Website alows the public full access to the 1940 Census images, census maps and descriptions.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

Abstract

The Integrated Public Use Microdata Series (IPUMS) Complete Count Data include more than 650 million individual-level and 7.5 million household-level records. The IPUMS microdata are the result of collaboration between IPUMS and the nation’s two largest genealogical organizations—Ancestry.com and FamilySearch—and provides the largest and richest source of individual level and household data.

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Documentation

Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier.

In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.Historic data are scarce and often only exists in aggregate tables. The key advantage of historic US census data is the availability of individual and household level characteristics that researchers can tabulate in ways that benefits their specific research questions. The data contain demographic variables, economic variables, migration variables and family variables. Within households, it is possible to create relational data as all relations between household members are known. For example, having data on the mother and her children in a household enables researchers to calculate the mother’s age at birth. Another advantage of the Complete Count data is the possibility to follow individuals over time using a historical identifier. In sum: the historic US census data are a unique source for research on social and economic change and can provide population health researchers with information about social and economic determinants.

The historic US 1940 census data was collected in April 1940. Enumerators collected data traveling to households and counting the residents who regularly slept at the household. Individuals lacking permanent housing were counted as residents of the place where they were when the data was collected. Household members absent on the day of data collected were either listed to the household with the help of other household members or were scheduled for the last census subdivision.

Notes

• We provide IPUMS household and person data separately so that it is convenient to explore the descriptive statistics on each level. In order to obtain a full dataset, merge the household and person on the variables SERIAL and SERIALP. In order to create a longitudinal dataset, merge datasets on the variable HISTID.
• Households with more than 60 people in the original data were broken up for processing purposes. Every person in the large households are considered to be in their own household. The original large households can be identified using the variable SPLIT40, reconstructed using the variable SERIAL40, and the original count is found in the variable NUMPREC40.
• Some variables are missing from this data set for specific enumeration districts. The enumeration districts with missing data can be identified using the variable EDMISS. These variables will be added in a future release.
• Coded variables derived from string variables are still in progress. These variables include: occupation, industry and migration status.
• Missing observations have been allocated and some inconsistencies have been edited for the following variables: Missing observations have been allocated and some inconsistencies have been edited for the following variables: SURSIM, SEX, SCHOOL, RELATE, RACE, OCC1950, MTONGUE, MBPL, FBPL, BPL, MARST, EMPSTAT, CITIZEN, OWNERSHP. The flag variables indicating an allocated observation for the associated variables can be included in your extract by clicking the ‘Select data quality flags’ box on the extract summary page.
• Most inconsistent information was not edited for this release, thus there are observations outside of the universe for many variables. In particular, the variables GQ, and GQTYPE have known inconsistencies and will be improved with the next r

The 1950 Census population schedules were created by the Bureau of the Census in an attempt to enumerate every person living in the United States on April 1, 1950, although some persons were missed. The 1950 census population schedules were digitized by the National Archives and Records Administration (NARA) and released publicly on April 1, 2022. The 1950 Census enumeration district maps contain maps of counties, cities, and other minor civil divisions that show enumeration districts, census tracts, and related boundaries and numbers used for each census. The coverage is nation wide and includes territorial areas. The 1950 Census enumeration district descriptions contain written descriptions of census districts, subdivisions, and enumeration districts.

The Census Tree is the largest-ever database of record links among the historical U.S. censuses, with over 700 million links for people living in the United States between 1850 and 1940. These links allow researchers to construct a longitudinal dataset that is highly representative of the population, and that includes women, Black Americans, and other under-represented populations at unprecedented rates. This project contains the files necessary to closely replicate the links between the 1900 and 1910 censuses. For more information, consult the included Read Me file, and visit https://censustree.org.

This dataset includes variable names, variable labels, variable values, and corresponding variable value labels for the IPUMS 1920 datasets.

This dataset includes all individuals from the 1940 US census.

An Act for ascertaining the Number of the Inhabitants of the Colony of New South Wales in the Year One thousand eight hundred and forty-one, 1840 (4 Victoria Act No. 26) required every householder, employer of servants and proprietor and occupier of land to complete the census schedule on the second day ('or on the days immediately subsequent thereto') of March 1841.

The 1841 Census was more complete than its predecessors, as the population was recorded in police districts, counties and towns. There was a broader tabulation of results which included age groups, conjugal condition (married or unmarried), religious denomination and civil condition. Civil condition provided statistical information on the number of bond (convict) or free males and females in a household, whether they were born in the colony, arrived free, held a ticket of leave, and whether they were in government employment or private assignment.

The Census was taken by specially appointed collectors generally responsible to a Commissioner or a Bench of Magistrates, the collector completed printed forms, known as Form ‘A’ for each household in the allotted territory. After the Census magistrates were instructed to check the returns and send abstracts to the Colonial Secretary, designated Form ‘C’. The returns were then gathered together, statistics extracted and the final returns made.

This series comprises bound volumes of Form C . (NRS 1281).

The Form C records: number of return, name of establishment (usually head of household), number of each age group for males, and then for females (the age divisions are under two, two and under seven, seven and under 14, 14 and under 21, 21 and under 45, 45 and under 60, 60 and upwards); married or single; civil condition: free (born in colony, arrived free, other free persons), bond (ticket of leave, in government employment, in private assignment); then religion divided into Church of England, Church of Scotland, Wesleyan Methodists, other Protestant dissenters, Roman Catholics, Jews, Mohammedans and Pagans; occupation divided into land proprietors, merchants, bankers, and professional men; shopkeepers and other retail dealers; mechanics and artificers; shepherds and others in the care of sheep; gardeners, stockmen and persons employed in agriculture; domestic servants; all other persons not included in the foregoing classes; totals for males, for females, and for both; houses - further divided into stone or brick, wood, total; finished or unfinished; inhabited or uninhabited. The columns are totalled at the bottom of each sheet.

As well as these Abstracts of returns, there are also a number of "condensed" abstracts of returns, filled in on Form C. These enumerate the running numbers covered by each sheet of abstracts eg. one-20, 21-40 and give sums for each group as well as grand total.

Form C abstracts are arranged by district following the order in the Returns of the Colony for 1841. 'Condensed' Abstracts are filed with the district abstracts to which they pertain.

Berrima-Port Phillip (X946-49)
Queanbeyan-Yass (X950-51)

References
1) State Records New South Wales Website, "Concise Guide to the State Archives (Ca - Commissioners): Colonial Secretary, later Chief Secretary, later Services; s. Population and Statistics, a. Musters and Census Records, ii. Census,23. 1841 Census: Abstracts of returns, CGS 1282."
2) State Records New South Wales Website, "Introduction to the 1841 Census: Index to the 1841 Census, Background".
3) State Records New South Wales Website, "Short Guide 12 - Muster and Census Records, 1788 - 1901".

This file contains the National Statistics Postcode Lookup (NSPL) for the United Kingdom as at February 2023 in Comma Separated Variable (CSV) and ASCII text (TXT) formats. To download the zip file click the Download button. The NSPL relates both current and terminated postcodes to a range of current statutory geographies via ‘best-fit’ allocation from the 2021 Census Output Areas (national parks and Workplace Zones are exempt from ‘best-fit’ and use ‘exact-fit’ allocations) for England and Wales. Scotland and Northern Ireland has the 2011 Census Output AreasIt supports the production of area based statistics from postcoded data. The NSPL is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The NSPL is issued quarterly. (File size - 188 MB).

Archive of 1971 census aggregate data for England, Wales and Scotland, as made available originally on the Casweb (https://casweb.ukdataservice.ac.uk) platform.

This dataset includes all households from the 1940 US census.

What you get:

Upvote! The database contains +40,000 records on US Gross Rent & Geo Locations. The field description of the database is documented in the attached pdf file. To access, all 325,272 records on a scale roughly equivalent to a neighborhood (census tract) see link below and make sure to upvote. Upvote right now, please. Enjoy!

Get the full free database with coupon code: FreeDatabase, See directions at the bottom of the description... And make sure to upvote :) coupon ends at 2:00 pm 8-23-2017

Gross Rent & Geographic Statistics:

• Mean Gross Rent (double)
• Median Gross Rent (double)
• Standard Deviation of Gross Rent (double)
• Number of Samples (double)
• Square area of land at location (double)
• Square area of water at location (double)

Geographic Location:

• Longitude (double)
• Latitude (double)
• State Name (character)
• State abbreviated (character)
• State_Code (character)
• County Name (character)
• City Name (character)
• Name of city, town, village or CPD (character)
• Primary, Defines if the location is a track and block group.
• Zip Code (character)
• Area Code (character)

Abstract

The data set originally developed for real estate and business investment research. Income is a vital element when determining both quality and socioeconomic features of a given geographic location. The following data was derived from over +36,000 files and covers 348,893 location records.

License

Only proper citing is required please see the documentation for details. Have Fun!!!

Golden Oak Research Group, LLC. “U.S. Income Database Kaggle”. Publication: 5, August 2017. Accessed, day, month year.

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965

please note: it is my personal number and email is preferred

Check our data's accuracy: Census Fact Checker

Access all 325,272 location for Free Database Coupon Code:

Don't settle. Go big and win big. Optimize your potential**. Access all gross rent records and more on a scale roughly equivalent to a neighborhood, see link below:

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A small startup with big dreams, giving the every day, up and coming data scientist professional grade data at affordable prices It's what we do.

The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about our nation and its people by contacting over 3.5 million households across the country. The resulting data provides incredibly detailed demographic information across the US aggregated at various geographic levels which helps determine how more than $675 billion in federal and state funding are distributed each year. Businesses use ACS data to inform strategic decision-making. ACS data can be used as a component of market research, provide information about concentrations of potential employees with a specific education or occupation, and which communities could be good places to build offices or facilities. For example, someone scouting a new location for an assisted-living center might look for an area with a large proportion of seniors and a large proportion of people employed in nursing occupations. Through the ACS, we know more about jobs and occupations, educational attainment, veterans, whether people own or rent their homes, and other topics. Public officials, planners, and entrepreneurs use this information to assess the past and plan the future. For more information, see the Census Bureau's ACS Information Guide . This public dataset is hosted in Google BigQuery as part of the Google Cloud Public Datasets Program , with Carto providing cleaning and onboarding support. It is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

This file contains the National Statistics Postcode Lookup (NSPL) for the United Kingdom as at August 2022 in Comma Separated Variable (CSV) and ASCII text (TXT) formats. To download the zip file click the Download button. The NSPL relates both current and terminated postcodes to a range of current statutory geographies via ‘best-fit’ allocation from the 2021 Census Output Areas (national parks and Workplace Zones are exempt from ‘best-fit’ and use ‘exact-fit’ allocations) for England and Wales. Scotland and Northern Ireland has the 2011 Census Output AreasIt supports the production of area based statistics from postcoded data. The NSPL is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The NSPL is issued quarterly. (File size - 184 MB).

Registration information on interstate, intrastate non-hazmat, and intrastate truck and bus companies that operate in the United States and have registered with FMCSA. Contains contact information and demographic information (number of drivers, vehicles, commodities carried, etc).

Abstract

For 156 years (1840 - 1996), the U.S. Department of Commerce, Bureau of the Census was responsible for collecting census of agriculture data. The 1997 Appropriations Act contained a provision that transferred the responsibility for the census of agriculture from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture is the 27th Federal census of agriculture and the third conducted by NASS. The first agriculture census was taken in 1840 as part of the sixth decennial census of population. The agriculture census continued to be taken as part of the decennial census through 1950. A separate middecade census of agriculture was conducted in 1925, 1935, and 1945. From 1954 to 1974, the census was taken for the years ending in 4 and 9. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data reference year so that it coincided with other economic censuses. This adjustment in timing established the agriculture census on a 5-year cycle collecting data for years ending in 2 and 7. Agriculture census data are used to:

• Evaluate, change, promote, and formulate farm and rural policies and programs that help agricultural producers; • Study historical trends, assess current conditions, and plan for the future; • Formulate market strategies, provide more efficient production and distribution systems, and locate facilities for agricultural communities; • Make energy projections and forecast needs for agricultural producers and their communities; • Develop new and improved methods to increase agricultural production and profitability; • Allocate local and national funds for farm programs, e.g. extension service projects, agricultural research, soil conservation programs, and land-grant colleges and universities; • Plan for operations during drought and emergency outbreaks of diseases or infestations of pests. • Analyze and report on the current state of food, fuel, feed, and fiber production in the United States.

American Samoa is one of the territories collectively referred as the "US Outlying areas". The 2008 American Samoa Census of Agriculture was conducted by personal interviews of all farm operations on the list of commercial farms, and supplemented by an area sample of the remaining households. The purpose of the area sample was to efficiently accountfor farms not on the commercialfarmlist and provide an accurate measure of the agricultural activity in American Samoa.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the CA 2008 was the farm, an operating unit defined as any place from which USD 1 000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The classical approach was used in the CA 2008.

ii. sample design The design of the sample for the 2008 Census of Agriculture made use of materials and information available from the American Samoa Department of Commerce. These included detailed maps of all the islands in the territory, up-to-date map-spotting (location on a map) of all households in the territory, a system of numbering each household to provide it a unique identifier, and identification of householdswhich were on the list of commercial farms. The households that were on the list of commercial farms were excluded from the universe used to select the area sample. A random sample of the remaining households was selected, using the available maps with the household identification information. It was determined that a 20 percent sample would be optimal. A serpentine selection methodology, starting at a point determined by the generation of a random number, was used to select the area sample.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

One questionnaire was used which collected information on:

• Land owned
• Field crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Aquaculture
• Expenditure
• Production expenses
• Machinery, equipment and buildings
• Household characteristics

Cleaning operations

1. DATA PROCESSING AND ARCHIVING The completed forms were scanned and Optical Mark Recognition (OMR) was used to retrieve categorical responses and to identify the other answer zones in which some type of mark was present. The edit system determined the best value to impute for reported responses that were deemed unreasonable and for required responses that were absent. The complex edit ensured the full internal consistency of the record. After tabulation and review of the aggregates, a comprehensive disclosure review was conducted. Cell suppression was used to protect the cells that were determined to be sensitive to a disclosure of information.

2. CENSUS DATA QUALITY NASS conducted an extensive program to follow-up all non-response. NASS also used capture-recapture methodology to adjust for under-coverage, non-response, and misclassification. To implement capture-recapture methods, two independent surveys were required --the 2012 Census of Agriculture (based on the Census Mail List) and the 2012 June Agricultural Survey (based on the area frame). Historically, NASS has been careful to maintain the independence of these two surveys.

Data appraisal

The complete data series from the 2008 Census of Agriculture is available from the NASS website free of charge in multiple formats, including Quick Stats 2.0 - an online database to retrieve customized tables with Census data at the national, state and county levels. The 2012 Census of Agriculture provides information on a range of topics, including agricultural practices, conservation, organic production, as well as traditional and specialty crops.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. The Census Bureau uses tabulation blocks as the basis for defining each ZCTA. Tabulation blocks are assigned to a ZCTA based on the most frequently occurring ZIP Code for the addresses contained within that block. The most frequently occurring ZIP Code also becomes the five-digit numeric code of the ZCTA. These codes may contain leading zeros. Blocks that do not contain addresses but are surrounded by a single ZCTA (enclaves) are assigned to the surrounding ZCTA. Because the Census Bureau only uses the most frequently occurring ZIP Code to assign blocks, a ZCTA may not exist for every USPS ZIP Code. Some ZIP Codes may not have a matching ZCTA because too few addresses were associated with the specific ZIP Code or the ZIP Code was not the most frequently occurring ZIP Code within any of the blocks where it exists. The ZCTA boundaries in this release are those delineated following the 2020 Census.

The Public Libraries Survey (PLS) is an annual survey of U.S. public libraries. Policymakers and practitioners depend on PLS data to allocate funding and strategically manage libraries. Academics rely on PLS data to conduct original research about public libraries. Data in the PLS come from over 17,000 outlets, and it represents a "gold standard" for national information about public libraries. While the PLS is an invaluable resource for the public library community, other organizations collect data that extends the reach and significance of the PLS. This dataset extends the PLS using information from the Public Library Association (PLA), the Association of Bookmobile and Outreach Services (ABOS), and the U.S. Census Bureau. PLA data comes from Project Outcome, a free toolkit and online resource for public libraries to document the outcomes associated with public library services. Since 2015, Project Outcome has collected more than 390,000 responses to surveys at 2,200+ libraries in the U.S. and Canada describing the outcomes resulting from library services. The standardized surveys used by Project Outcome have enabled libraries to aggregate their outcome data and analyze trends by topic, type, and program. ABOS data comes from a 2023 national, non-representative survey of public libraries regarding their outreach departments, services, and vehicles. Census data is from the American Community Survey and provides demographic information regarding the geographies that public libraries serve. As part of an Institute of Museum and Library Services grant, the Inter-university Consortium for Political and Social Research curated these data for reuse and mapped them to libraries in the PLS. The result is a combined dataset that documents the impact of library programming and outreach on nationwide communities. To enhance these data, a committee led by the University of Missouri, School of Information Science and Learning Technologies identified supplemental variables (e.g., Census demographic figures) and guided data curation by creating a "data module" specifying curation enhancements.