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.

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

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.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

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

This dataset includes all individuals 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:

• Website: Golden Oak Research make sure to upvote

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.

Summary data of fixed broadband coverage by geographic area. License and Attribution: Broadband data from FCC Form 477, and data from the U.S. Census Bureau that are presented on this site are offered free and not subject to copyright restriction. Data and content created by government employees within the scope of their employment are not subject to domestic copyright protection under 17 U.S.C. § 105. See, e.g., U.S. Government Works. While not required, when using content, data, documentation, code and related materials from fcc.gov or broadbandmap.fcc.gov in your own work, we ask that proper credit be given. Examples include: • Source data: FCC Form 477 • Map layer based on FCC Form 477 • Code data based on broadbandmap.fcc.gov The geography look ups are created from the US census shapefiles, which are in Global Coordinate System North American Datum of 1983 (GCS NAD83). The coordinates do not get reprojected during processing. The "centroid_lng", "centroid_lat" columns in the lookup table are the exact values from the US census shapefile (INTPTLON, INTPTLAT). The "bbox_arr" column is calculated from the bounding box/extent of the original geometry in the shapefile; no reprojection or transformations are done to the geometry.

This dataset includes all households from the 1940 US census.

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).

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information. This tool is called the Climate and Economic Justice Screening Tool. The tool uses datasets that are indicators of burdens in eight categories: climate change, energy, health, housing, legacy pollution, transportation, water and wastewater, and workforce development. The tool uses this information to identify communities that are experiencing these burdens. These are the communities that are disadvantaged because they are marginalized by underinvestment and overburdened by pollution. CEQ will update the tool, after reviewing public feedback, research, and the availability of new data. Version 2.0 Release update - Dec 20, 2024 New & Improved Added the low income burden to American Samoa, Guam, the Mariana Islands, and the U.S. Virgin Islands Tracts in these territories that are completely surrounded by disadvantaged tracts and exceed an adjusted low income threshold are now considered disadvantaged Additionally, census tracts in these four Territories are now considered disadvantaged if they meet the low income threshold only, because these Territories are not included in the nationally-consistent datasets on environmental and climate burdens used in the tool Updated the data in the workforce development category to the Census Decennial 2020 data for the U.S. territories of Guam, Virgin Islands, Northern Mariana Islands, and American Samoa Made improvements to the low income burden to better identify college students before they are excluded from that burden’s percentile Census tracts that were disadvantaged under version 1.0 continue to be considered as disadvantaged in version 2.0 Technical Fixes For tracts that have water boundaries, e.g. coastal or island tracts, the water boundaries are now excluded from the calculation to determine if a tract is 100% surrounded by disadvantaged census tracts User Interface Improvements Added the ability to search by census tract ID The basemap has been updated to use a free, open source map

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

About this dataset

Context

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

Key observations

The largest age group in Kansas City, MO was for the group of age 25 to 29 years years with a population of 45,678 (9.03%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Kansas City, MO was the 80 to 84 years years with a population of 7,502 (1.48%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Kansas City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Kansas City 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 Kansas City Population by Age. You can refer the same here

These are the full-resolution boundary zip code tabular areas (ZCTA), derived from the US Census Bureau's TIGER/Line Shapefiles. The dataset contains polygons that roughly approximate each of the USPS 5-digit zip codes. It is one of many geography datasets available in BigQuery through the Google Cloud Public Dataset Program to support geospatial analysis. You can find more information on the other datasets at the US Geographic Boundaries Marketplace page . Though they do not continuously cover all land and water areas in the United States, ZCTAs are a great way to visualize geospatial data in an understandable format with excellent spatial resolution. This dataset gives the area of land and water within each zip code, as well as the corresponding city and state for each zip code. This makes the dataset an excellent candidate for JOINs to support geospatial queries with BigQuery’s GIS capabilities. Note: BQ-GIS is in public beta, so your GCP project will need to be whitelisted to try out these queries. You can sign up to request access here . This public dataset is hosted in Google BigQuery and 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 .

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).

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.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of State Line by race. It includes the distribution of the Non-Hispanic population of State Line across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of State Line across relevant racial categories.

Key observations

With a zero Hispanic population, State Line is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 3 (100% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the State Line
• Population: The population of the racial category (for Non-Hispanic) in the State Line is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of State Line total Non-Hispanic 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 State Line Population by Race & Ethnicity. You can refer the same here

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).

Census tracts are small, relatively permanent geographic entities within counties (or the statistical equivalents of counties) delineated by a committee of local data users. Generally, census tracts have between 2,500 and 8,000 residents and boundaries that follow visible features. When first established, census tracts are to be as homogeneous as possible with respect to population characteristics, economic status, and living conditions. (www.census.gov)

Data presented here is an extract of data taken from the Annual School Meal Census in publicly funded schools in Scotland. The data shows the provision of school meals (including free school meals) for each school in the Glasgow local authority area. The dataset forms part of a time series and is available for the years 2003 through to 2014. Full datasets can be downloaded from The Scottish Government. The data is graduated to school level and data includes: the numbers on the school roll; counts of pupils entitled to free school meals; counts of pupils present on the day of the survey, counts of pupils taking a school meal (free or not) on the day of the survey; and counts of pupils taking a free school meal on the day of the survey. In order to protect the identity of pupils a * used in the dataset denotes the number of pupils is 4 or less (zero included) or where such a figure could be worked out. A * * used in the dataset denotes where the difference between the number of pupils on the register and pupils with FME is 4 or less. Some of the datasets also include information on breakfast clubs, the provision of fresh fruit and water and the anonymity of the free school meal application process. The School Meal Census is carried out annually. For individual dataset errata or qualifications users should consult the background data or notes of the individual datasets. Licence: None fsm2003-2014.zip - https://dataservices.open.glasgow.gov.uk/Download/Organisation/728522f0-86da-48c6-8f75-1649934eb8a4/Dataset/7a0f701f-b55f-463f-a748-d62d6adf9979/File/51a57ba3-cf7b-4738-90ff-81d48dac9820/Version/7e43a6c6-cff0-4be2-b6e4-cd2e9c2707a5

This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.