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.

"Website allows the public full access to the 1950 Census images, census maps and descriptions.

The 1950 Census Tract files were originally created by keypunching the data from the printed publications prepared by the Bureau of the Census. The work was done under the direction of Dr. Donald Bogue, whose wife, Elizabeth Mullen Bogue, completed much of the data work. Subsequently, the punchcards were converted to data files and transferred to the National Archive and Records Administration (NARA). ICPSR received copies of these files from NARA and converted the binary block-length records to ASCII format.

This metadata report documents tabular data sets consisting of items from the Census of Agriculture. These data are a subset of items from county-level data (including state totals) for the conterminous United States covering the census reporting years (every five years, with adjustments for 1978 and 1982) beginning with the 1950 Census of Agriculture and ending with the 2012 Census of Agriculture. Historical (1950-1997) data were extracted from digital files obtained through the Intra-university Consortium on Political and Social Research (ICPSR). More current (1997-2012) data were extracted from the National Agriculture Statistical Service (NASS) Census Query Tool for the census years of 1997, 2002, 2007, and 2012. Most census reports contain item values from the prior census for comparison. At times these values are updated or reweighted by the reporting agency; the Census Bureau prior to 1997 or NASS from 1997 on. Where available, the updated or reweighted data were used; otherwise, the original reported values were used. Changes in census item definitions and reporting as well as changes to county areas and names over the time span required a degree of manipulation on the data and county codes to make the data as comparable as possible over time. Not all of the census items are present for the entire 1950-2012 time span as certain items have been added since 1950 and when possible the items were derived from other items by subtracting or combining sub items. Specific changes and calculations are documented in the processing steps sections of this report. Other missing data occurs at the state and (or) county level due to census non-disclosure rules where small numbers of farms reporting an item have acres and (or) production values withheld to prevent identification of individual farms. In general, caution should be exercised when comparing current (2012) data with values reported in earlier censuses. While the 1974-2012 data are comparable, data prior to 1974 will have inflated farm counts and slightly inflated production amounts due to the differences in collection methods, primarily, the definition of a farm. Further discussion on comparability can be found the comparability section of the Supplemental Information element of this metadata report. Excluded from the tabular data are the District of Columbia, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the three county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. Data for independent cities of Virginia prior to 1959 have been included with their surrounding or adjacent county. Please refer to the Supplemental Information element for information on terminology, the Census of Agriculture, the Inter-university Consortium for Political and Social Research (ICPSR), table and variable structure, data comparability, all farms and economic class 1-5 farms, item calculations, increase of farms from 1974 to 1978, missing data and exclusion explanations, 1978 crop irregularities, pastureland irregularities, county alignment, definitions, and references. In addition to the metadata is an excel workbook (VariableKey.xlsx) with spreadsheets containing key spreadsheets for items and variables by category and a spreadsheet noting the presence or absence of entire variable data by year. Note: this dataset was updated on 2016-02-10 to populate omitted irrigation values for Miami-Dade County, Florida in 1997.

description: This polygon shapefile provides county or county-equivalent boundaries for the conterminous United States and was created specifically for use with the data tables published as Selected Items from the Census of Agriculture for the Conterminous United States, 1950-2012 (LaMotte, 2015). This data layer is a modified version of Historic Counties for the 2000 Census of Population and Housing produced by the National Historical Geographic Information System (NHGIS) project, which is identical to the U.S. Census Bureau TIGER/Line Census 2000 file, with the exception of added shorelines. Excluded from the CAO_STCOFIPS boundary layer are Broomfield County, Colorado, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the 3 county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. The census of agriculture was not taken in the District of Columbia for 1959, but available data indicate few if any farms in that area, the polygon was left in place to preserve the areas of the surrounding counties. Baltimore City, Maryland was combined with Baltimore County and the St. Louis City, Missouri, was combined with St. Louis County. La Paz County, Arizona was combined with Yuma County, Arizona and Cibola County, New Mexico was combined with Valencia County, New Mexico. Minor county border changes were at a level of precision beyond the scope of the data collection. A major objective of the census data tabulation is to maintain a reasonable degree of comparability of agricultural data from census to census. The tabular data collection is from 14 different censuses where definitions and data collection techniques may change over time and while the data are mostly comparable, a degree of caution should be exercised when using the data in analysis procedures. While the data are at a county-level resolution, a regional approach is more appropriate than a county-by-county analysis. The main purpose of this layer is to provide a base to generate a county raster for the allocation of agricultural census values to specific (agricultural) pixels. Vector format is provided so the raster pixel size can be user designated. References cited: LaMotte, A.E., 2015, Selected items from the Census of Agriculture at the county level for the conterminous United States, 1950-2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7H13016. National Historical Geographic Information System, Minnesota Population Center, 2004, Historic counties for the 2000 census of population and housing: Minneapolis, MN, University of Minnesota, accessed 03/18/2013 at http://nhgis.org; abstract: This polygon shapefile provides county or county-equivalent boundaries for the conterminous United States and was created specifically for use with the data tables published as Selected Items from the Census of Agriculture for the Conterminous United States, 1950-2012 (LaMotte, 2015). This data layer is a modified version of Historic Counties for the 2000 Census of Population and Housing produced by the National Historical Geographic Information System (NHGIS) project, which is identical to the U.S. Census Bureau TIGER/Line Census 2000 file, with the exception of added shorelines. Excluded from the CAO_STCOFIPS boundary layer are Broomfield County, Colorado, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the 3 county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. The census of agriculture was not taken in the District of Columbia for 1959, but available data indicate few if any farms in that area, the polygon was left in place to preserve the areas of the surrounding counties. Baltimore City, Maryland was combined with Baltimore County and the St. Louis City, Missouri, was combined with St. Louis County. La Paz County, Arizona was combined with Yuma County, Arizona and Cibola County, New Mexico was combined with Valencia County, New Mexico. Minor county border changes were at a level of precision beyond the scope of the data collection. A major objective of the census data tabulation is to maintain a reasonable degree of comparability of agricultural data from census to census. The tabular data collection is from 14 different censuses where definitions and data collection techniques may change over time and while the data are mostly comparable, a degree of caution should be exercised when using the data in analysis procedures. While the data are at a county-level resolution, a regional approach is more appropriate than a county-by-county analysis. The main purpose of this layer is to provide a base to generate a county raster for the allocation of agricultural census values to specific (agricultural) pixels. Vector format is provided so the raster pixel size can be user designated. References cited: LaMotte, A.E., 2015, Selected items from the Census of Agriculture at the county level for the conterminous United States, 1950-2012: U.S. Geological Survey data release, http://dx.doi.org/10.5066/F7H13016. National Historical Geographic Information System, Minnesota Population Center, 2004, Historic counties for the 2000 census of population and housing: Minneapolis, MN, University of Minnesota, accessed 03/18/2013 at http://nhgis.org

1950 Employment Census Data for Baltimore, Maryland. Refer to the 1950 codebook (codebook_1950.pdf) for more information. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Mexico Population: Census: 50 to 54 Yrs Old data was reported at 6,155.306 Person th in 2015. This records an increase from the previous number of 5,064.291 Person th for 2010. Mexico Population: Census: 50 to 54 Yrs Old data is updated yearly, averaging 2,896.049 Person th from Dec 1950 (Median) to 2015, with 9 observations. The data reached an all-time high of 6,155.306 Person th in 2015 and a record low of 828.126 Person th in 1950. Mexico Population: Census: 50 to 54 Yrs Old data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.G002: Population: Census.

Two novel datasets—French 19th-century and U.S. 1950 Census records—to demonstrate our approach.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

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/international-census-data

Establishments in Japan meeting the following conditions:　(1) Establishment primarily engaged in manufacturing and processing and primarily wholesaling those goods.　(2) Establishments primarily engaged in retail (except those engaged in the retail of food and drink) and primarily manufacturing and processing those goods.　(3) Establishments primarily manufacturing raw materials supplied to others and primarily wholesaling those goods.　(4) Households engaged in manufacturing or processing as by-employment, excluding the following: (a) those that do not conduct any manufacturing or processing and only retail those goods; (b) those that manufacture or process raw materials consigned to them by general consumers; and (c) farmers and fishermen manufacturing or processing raw materials grown, harvested, or caught by themselves.　(5) Establishments primarily engaged in the purchase, sale, and repair of bicycles that assemble and sell new bicycles.

The study of social class and corresponding measurement schemes has evolved separately in Europe and the US. On both continents a standardized occupational coding system exists that can be transferred into a wide scala of measures of socioeconomic status. This dataset contains a crosswalk between the two standardized historical occupational coding schemes: HISCO and Occ1950.

The Historical International Standardized Classification of Occupations (HISCO) is the European standard for occupational coding and can be used to generate social class schemes, such as HISCLASS, SOCPO, and HISCAM. The U.S. Bureau of the Census' 1950 standard (Occ1950) is the U.S. standard for occupational coding and can be used to generate social class schemes, like NPBOSS, OCCSCORE, PRESGL, and SEI. With the crosswalk, HISCO can be converted to the American class coding schemes and Occ1950 into the European class coding schemes.

Occupational categories were linked between HISCO and Occ1950 on the underlying occupations. Both HISCO and Occ1950 consist of multiple layers of occupational groups. HISCO is divided in 7 major, 76 minor, 296 unit, and 1,675 micro groups, which roughly correspond with: social classes, sectors, occupational groups, and occupational subgroups. Occ1950 on the other hand is divided in 10 social classes and 269 occupational groups. HISCO’s micro groups and Occ1950’s occupational subgroups are based on a well-documented number of occupations, which can easily be compared and matched between both occupational coding schemes.

In the translation from HISCO to Occ1950 1,675 occupational categories were collapsed into 229 Occ1950 unique occupational groups. Although 40 occupational groups in Occ1950 could not be retrieved from HISCO, all occupations were successfully attributed to the right social class. Vice versa, 269 occupational groups in Occ1950 were recoded into 227 HISCO micro groups. Together these 227 unique codes are well-spread over the different branches of the HISCO tree, as they cover most of the unit groups.

#Please note that this is not the crosswalk from Occ1950 to the intermediate HISCO used by the NAPP project, also known as OCCHISCO or NAPPHISCO. This crosswalk can be retrieved from: https://github.com/rlzijdeman/o-clack/tree/master/crosswalks/occhisco_to_hisco

#HISCO is the European standard for occupational coding and can be used to generate HISCLASS, SOCPO and HISCAM classifications. The necessary conversion table has been made available by Mandemakers et al. and is available on: https://socialhistory.org/en/hsn/hsn-standardized-hisco-coded-and-classified-occupational-titles-release-201301?language=en

#Occ1950 is the US standard for occupational coding. The occupational coding system is based on the US Census of 1950 and can be transferred into OCCSCORE, PRESGL, SEI, and Nam-Powers-Boyd. Crosswalks are available on request: https://usa.ipums.org/usa/vols_4_5_index.shtml

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Mexico Population: Census: 45 to 49 Yrs Old data was reported at 6,814.143 Person th in 2015. This records an increase from the previous number of 5,928.730 Person th for 2010. Mexico Population: Census: 45 to 49 Yrs Old data is updated yearly, averaging 3,612.452 Person th from Dec 1950 (Median) to 2015, with 9 observations. The data reached an all-time high of 6,814.143 Person th in 2015 and a record low of 1,073.549 Person th in 1950. Mexico Population: Census: 45 to 49 Yrs Old data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.G002: Population: Census.

Mexico Population: Census: 20 to 24 Yrs Old data was reported at 10,665.816 Person th in 2015. This records an increase from the previous number of 9,892.271 Person th for 2010. Mexico Population: Census: 20 to 24 Yrs Old data is updated yearly, averaging 8,964.629 Person th from Dec 1950 (Median) to 2015, with 9 observations. The data reached an all-time high of 10,665.816 Person th in 2015 and a record low of 2,299.334 Person th in 1950. Mexico Population: Census: 20 to 24 Yrs Old data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.G002: Population: Census.

1950 Education Census Data for Baltimore, Maryland. Refer to the 1950 codebook (codebook_1950.pdf) for more information. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

In 2023, about 17.7 percent of the American population was 65 years old or over; an increase from the last few years and a figure which is expected to reach 22.8 percent by 2050. This is a significant increase from 1950, when only eight percent of the population was 65 or over. A rapidly aging population In recent years, the aging population of the United States has come into focus as a cause for concern, as the nature of work and retirement is expected to change to keep up. If a population is expected to live longer than the generations before, the economy will have to change as well to fulfill the needs of the citizens. In addition, the birth rate in the U.S. has been falling over the last 20 years, meaning that there are not as many young people to replace the individuals leaving the workforce. The future population It’s not only the American population that is aging -- the global population is, too. By 2025, the median age of the global workforce is expected to be 39.6 years, up from 33.8 years in 1990. Additionally, it is projected that there will be over three million people worldwide aged 100 years and over by 2050.

1950 Dwellings Census Data for Baltimore, Maryland. Refer to the 1950 codebook (codebook_1950.pdf) for more information. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Context

The United States Census Bureau’s International Dataset provides estimates of country populations since 1950 and projections through 2050.

Content

The U.S. Census Bureau provides estimates and projections for countries and areas that are recognized by the U.S. Department of State that have a population of at least 5,000. Specifically, the data set includes midyear population figures broken down by age and gender assignment at birth. Additionally, they provide time-series data for attributes including fertility rates, birth rates, death rates, and migration rates.

Fork this kernel to get started.

Acknowledgements

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

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

Dataset Source: www.census.gov

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 countries have the longest life expectancy?

Which countries have the largest proportion of their population under 25?

Which countries are seeing the largest net migration?

The 7th Population Census, this is the first large-scale census after the war. In order to clarify the state of Japan’s population and households, the population census has been conducted in Japan almost every five years.More details on the "Population Census of Japan" overall including other years can be found here: https://d-infra.ier.hit-u.ac.jp/Japanese/statistical-yb/b001.html. 　　　　　 Implemented under the auspices of the General Headquarters of the Supreme Commander for the Allied Powers. From this census onward, persons are surveyed not on the basis of where they were at the time of the census but their place of usual residence. Moreover, this was a census administered by enumerators.

These are the files, results, model and source code for the OCCODE project. They include:The results from running the occupational column from the full county 1950 Norwegian census through the OCCODE pipeline.An overview of the coding system used by Statistics Norway to encode the occupations of the 1950 Norwegian full count census.This overview was created by Statistics Norway, and the original can be found here: https://www.ssb.no/a/folketellinger/The model trained on the Random sample dataset.Previous results from running the occupational column from the full county 1950 Norwegian census through the OCCODE pipeline.Note that these results are worse than the current ones, found in RandomSample_total_confidence_scores.csvThe source code for the OCCODE pipeline as of 2021.05.11