for entry into the occupation.

Potential Use Cases: ✔ Career Guidance – Helps individuals choose high-growth career paths. ✔ Economic Research – Understand how employment trends impact the economy. ✔ Machine Learning Models – Build predictive models for workforce demand.

If you find this dataset useful, please upvote! Your support encourages more high-quality datasets.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

Coastal armoring structures are built extensively along California’s 1,271-mile coastline by private landowners, local, state, and federal governments to protect coastal development threatened by erosion. This dataset shows the locations of shore parallel coastal armoring structures Statewide. In 2005, NOAA Coastal Management Fellow, Jennifer Dare, developed a statewide coastal armoring and erosion GIS data layer for the California Coastal Commission (CCC) that contained polylines representing a variety of along-shore armoring structures. A combination of aerial images, oblique images from the California Coastal Records Project and georeferenced ortho-images were used to identify and map shoreline protective structures along the entire California coast. Building upon J.Dare's work, ESA PWA designed and constructed a comprehensive coastal armoring geodatabase for the California Coastal Commisison to help answer key management questions related to the impacts of armoring on the shoreline. The California Coastal Commission’s Coastal Armoring database was updated in 2018. During the 2018 effort, California Coastal Commission analyst E. Essoudry used 2013 oblique images from the California Coastal Records Project and 2016-2018 ESRI and Google orbital imagery to approximate location and structure extent. The current draft database is a comprehensive statewide geospatial inventory of coastal armoring structures that will assist coastal managers and planners in identifying where past and present projects are located, locations of potential future development projects, identify resources potentially impacted by a project, and perform analyses on the cumulative impacts of armoring projects. This dataset also allows users to analyze spatial relationships among coastal armoring practices, answer important coastal management questions related to coastal armoring, and aid in climate change adaptation planning. Each armor structure is identified by a unique identifier number in the attribute table (Structure ID). The Structure ID uses the county abbreviation with a five-digit number and links the Structure History table within the database to its geographic representation.Terms of Use(1) The State of California and the California Coastal Commission make no representations or warranties regarding the accuracy or completeness of this dataset or the data sources from which it was derived. Neither the State nor the Commission shall be liable under any circumstances for any direct, special, incidental, or consequential damages with respect to any claim by any user or third party on account of or arising from the use of this dataset or the data sources from which it was derived. The data included are representational, may be revised at any time in the future, and are not binding on the Coastal Commission. (2) This dataset is intended solely to be used for illustrative purposes of structures along the California coast. Information in this dataset is solely for consideration by federal, state, and local government agencies, organizations, and committees involved in the management and protection of coastal resources in California. The list of structures has not been accepted nor approved by any governmental agencies and, as such, should not be construed to represent policy for any agency.(3) The user of this data agrees to defend, indemnify, and hold harmless, the California Coastal Commission, its Commissioners, employees, and agents, from and against all claims and expenses, including attorneys’ fees, arising out of the use of this data by user. (4) The accuracy of the GIS dataset is dependent on the ability to identify structure types and materials from aerial imagery and field verification. Therefore, the polylines are intended to provide an approximate geographic extent of the structure and are for representational purposes only. (4) Many shoreline protective structures have a relatively small footprint, therefore it is difficult to identify them via aerial imagery. By comparing the location of the structure in the 2013 oblique images from the California Coastal Records Project with the location on the 2018 aerial photograph, an approximate location and structure extent was determined. It is important to note that the dataset is based on what was identified in the 2013 Coastal Records photographs and may not accurately represent the state of armoring on the coast at the present time. The structure locations dataset is based on the visual characteristics of the structures. Some structures are buried, concealed by vegetation, or are constructed to look like the natural color and texture of the surrounding bluffs. These structures may have been overlooked when the dataset was created (in 2005) and updated in 2018. Structures that are no longer present on the ground may be included in the dataset for purposes of recording a structure that has since been altered, deteriorated, removed or hidden in the visual imagery. (5) Inclusion of the following acknowledgement in any products derived from these data would be appreciated: "Prepared in part with geographic information created and provided by the California Coastal Commission, GIS/Mapping Unit."

Premise of the study: To survive climate change and habitat loss plants must rely on phenotypic changes to the environment, local adaptation, or migration. Understanding the drivers of intraspecific variation is critical to anticipate how plant species will respond to climate change and inform conservation decisions. Here we explore the extent of local adaptation and phenotypic plasticity in Heteromeles arbutifolia, toyon, a species endemic to the California Floristic Province. Methods: We collected leaves from 286 individuals across toyon’s range and used seeds from 37 individuals to establish experimental gardens in the northern and southern parts of toyon’s range. We measured leaf functional traits of the wild collected leaves, and functional and fitness traits of the offspring grown in the experimental gardens. We then investigated the relationships between traits and source environment. Key results: Most traits we investigated responded plastically to the environment, and some traits measured in young seedlings were influenced by maternal effects. We found strong evidence that variation in leaf margins is a result of local adaptation to variation in temperature and temperature range. However, the source environment was not related to fitness traits or survival in the experimental gardens. Conclusions: Our findings reiterate the adaptive role of toothed leaf margins in colder and more seasonally variable environments. Additionally, we provide evidence that fitness of toyon is not dependent on where they are sourced, and thus toyon can be sourced across its range for restoration purposes.

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Context

The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men. There are two distinct numbers regarding the pay gap: non-adjusted versus adjusted pay gap. The latter typically takes into account differences in hours worked, occupations were chosen, education, and job experience. In the United States, for example, the non-adjusted average female's annual salary is 79% of the average male salary, compared to 95% for the adjusted average salary.

The reasons link to legal, social, and economic factors, and extend beyond "equal pay for equal work".

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

This dataset aims to replicate the data used in the famous paper "The Gender Wage Gap: Extent, Trends, and Explanations", which provides new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during the 1980–2010 period.

Citation

fedesoriano. (January 2022). Gender Pay Gap Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset.

Content

There are 2 files in this dataset: a) the Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, and b) the Current Population Survey (CPS) to provide some additional US national data on the gender pay gap.

PSID variables:

NOTES: THE VARIABLES WITH fz ADDED TO THEIR NAME REFER TO EXPERIENCE WHERE WE HAVE FILLED IN SOME ZEROS IN THE MISSING PSID YEARS WITH DATA FROM THE RESPONDENTS’ ANSWERS TO QUESTIONS ABOUT JOBS WORKED ON DURING THESE MISSING YEARS. THE fz variables WERE USED IN THE REGRESSION ANALYSES THE VARIABLES WITH A predict PREFIX REFER TO THE COMPUTATION OF ACTUAL EXPERIENCE ACCUMULATED DURING THE YEARS IN WHICH THE PSID DID NOT SURVEY THE RESPONDENTS. THERE ARE MORE PREDICTED EXPERIENCE LEVELS THAT ARE NEEDED TO IMPUTE EXPERIENCE IN THE MISSING YEARS IN SOME CASES. NOTE THAT THE VARIABLES yrsexpf, yrsexpfsz, etc., INCLUDE THESE COMPUTATIONS, SO THAT IF YOU WANT TO USE FULL TIME OR PART TIME EXPERIENCE, YOU DON’T NEED TO ADD THESE PREDICT VARIABLES IN. THEY ARE INCLUDED IN THE DATA SET TO ILLUSTRATE THE RESULTS OF THE COMPUTATION PROCESS. THE VARIABLES WITH AN orig PREFIX ARE THE ORIGINAL PSID VARIABLES. THESE HAVE BEEN PROCESSED AND IN SOME CASES RENAMED FOR CONVENIENCE. THE hd SUFFIX MEANS THAT THE VARIABLE REFERS TO THE HEAD OF THE FAMILY, AND THE wf SUFFIX MEANS THAT IT REFERS TO THE WIFE OR FEMALE COHABITOR IF THERE IS ONE. AS SHOWN IN THE ACCOMPANYING REGRESSION PROGRAM, THESE orig VARIABLES AREN’T USED DIRECTLY IN THE REGRESSIONS. THERE ARE MORE OF THE ORIGINAL PSID VARIABLES, WHICH WERE USED TO CONSTRUCT THE VARIABLES USED IN THE REGRESSIONS. HD MEANS HEAD AND WF MEANS WIFE OR FEMALE COHABITOR.

1. intnum68: 1968 INTERVIEW NUMBER
2. pernum68: PERSON NUMBER 68
3. wave: Current Wave of the PSID
4. sex: gender SEX OF INDIVIDUAL (1=male, 2=female)
5. intnum: Wave-specific Interview Number
6. farminc: Farm Income
7. region: regLab Region of Current Interview
8. famwgt: this is the PSID’s family weight, which is used in all analyses
9. relhead: ER34103L this is the relation to the head of household (10=head; 20=legally married wife; 22=cohabiting partner)
10. age: Age
11. employed: ER34116L Whether or not employed or on temp leave (everyone gets a 1 for this variable, since our wage analyses use only the currently employed)
12. sch: schLbl Highest Year of Schooling
13. annhrs: Annual Hours Worked
14. annlabinc: Annual Labor Income
15. occ: 3 Digit Occupation 2000 codes
16. ind: 3 Digit Industry 2000 codes
17. white: White, nonhispanic dummy variable
18. black: Black, nonhispanic dummy variable
19. hisp: Hispanic dummy variable
20. othrace: Other Race dummy variable
21. degree: degreeLbl Agent's Degree Status (0=no college degree; 1=bachelor’s without advanced degree; 2=advanced degree)
22. degupd: degreeLbl Agent's Degree Status (Updated with 2009 values)
23. schupd: schLbl Schooling (updated years of schooling)
24. annwks: Annual Weeks Worked
25. unjob: unJobLbl Union Coverage dummy variable
26. usualhrwk: Usual Hrs Worked Per Week
27. labincbus: Labor Income from...

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

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