analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

About this dataset

Context

The dataset tabulates the Table Grove 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 Table Grove. The dataset can be utilized to understand the population distribution of Table Grove by age. For example, using this dataset, we can identify the largest age group in Table Grove.

Key observations

The largest age group in Table Grove, IL was for the group of age 20-24 years with a population of 29 (9.83%), according to the 2021 American Community Survey. At the same time, the smallest age group in Table Grove, IL was the 45-49 years with a population of 8 (2.71%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Table Grove is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Table Grove 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 Table Grove Population by Age. You can refer the same here

Abstract

The L2 Voter and Demographic data includes demographic and voter history tables for all 50 states and the District of Columbia. The dataset is built from publicly available government records about voter registration and election participation. These records indicate whether a person voted in an election or not, but they do not record whom that person voted for. Voter registration and election participation data are augmented by demographic information from outside data sources.

The L2 Voter and Demographic data is current as of February 06 2023.

Methodology

To create this file, L2 processes registered voter data on an ongoing basis for all 50 states and the District of Columbia, with refreshes of the underlying state voter data typically at least every six months and refreshes of telephone numbers and National Change of Address processing approximately every 30 to 60 days. These data are standardized and enhanced with propriety commercial data and modeling codes and consist of approximately 185,000,000 records nationwide.

Usage

For each state, there are two available tables: demographic and voter history. The demographic and voter tables can be joined on the 'LALVOTERID' variable. One can also use the 'LALVOTERID' variable to link the L2 Voter and Demographic data with the L2 Consumer Dataset.

The 'LALVOTERID' variable can also be used to validate the state. For example, let's look at the 'LALVOTERID' LALCA123456789. The characters in the fourth and fifth positions of this identifier are 'CA' (California). The second way to validate the state is by using the RESIDENCE_ADDRESSES_STATE, which should have a value of 'CA' (California).

The date appended to each table name represents when the data was last updated. These dates will differ state by state because states update their voter files at different cadences.

The demographic files use 695 consistent variables. For more information about these variables, see 2022-12-31-L2-Voter-Demographic-File-Layout.xlsx.

The voter history files have different variables depending on the state. The ***2023-02-06-L2-Voter-Data-Dictionaries.tar.gz ***file expands into a folder with .csv data dictionaries for each state's demographic and voter files. While the demographic file data dictionaries should complement the 2021-11-03-L2-Voter-Demographic-File-Layout.xlsx file, the voter file data dictionaries will be unique to each state.

***Experian_Mosaic_Guide.pdf *** is a codebook for two variables which appear in the demographic tables: 'CommercialData_MosaicZ4' and 'CommercialData_MosaicZ4Global.'

***L2_Data_Dictionary.pdf ***has information about where variables were sourced from.

***2022-12-31-National-File-Notes.pdf ***contains L2 Voter and Demographic Dataset ("National File") release notes from between 2018 and 2022.

***2022-06-03_Nationwide_Congressional_District_Redistricting_Analysis.xlsx ***summarizes the redistricting that has occurred following the 2020 census.

Bulk Data Access

Data access is required to view this section.

DataMapping Tool

Data access is required to view this section.

Demographic table of psychiatric brain samples.

About this dataset

Context

The dataset tabulates the Table Rock 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 Table Rock. The dataset can be utilized to understand the population distribution of Table Rock by age. For example, using this dataset, we can identify the largest age group in Table Rock.

Key observations

The largest age group in Table Rock, NE was for the group of age 35-39 years with a population of 49 (13.32%), according to the 2021 American Community Survey. At the same time, the smallest age group in Table Rock, NE was the 80-84 years with a population of 1 (0.27%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Table Rock is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Table Rock 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 Table Rock Population by Age. You can refer the same here

The following data set is information obtained about counties in the United States from 2010 through 2019 through the United States Census Bureau. Information described in the data includes the age distributions, the education levels, employment statistics, ethnicity percents, houseold information, income, and other miscellneous statistics. (Values are denoted as -1, if the data is not available)

Data Dictionary

This table displays the demographic information of the manuscript entitled "An investigation on the factors of community of inquiry on adolescents’ reading performance in the blended learning environment". Overall, 152,218 adolescents from 32 OECD countries who completed the optional ICT familiarity questionnaires in PISA 2018 were selected.

Objectives: Inborn error of immunity (IEI) comprises a broad group of inherited immunological disorders that usually display an overlap in many clinical manifestations challenging their diagnosis. The identification of disease-causing variants comprises the gold-standard approach to ascertain IEI diagnosis. The efforts to increase the availability of clinically relevant genomic data for these disorders constitute an important improvement in the study of rare genetic disorders. This work aims to make available whole-exome sequencing (WES) data of Brazilian patients' suspicion of IEI without a genetic diagnosis. We foresee a broad use of this dataset by the scientific community in order to provide a more accurate diagnosis of IEI disorders. Data description: Twenty singleton unrelated patients treated at four different hospitals in the state of Rio de Janeiro, Brazil were enrolled in our study. Half of the patients were male with mean ages of 9±3, while females were 12±10  years old. The WES was performed in the Illumina NextSeq platform with at least 90% of sequenced bases with a minimum of 30 reads depth. Each sample had an average of 20,274 variants, comprising 116 classified as rare pathogenic or likely pathogenic according to ACMG guidelines. The genotype-phenotype association was impaired by the lack of detailed clinical and laboratory information, besides the unavailability of molecular and functional studies which, comprise the limitations of this study. Overall, the access to clinical exome sequencing data is limited, challenging exploratory analyses and the understanding of genetic mechanisms underlying disorders. Therefore, by making these data available, we aim to increase the number of WES data from Brazilian samples despite contributing to the study of monogenic IEI-disorders.

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

These detailed tables show standard errors for sample sizes and population estimates from the 2012 National Survey on Drug Use and Health (NSDUH). Standard errors for samples sizes and population estimates are provided by age group, gender, race/ethnicity, education level, employment status, geographic area, pregnancy status, college enrollment status, and probation/parole status.

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

About this dataset

Context

The dataset tabulates the Table Rock 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 Table Rock. The dataset can be utilized to understand the population distribution of Table Rock by age. For example, using this dataset, we can identify the largest age group in Table Rock.

Key observations

The largest age group in Table Rock, NE was for the group of age 65 to 69 years years with a population of 56 (17.13%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Table Rock, NE was the 80 to 84 years years with a population of 1 (0.31%). 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 Table Rock is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Table Rock 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 Table Rock Population by Age. You can refer the same here

These summary metadata refer to the first results on the main demographic developments in the year of reference.

Member States send to Eurostat the first results on the main demographic developments in the year of reference (T), containing the total population figure on 31 December of year T (further published by Eurostat as Population on 1 January of year T+1), total births and total deaths during year T. This data collection is defined under http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32013R1260&from=EN" target="_blank">Regulation 1260/2013 on European demographic statistics. Countries may also transmit to Eurostat, on voluntary basis, provisional data on total immigration, emigration and net migration during the year (T).

Eurostat's data collection on the above figures is called DEMOBAL and it is carried out in June of each year. Eurostat publishes these first demographic estimates in July of each year in the online database, in the table Population change - Demographic balance and crude rates (demo_gind).

These first demographic estimates may either be confirmed or updated by Eurostat's demographic data collection taking place in December each year (called Unidemo), whereby countries submit detailed breakdowns (e.g. by age and sex) of their yearly population data, including data on migration, both at national and at regional level. The online table Population change - Demographic balance and crude rates (demo-gind) will be accordingly updated. This table includes the latest updates on total population, births and deaths reported by the countries, while the detailed breakdowns by various characteristics included in the rest of the tables of the Eurostat database (Demography domain and Migration, for example the Population by citizenship and by country of birth table) may be transmitted to Eurostat at a subsequent date.

The online table Population change - Demographic balance and crude rates (demo-gind) contains time series going back to 1960; data before 2013 were collected by Eurostat from the national statistical offices on voluntary basis.

The individual metadata files reported by the countries are attached to this metadata file.

These detailed tables show sample sizes and population estimates from the 2012 National Survey on Drug Use and Health (NSDUH). Samples sizes and population estimates are provided by age group, gender, race/ethnicity, education level, employment status, geographic area, pregnancy status, college enrollment status, and probation/parole status.

The table Population by County is part of the dataset Example Workflow Dataset, available at https://redivis.com/datasets/fgd4-ffrp1ze26. It contains 838 rows across 4 variables.

These detailed tables show standard errors sample sizes and population estimates pertaining to mental health from the 2010 National Survey on Drug Use and Health (NSDUH). Samples sizes and population estimates are provided by age group, gender, race/ethnicity, education level, employment status, poverty level, geographic area, insurance status.

Demographic data by diagnositic and genotype groups. N6, N12, and N24 indicate sample size of the 6-month, 12-month and 24-month follow up cohorts, respectively. The number of women in the samples is indicated in parentheses. Means are followed by standard deviations in parentheses for age and MMSE measures.

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

This feature service contains data from the American Community Survey: 5-year Estimates Data Profiles for the greater Bozeman, MT area. The attributes come from the Demographic and Housing Estimates table (DP05). Processing Notes:Data was downloaded from the U.S. Census Bureau and imported into FME to create an AGOL Feature Service. Each attribute has been given an abbreviated alias name derived from the American Community Survey (ACS) categorical descriptions. The Data Dictionary below includes all given ACS attribute name aliases.For Example: Pct_BAA is equal to the percentage of the population that identifies as Black or African AmericanData DictionaryACS_EST_YR: American Community Survey 5-Year Estimate Data Profile yearGEO_ID: Census Bureau geographic identifierNAME: Specified geographyPct: Percentage of the selected populationRace/Ethnicity:A: AsianAIAN: American Indian or Alaska NativeBAA: Black or African AmericanHL: Hispanic or LatinoNHPI: Native Hawaiian or other Pacific IslanderW: WhiteOther: Some other raceTwo: Two or more racesAge Group:15to24: Ages 15 to 24 years old25to34: Ages 25 to 34 years old35to44: Ages 35 to 44 years old45to54: Ages 45 to 54 years old55to64: Ages 55 to 64 years old65andover: Ages 65 and overGenderMale: Male identifyingFemale: Female identifyingDownload ACS Demographic Profile data for the greater Bozeman, MT areaAdditional LinksU.S. Census BureauU.S. Census Bureau American Community Survey (ACS)About the American Community Survey

Global AI Tool Adoption Across Industries and Regions (2023–2025)

A comprehensive, research-grade dataset capturing the adoption, usage, and impact of leading AI tools—such as ChatGPT, Midjourney, Stable Diffusion, Bard, and Claude—across multiple industries, countries, and user demographics. This dataset is designed for advanced analytics, machine learning, natural language processing, and business intelligence applications.

Dataset Overview

This dataset provides a panoramic view of how AI technologies are transforming business, industry, and society worldwide. Drawing inspiration from real-world adoption surveys, academic research, and industry reports, it enables users to:

• Analyze adoption rates of popular AI tools across regions and sectors.
• Study user demographics and company profiles influencing AI integration.
• Explore textual user feedback for sentiment and topic modeling.
• Perform time series analysis on AI adoption trends from 2023 to 2025.
• Benchmark industries, countries, and company sizes for AI readiness.

To add a column descriptor (column description) to your Kaggle dataset's Data Card, you should provide a clear and concise explanation for each column. This improves dataset usability and helps users understand your data structure, which is highly recommended for achieving a 10/10 usability score on Kaggle[2][9].

Below is a ready-to-copy Column Descriptions table for your dataset. You can paste this into the "Column Descriptions" section of your Kaggle Data Card (after clicking the pencil/edit icon in the Data tab)[2][9]:

Column Descriptions

Example Data

country,industry,ai_tool,adoption_rate,daily_active_users,year,user_feedback,age_group,company_size
USA,Technology,ChatGPT,78.5,5423,2024,"Great productivity boost for our team!",25-34,Enterprise
India,Healthcare,Midjourney,62.3,2345,2024,"Improved patient engagement and workflow.",35-44,SME
Germany,Manufacturing,Stable Diffusion,45.1,1842,2023,"Enhanced our design process.",45-54,Enterprise
Brazil,Retail,Bard,33.2,1200,2024,"Helped automate our customer support.",18-24,Startup
UK,Finance,Claude,55.7,2100,2023,"Increased accuracy in financial forecasting.",25-34,SME

How to Use This Dataset

1. Load and Preview the Data

import pandas as pd

df = pd.read_csv('/path/to/ai_adoption_dataset.csv')
print(df.head())
print(df.info())

2. Analyze Adoption Rates by Industry and Country

industry_adoption = df.groupby(['industry', 'country'])['adoption_rate'].mean().reset_index()
print(industry_adoption.sort_values(by='adoption_rate', ascending=False).head(10))

3. Visualize AI Tool Popularity

import matplotlib.pyplot as plt

tool_counts = df['ai_tool'].value_counts()
tool_counts.plot(kind='bar', title='AI Tool Usage Distribution')
plt.xlabel('AI Tool')
plt.ylabel('Number of Records')
plt.show()

4. Sentiment Analysis on User Feedback

from textblob import TextBlob

df['feedback_sentiment'] = df['user_feedback'].apply(lambda x: TextBlob(x).sentiment.polarity)
print(df[['user_feedback', 'feedback_sentiment']].head())

5. Time Series Analysis of Adoption Trends

yearly_trends = df.groupby(['year', 'ai_tool'])['adoption_rate'].mean().unstack()
yearly_trends.plot(marker='o', title='AI Tool Adoption Rate Over Time')
plt.xlabel('Year')
plt.ylabel('Average Adoption Rate (%)')
plt.show()

**6. Demographic Insights*...