Context

The United States Census is a decennial census mandated by Article I, Section 2 of the United States Constitution, which states: "Representatives and direct Taxes shall be apportioned among the several States ... according to their respective Numbers."
Source: https://en.wikipedia.org/wiki/United_States_Census

Content

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole.

The United States census dataset includes nationwide population counts from the 2000 and 2010 censuses. Data is broken out by gender, age and location using zip code tabular areas (ZCTAs) and GEOIDs. ZCTAs are generalized representations of zip codes, and often, though not always, are the same as the zip code for an area. GEOIDs are numeric codes that uniquely identify all administrative, legal, and statistical geographic areas for which the Census Bureau tabulates data. GEOIDs are useful for correlating census data with other censuses and surveys.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:census_bureau_usa

https://cloud.google.com/bigquery/public-data/us-census

Dataset Source: United States Census Bureau

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Steve Richey from Unsplash.

Inspiration

What are the ten most populous zip codes in the US in the 2010 census?

What are the top 10 zip codes that experienced the greatest change in population between the 2000 and 2010 censuses?

https://cloud.google.com/bigquery/images/census-population-map.png" alt="https://cloud.google.com/bigquery/images/census-population-map.png"> https://cloud.google.com/bigquery/images/census-population-map.png

Context :

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole. Update frequency: Historic (none)

Dataset source

United States Census Bureau

Sample Query

SELECT zipcode, population FROM bigquery-public-data.census_bureau_usa.population_by_zip_2010 WHERE gender = '' ORDER BY population DESC LIMIT 10

Terms of use

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/us-census-data

Street tree data from the TreesCount! 2015 Street Tree Census, conducted by volunteers and staff organized by NYC Parks & Recreation and partner organizations. Tree data collected includes tree species, diameter and perception of health. Accompanying blockface data is available indicating status of data collection and data release citywide. The 2015 tree census was the third decadal street tree census and largest citizen science initiative in NYC Parks’ history. Data collection ran from May 2015 to October 2016 and the results of the census show that there are 666,134 trees planted along NYC's streets. The data collected as part of the census represents a snapshot in time of trees under NYC Parks' jurisdiction. The census data formed the basis of our operational database, the Forestry Management System (ForMS) which is used daily by our foresters and other staff for inventory and asset management: https://data.cityofnewyork.us/browse?sortBy=most_accessed&utf8=%E2%9C%93&Data-Collection_Data-Collection=Forestry+Management+System+%28ForMS%29 To learn more about the data collected and managed in ForMS, please refer to this user guide: https://docs.google.com/document/d/1PVPWFi-WExkG3rvnagQDoBbqfsGzxCKNmR6n678nUeU/edit. For information on the city's current tree population, use the ForMS datasets.

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

Pakistan Census 2017 Dataset

Description This dataset comprises data related to Pakistan's national census conducted in 2017. It includes final census results, population figures, household data, and administrative unit statistics. The census was carried out by the Pakistan Bureau of Statistics (PBS) between March 15, 2017, and May 25, 2017.

The dataset provides valuable insights into Pakistan's population, demographic characteristics, urbanization, ethnic diversity, religious composition, literacy, employment, housing, and more. It is a comprehensive resource for researchers, analysts, and data enthusiasts interested in exploring and analyzing various aspects of Pakistan's census.

Content The dataset includes the following files:

Pakistan_2017_Census_final.csv: Final census results at the tehsil (administrative unit) level.

The dataset was compiled from official releases by the Pakistan Bureau of Statistics (PBS) and is made available on Kaggle for wider access and analysis. The PBS conducted the census and provided the necessary data for this dataset.

Usage This dataset can be utilized for various purposes, including demographic research, socio-economic analysis, urban planning, policy formulation, and more. Researchers, data scientists, and analysts can explore and derive valuable insights from this dataset.

Update Frequency The dataset is based on the Pakistan Census 2017, and no further updates or revisions have been released beyond the final census results published in 2021.

License The dataset is made available under the Open Database License (ODbL). Users are encouraged to attribute the Pakistan Bureau of Statistics (PBS) as the source of the data when utilizing it for research or analysis.

Citywide street tree data from the 2005 Street Tree Census, conducted partly by volunteers organized by NYC Parks & Recreation. Trees were inventoried by address, and were collected from 2005-2006. Data collected includes tree species, diameter, condition.

The Economic Census is the U.S. Government's official five-year measure of American business and the economy. It is conducted by the U.S. Census Bureau, and response is required by law. In October through December of the census year, forms are sent out to nearly 4 million businesses, including large, medium and small companies representing all U.S. locations and industries. Respondents were asked to provide a range of operational and performance data for their companies. This dataset presents company, establishments, value of shipments, value of product shipments, percentage of product shipments of the total value of shipments, and percentage of distribution of value of product shipments.

The Agricultural Census (RA) is a comprehensive survey of all agricultural holdings, conducted every ten years. In particular, it makes it possible to know the utilised agricultural area (UAA) of each holding. In 2000, the RA included a question to break down this UAA between the 9 main municipalities where the land of the farm was actually located. In 2010, this issue was deleted. The entire area was therefore located in the municipality of the head office of the holding. It should be recalled that the head office of the holding is the farm body, the main building of the holding, or, failing that, the municipality where the holding has most of its parcels. This is not the registered office, and the place of business is therefore normally situated near at least part of the parcels of the holding. The Agricultural Census (RA) is a comprehensive survey of all agricultural holdings, conducted every ten years.In particular, it makes it possible to know the utilised agricultural area (UAA) of each holding. In 2000, the RA included a question to break down this UAA between the 9 main municipalities where the land of the farm was actually located. In 2010, this issue was deleted. The entire area was therefore located in the municipality of the head office of the holding.

It should be recalled that the head office of the holding is the farm body, the main building of the holding, or, failing that, the municipality where the holding has most of its parcels. This is not the registered office, and the place of business is therefore normally situated near at least part of the parcels of the holding.

The Agricultural Census (RA) is a comprehensive survey of all agricultural holdings, conducted every ten years. In particular, it makes it possible to know the utilised agricultural area (UAA) of each holding. In 2000, the RA included a question to break down this UAA between the 9 main municipalities where the land of the farm was actually located. In 2010, this issue was deleted. The entire area was therefore located in the municipality of the head office of the holding.

It should be recalled that the head office of the holding is the farm body, the main building of the holding, or, failing that, the municipality where the holding has most of its parcels. This is not the registered office, and the place of business is therefore normally situated near at least part of the parcels of the holding.

For matching and analyzing demographic data collected and compiled by the U.S. Census Bureau & American Community Survey(ACS) to the geography of Census Block Group boundaries within the City of Philadelphia. These boundaries can change every ten years when the decennial census is conducted. Adjusted to City's Standard Boundary Format.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Alton. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Alton, while the Census reported a median income of $57,917 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the city of Alton was not possible.

- Full-time workers, aged 15 years and older: In Alton, for full-time, year-round workers aged 15 years and older, while the Census reported a median income of $68,750 for males, while data for females was unavailable due to an insufficient number of sample observations.

As there was no available median income data for females, conducting a comprehensive assessment of gender-based pay disparity in Alton was not feasible.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Alton median household income by race. You can refer the same here

Description: Since 2009, the departmental directorate of the Marne territories has been carrying out a census of the land resources available within the main urban areas of the Marne. This census concerns land rights of way for known or probable brownfields of all types (industrial, railway, military, etc.). The area retained for brownfields is 2 000 m², threshold defined as potentially an issue. This census is not exhaustive. Census conducted until October 2018. Genealogy: A first census of brownfields was carried out from the databases of the Ministry of Ecology, Sustainable Development and Energy “BASIAS” and “Basol”, listing polluted sites. Visits by the territorial referents (RT) of the departmental directorate of the territories were then carried out on site and made it possible to complete the first census. The data is geolocated in the form of polygons using the parcelal cD (default of digitised cadastre). Metadata: ID: identification of wasteland by number ENTITE: current state (fridge or wasteland converted) CD_INSEE: INSEE code NOM_COM: name of the municipality DESCRIPTION: current state of land or built at census date ACTIV: past or new activity for brownfields ENTER: name of company and/or owner, who is or was installed ADDRESS: address of the wasteland PROJECT: project in progress at census date if known COMMENT: further information on the situation of the wasteland at the last date of its census. SOURCE: Source of information. Date_MAJ: date of update The geolocation data is in the form of a polygon.

The Census of Population and Housing is one of the most important surveys carried out by ISTAT. It is conducted every ten years from 1861, and the main objectives are: the count of the whole population and the recognition of its structural characteristics; updating and revision of civil registers; the definition of the legal population for juridical and electoral purposes; the collection of information about the number and structural characteristics of houses and buildings. The Census collects information about demographic and family structure of the population, the types of their households, their level of education, their employment status, and other informations on residents population. In 2011, for the first time, some information of socio-economic character were measured on a sample basis through the use of two types of questionnaire: one in a reduced form, with a few questions, including indispensable information for the production of the data required by the European Union with an high spatial detail, and one in complete form. The extended dataset is a supplement to the data of the 15th Population and Housing Census carried out by Istat in 2011. Compared to the data distributed by Istat, this version contains additional variables that report, for each census tracts of the Italian municipalities, information related to: - the professional position (number of employees classified through eight categories) - the housing supplies (heating, water, cooking, etc.) - disadvantaged family type (single parent, single parent with children under 15 and single person over 65) The dataset therefore allows to have more data than those released with the official census, useful in particular to carry out in-depth studies on the employment status, deprivation and poverty. 366,863 census tracts, 8,092 municipalities. In urban areas with at least 20,000 inhabitants a sample was selected by a simple random sampling without replacement procedure of one third of the families. A complete version (long form) of the questionnaire has been sent to the sample, while a short version the questionnaire has been sent to all other inhabitants.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Burdette. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Burdette, while the Census reported a median income of $33,000 for all female workers aged 15 years and older, data for males in the same category was unavailable due to an insufficient number of sample observations.

Because income data for males was not available from the Census Bureau, conducting a comprehensive analysis of gender-based pay disparity in the town of Burdette was not possible.

- Full-time workers, aged 15 years and older: In Burdette, for full-time, year-round workers aged 15 years and older, while the Census reported a median income of $44,583 for males, while data for females was unavailable due to an insufficient number of sample observations.

As there was no available median income data for females, conducting a comprehensive assessment of gender-based pay disparity in Burdette was not feasible.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Burdette median household income by race. You can refer the same here

The census of population and housing, taken by the Census Bureau in years ending in 0 (zero). Article I of the Constitution requires that a census be taken every ten years for the purpose of reapportioning the U.S. House of Representatives. Title 13 of the U. S. Code provides the authorization for conducting the census in Puerto Rico and the Island Areas. After each decennial census, the results are released to the public in a variety of ways, including publishing multiple series of reports titled Census of Population and Housing. The abbreviation for these reports was CPH for some decades (including 1990 and 2010) and PHC for some decades (including 1970 and 2000).

Description:

Since 2009, the departmental directorate of the Marne territories has been carrying out a census of the land resources available within the main urban areas of the Marne. This census concerns land rights of way corresponding on the one hand to known or probable brownfields of all types (industrial, railway, military, etc.) and on the other hand to unbuilt spaces surrounded by built plots (at least 2) qualified as hollow teeth. The area retained for hollow teeth since 2015 is 500 m² and that of brownfields 2 000 m² This census is not exhaustive. Census conducted until October 2014.

Genealogy:

A first census of brownfields was carried out from databases of the Ministry of Ecology, Sustainable Development and Energy “BASIAS” and “Basol”, listing polluted sites. Visits by the territorial referents (RT) of the departmental directorate of the territories were then carried out on site and made it possible to complete the first census.

According to the definition adopted by the DDT, the hollow teeth identified have an area greater than 400 m² (500 m² since 2015). Hollow teeth located in areas AU (area to be urbanised) and N (natural and forested) or enclaved with no service, were not retained. Land of more than 400 m² with an area U (urban area) of less than 400 m² is excluded. Visits by the territorial referents (RT) of the departmental directorate of the territories were then carried out on site and made it possible to complete the first census, which is not exhaustive.

Description:

Since 2009, the departmental directorate of the Marne territories has been carrying out a census of the land resources available within the main urban areas of the Marne. This census concerns land rights of way corresponding on the one hand to known or probable brownfields of all types (industrial, railway, military, etc.) and on the other hand to unbuilt spaces surrounded by built plots (at least 2) qualified as hollow teeth. The area retained for hollow teeth since 2015 is 500 m² and that of brownfields 2 000 m² This census is not exhaustive. Census conducted until October 2017.

Genealogy:

A first census of brownfields was carried out from databases of the Ministry of Ecology, Sustainable Development and Energy “BASIAS” and “Basol”, listing polluted sites. Visits by the territorial referents (RT) of the departmental directorate of the territories were then carried out on site and made it possible to complete the first census.

According to the definition adopted by the DDT, the hollow teeth identified have an area greater than 400 m² (500 m² since 2015). Hollow teeth located in areas AU (area to be urbanised) and N (natural and forested) or enclaved with no service, were not retained. Land of more than 400 m² with an area U (urban area) of less than 400 m² is excluded. Visits by the territorial referents (RT) of the departmental directorate of the territories were then carried out on site and made it possible to complete the first census, which is not exhaustive.

Investigator(s): Bureau of Justice Statistics The National Jail Census was conducted by the U.S. Census Bureau for the Bureau of Justice Statistics. Excluded from the census were federal- or state-administered facilities, including the combined jail-prison systems in Alaska, Connecticut, Delaware, Hawaii, Rhode Island, and Vermont. Data include jail population by reason being held, age (juvenile or adult) and sex, maximum sentence that can be served in the facility, available services, type of security available, facility capacity, age, construction and renovation of the facility, employment, and operating expenditures.Years Produced: Every 5 years

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Jeffersonville. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Jeffersonville, while the Census reported a median income of $34,063 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the village of Jeffersonville was not possible.

- Full-time workers, aged 15 years and older: In Jeffersonville, for full-time, year-round workers aged 15 years and older, the Census Bureau did not report the median income for both males and females due to an insufficient number of sample observations.

As income data for both males and females was unavailable, conducting a comprehensive analysis of gender-based pay disparity in the village of Jeffersonville was not possible.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Jeffersonville median household income by race. You can refer the same here

According to the official Statement: The first census of water bodies was conducted with the reference year 2017-18 across the country in 33 States/UTs except Daman & Diu, Dadra & Nagar Haveli and Lakshadweep. The objective of the 1st Census of Water Bodies is to have a comprehensive national database of all water bodies by collecting information on all important aspects of the water body including their type, condition, status of encroachments, use, storage capacity, status of filling up of storage etc. The census also took into account all types of uses of water bodies like irrigation, industry, pisciculture, domestic/ drinking, recreation, religious, groundwater recharge etc.

The contributors to this official data creation are Department of Water Resources, River Development & Ganga Rejuvenation.

The dataset is released under National Data Sharing and Accessibility Policy (NDSAP) India

Source - https://data.gov.in/catalog/first-census-water-bodies-data

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in St. Charles. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In St. Charles, while the Census reported a median income of $11,217 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the town of St. Charles was not possible.

- Full-time workers, aged 15 years and older: In St. Charles, for full-time, year-round workers aged 15 years and older, the Census Bureau did not report the median income for both males and females due to an insufficient number of sample observations.

As income data for both males and females was unavailable, conducting a comprehensive analysis of gender-based pay disparity in the town of St. Charles was not possible.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 St. Charles median household income by race. You can refer the same here