Historical population as enumerated and corrected from 1790 through 2020. North Carolina was one of the 13 original States and by the time of the 1790 census had essentially its current boundaries. The Census is mandated by the United States Constitution and was first completed for 1790. The population has been counted every ten years hence, with some limitations. In 1790 census coverage included most of the State, except for areas in the west, parts of which were not enumerated until 1840. The population for 1810 includes Walton County, enumerated as part of Georgia although actually within North Carolina. Historical populations shown here reflect the population of the respective named county and not necessarily the population of the area of the county as it was defined for a particular census. County boundaries shown in maps reflect boundaries as defined in 2020. Historic boundaries for some counties may include additional geographic areas or may be smaller than the current geographic boundaries. Notes below list the county or counties with which the population of a currently defined county were enumerated historically (Current County: Population counted in). The current 100 counties have been in place since the 1920 Census, although some modifications to the county boundaries have occurred since that time. For historical county boundaries see: Atlas of Historical County Boundaries Project (newberry.org)County Notes: Note 1: Total for 1810 includes population (1,026) of Walton County, reported as a Georgia county but later determined to be situated in western North Carolina. Total for 1890 includes 2 Indians in prison, not reported by county. Note 2: Alexander: *Iredell, Burke, Wilkes. Note 3: Avery: *Caldwell, Mitchell, Watauga. Note 4: Buncombe: *Burke, Rutherford; see also note 22. Note 5: Caldwell: *Burke, Wilkes, Yancey. Note 6: Cleveland: *Rutherford, Lincoln. Note 7: Columbus: *Bladen, Brunswick. Note 8: Dare: *Tyrrell, Currituck, Hyde. Note 9: Hoke: *Cumberland, Robeson. Note 10: Jackson: *Macon, Haywood. Note 11: Lee: *Moore, Chatham. Note 12: Lenoir: *Dobbs (Greene); Craven. Note 13: McDowell: *Burke, Rutherford. Note 14: Madison: *Buncombe, Yancey. Note 15: Mitchell: *Yancey, Watauga. Note 16: Pamlico: *Craven, Beaufort. Note 17: Polk: *Rutherford, Henderson. Note 18: Swain: *Jackson, Macon. Note 19: Transylvania: *Henderson, Jackson. Note 20: Union: *Mecklenburg, Anson. Note 21: Vance: *Granville, Warren, Franklin. Note 22: Walton: Created in 1803 as a Georgia county and reported in 1810 as part of Georgia; abolished after a review of the State boundary determined that its area was located in North Carolina. By 1820 it was part of Buncombe County. Note 23: Watauga: *Ashe, Yancey, Wilkes; Burke. Note 24: Wilson: *Edgecombe, Nash, Wayne, Johnston. Note 25: Yancey: *Burke, Buncombe. Note 26: Alleghany: *Ashe. Note 27: Haywood: *Buncombe. Note 28: Henderson: *Buncombe. Note 29: Person: Caswell. Note 30: Clay: Cherokee. Note 31: Graham: Cherokee. Note 32: Harnett: Cumberland. Note 33: Macon: Haywood.

Note 34: Catawba: Lincoln. Note 35: Gaston: Lincoln. Note 36: Cabarrus: Mecklenburg.
Note 37: Stanly: Montgomery. Note 38: Pender: New Hanover. Note 39: Alamance: Orange.
Note 40: Durham: Orange, Wake. Note 41: Scotland: Richmond. Note 42: Davidson: Rowan. Note 43: Davie: Rowan.Note 44: Forsyth: Stokes. Note 45: Yadkin: Surry.
Note 46: Washington: Tyrrell.Note 47: Ashe: Wilkes. Part III. Population of Counties, Earliest Census to 1990The 1840 population of Person County, NC should be 9,790. The 1840 population of Perquimans County, NC should be 7,346.

Starting in mid-July of 2020, despite many delays due to Covid-19, census takers began interviewing households who had not yet responded online or via the mail to the U.S. 2020 Census. The federal census, required by the United States’ Constitution, happens once every 10 years and each time, there are new variations in enumeration (counting) techniques and what statistical data to collect. There are processes around “how” to count and then also “what” to count; the data collected needs to be useful for governance and allocation yet also respectful of privacy and remain fair and impartial for the entire U.S. population. In 2019 and 2020, hundreds of thousands of temporary workers from local communities were hired to go out into the field as census takers as well as staff offices and provide supervision. This 22nd federal census count began in January 2020 with remote portions of Alaska, where the territory was still frozen and traversable. These employed citizens are just one aspect of how the census is truly a community event. Let’s dive into the history of the U.S. Census and also learn why this count is so important.

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.

US County & Zipcode Historical Demographics

Easily lookup US historical demographics by county FIPS or zipcode in seconds with this file containing over 5,901 different columns including:

*Lat/Long *Boundaries *State FIPS *Population from 2010-2019 *Death Rate from 2010-2019 *Unemployment from 2001-2020 *Education from 1970-2019 *Gender and Age Population

Provided by bitrook.com to help Data Scientists clean data faster.

Data Sources

All Data Combined Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Population Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Unemployment Source:

https://www.ers.usda.gov/data-products/county-level-data-sets/download-data/

Zip FIPS Crosswalk Source:

https://data.world/niccolley/us-zipcode-to-county-state

County Boundaries Source:

https://public.opendatasoft.com/explore/dataset/us-county-boundaries/table/?disjunctive.statefp&disjunctive.countyfp&disjunctive.name&disjunctive.namelsad&disjunctive.stusab&disjunctive.state_name

Age Sex Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-agesex-**.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-agesex.pdf

Races Source:

https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/cc-est2019-alldata.csv https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/cc-est2019-alldata.pdf

Initially taken in 1838 to demonstrate the stability and significance of the African American community and to forestall the abrogation of African American voting rights, the Quaker and Abolitionist census of African Americans was continued in 1847 and 1856 and present an invaluable view of the mid-nineteenth century African American population of Philadelphia. Although these censuses list only household heads, providing aggregate information for other household members, and exclude the substantial number of African Americans living in white households, they provide data not found in the federal population schedules. When combined with the information on African Americans taken from the four federal censuses, they offer researchers a richly detailed view of Philadelphia's African American community spanning some forty years. The three censuses are not of equal inclusiveness or quality, however. The 1838 and 1847 enumerations cover only the "old" City of Philadelphia (river-to-river and from Vine to South Streets) and the immediate surrounding districts (Spring Garden, Northern Liberties, Southwark, Moyamensing, Kensington--1838, West Philadelphia--1847); the 1856 survey includes African Americans living throughout the newly enlarged city which, as today, conforms to the boundaries of Philadelphia County. In spite of this deficiency in areal coverage, the earlier censuses are superior historical documents. The 1838 and 1847 censuses contain data on a wide range of social and demographic variables describing the household indicating address, household size, occupation, whether members were born in Pennsylvania, status-at-birth, debts, taxes, number of children attending school, names of beneficial societies and churches (1838), property brought to Philadelphia from other states (1838), sex composition (1847), age structure (1847), literacy (1847), size of rooms and number of people per room (1847), and miscellaneous remarks (1847). While the 1856 census includes the household address and reports literacy, occupation, status-at-birth, and occasional passing remarks about individual households and their occupants, it excludes the other informational categories. Moreover, unlike the other two surveys, it lists the occupations of only higher status African Americans, excluding unskilled and semiskilled designations, and records the status-at-birth of adults only. Indeed, it even fails to provide data permitting the calculation of the size and age and sex structure of households. Variables for each household head and his household include (differ slightly by census year): name, sex, status-at-birth, occupation, wages, real and personal property, literacy, education, religion, membership in beneficial societies and temperance societies, taxes, rents, dwelling size, address, slave or free birth.

Historical population counts for municipalities in the State of Vermont (1791-2020) compiled by the Vermont Historical Society (years 1791-2010) then appended with 2020 Census counts.An attempt was made to convert counts to current town names to allow for analyses of population change of an area over time. The Historical Society notes, “For example, the census numbers from Kellyvale are counted as the town of Lowell because the name was changed in 1831. Cabot is included in Washington County records, even though it was in Caledonia County through the 1850 census.” This does create some issues where there are changes in geography such as boundary changes, annexations, and new incorporations (such as Rutland City splitting off from Rutland Town).The Historical Society collected the data from a variety of sources.The 1791-2010 data was extracted from PDF’s by VCGI Open Data Fellow Kendal Fortney in 2017.

Context. This historical dataset stems from the project of automatic extraction of 72 census records of Lausanne, Switzerland. The complete dataset covers a century of historical demography in Lausanne (1805-1898), which corresponds to 18,831 pages, and nearly 6 million cells.

Content. The data published in this repository correspond to a first release, i.e. a diachronic slice of one register every 8 to 9 years. Unfortunately, the remaining data are currently under embargo. Their publication will take place as soon as possible, and at the latest by the end of 2023. In the meantime, the data presented here correspond to a large subset of 2,844 pages, which already allows to investigate most research hypotheses.

Description. The population censuses, digitized by the Archives of the city of Lausanne, continuously cover the evolution of the population in Lausanne throughout the 19th century, starting in 1805, with only one long interruption from 1814 to 1831. Highly detailed, they are an invaluable source for studying migration, economic and social history, and traces of cultural exchanges not only with Bern, but also with France and Italy. Indeed, the system of tracing family origin, specific to Switzerland, allows to follow the migratory movements of families long before the censuses appeared. The bourgeoisie is also an essential economic tracer. In addition, censuses extensively describe the organization of the social fabric into family nuclei, around which gravitate various boarders, workers, servants or apprentices, often living in the same apartment with the family.

Production. The structure and richness of censuses have also provided an opportunity to develop automatic methods for processing structured documents. The processing of censuses includes several steps, from the identification of text segments to the restructuring of information as digital tabular data, through Handwritten Text Recognition and the automatic segmentation of the structure using neural networks. Please note that the detailed extraction methodology, as well as the complete evaluation of performance and reliability is published in:

Petitpierre R., Rappo L., Kramer M. (2023). An end-to-end pipeline for historical censuses processing. International Journal on Document Analysis and Recognition (IJDAR). doi: 10.1007/s10032-023-00428-9

Data structure. The data are structured in rows and columns, with each row corresponding to a household. Multiple entries in the same column for a single household are separated by vertical bars ⟨|⟩. The center point ⟨·⟩ indicates an empty entry. For some columns (e.g., street name, house number, owner name), an empty entry indicates that the last non-empty value should be carried over. The page number is in the last column.

Liability. The data presented here are not curated nor verified. They are the raw results of the extraction, the reliability of which was thoroughly assessed in the above-mentioned publication. We insist on the fact that for any reuse of this data for research purposes, the implementation of an appropriate methodology is necessary. This may typically include string distance heuristics, or statistical methodologies to deal with noise and uncertainty.

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 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 1910 census data was collected in April 1910. 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.

Section 2

This dataset was created on 2020-01-10 23:47:27.924 by merging multiple datasets together. The source datasets for this version were:

IPUMS 1910 households: The Integrated Public Use Microdata Series (IPUMS) Complete Count Data are historic individual and household census records and are a unique source for research on social and economic change.

IPUMS 1910 persons: This dataset includes all individuals from the 1910 US census.

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin; for the United States, States, Counties; and for Puerto Rico and its Municipios: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

The data sets in this repository allow users to link people among the U.S. decennial censuses, using the "histid" identifier. The census data sets users will need are indexed by Ancestry.com and are hosted by IPUMS at https://usa.ipums.org/usa-action/samples. Users will need to download the full-count census for each year and be sure to select the "histid" variable that is available under the Person/Historical Technical drop-down menu.As of 7/12/21, links are available between the 1900-1910, 1910-1920, and 1900-1920 censuses.A detailed account of how these links are created and a description of the data and its characteristics are available in the following article:Price, J., Buckles, K., Van Leeuwen, J., & Riley, I. (2021). Combining family history and machine learning to link historical records: The Census Tree data set. Explorations in Economic History, 80, 101391.https://www.sciencedirect.com/science/article/pii/S0014498321000024

The MARS file contains modified race and age data based on the 1990 Census. Both race and age are tabulated by sex and Hispanic origin for several layers of geography. The race data were modified to make reporting categories comparable to those used by state and local agencies. The 1990 Census included 9,804,847 persons who checked the "other race" category and were therefore not included in one of the 15 racial categories listed on the Census form. "Other race" is usually not an acceptable reporting category for state and local agencies. Therefore, the Census Bureau assigned each "other race" person to the specified race reported by another person geographically close with an identical response to the Hispanic-origin question. Hispanic origin was taken into account because over 95 percent of the "other race" persons were of Hispanic origin. (Hispanic-origin persons may be of any race.) The assignment of race to Hispanic-origin persons did not affect the Hispanic-origin category that they checked (i.e, Mexican, Puerto Rican, Cuban, etc.). Age data were modified because respondents tended to report age as of the date they completed the 1990 questionnaire, instead of age as of the April 1, 1990 Census date. In addition, there may have been a tendency for respondents to round up their age if they were close to having a birthday. Age data for individuals in households were modified by adjusting the reported birth-year data by race and sex for each of the 1990 Census's 449 district offices to correspond with the national level quarterly distribution of births available from the National Center for Health Statistics. The data for persons in group quarters were adjusted similarly, but on a state basis. The age adjustment affects approximately 100 million people. In this file their adjusted age is one year different from that reported in the 1990 Census.

This dataset contains data on population by sex and age on the basis of the results of the Census Data of Estonia, which was carried out on 28 December 1922. Dataset "Estonian Population by Sex and Age in 1922 Census Data" was published implementing project "Historical Sociology of Modern Restorations: a Cross-Time Comparative Study of Post-Communist Transformation in the Baltic States" from 2018 to 2022. Project leader is prof. Zenonas Norkus. Project is funded by the European Social Fund according to the activity "Improvement of researchers' qualification by implementing world-class R&D projects' of Measure No. 09.3.3-LMT-K-712".

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. Each .csv file consists of a crosswalk between the two years indicated in the filename, using the IPUMS histids. For more information, consult the included Read Me file, and visit https://censustree.org.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED March 2020

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2018) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2018) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2018. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

Annual County Resident Population Estimates for 5 Race Groups (5 Race Alone or in Combination Groups) by Selected Age Groups, Sex, and Hispanic Origin // Source: U.S. Census Bureau, Population Division // Note: 'In combination' means in combination with one or more other races. The sum of the five race groups adds to more than the total population because individuals may report more than one race. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see http://www.census.gov/popest/data/historical/files/MRSF-01-US1.pdf. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2014) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

In the past four centuries, the population of the United States has grown from a recorded 350 people around the Jamestown colony of Virginia in 1610, to an estimated 331 million people in 2020. The pre-colonization populations of the indigenous peoples of the Americas have proven difficult for historians to estimate, as their numbers decreased rapidly following the introduction of European diseases (namely smallpox, plague and influenza). Native Americans were also omitted from most censuses conducted before the twentieth century, therefore the actual population of what we now know as the United States would have been much higher than the official census data from before 1800, but it is unclear by how much. Population growth in the colonies throughout the eighteenth century has primarily been attributed to migration from the British Isles and the Transatlantic slave trade; however it is also difficult to assert the ethnic-makeup of the population in these years as accurate migration records were not kept until after the 1820s, at which point the importation of slaves had also been illegalized. Nineteenth century In the year 1800, it is estimated that the population across the present-day United States was around six million people, with the population in the 16 admitted states numbering at 5.3 million. Migration to the United States began to happen on a large scale in the mid-nineteenth century, with the first major waves coming from Ireland, Britain and Germany. In some aspects, this wave of mass migration balanced out the demographic impacts of the American Civil War, which was the deadliest war in U.S. history with approximately 620 thousand fatalities between 1861 and 1865. The civil war also resulted in the emancipation of around four million slaves across the south; many of whose ancestors would take part in the Great Northern Migration in the early 1900s, which saw around six million black Americans migrate away from the south in one of the largest demographic shifts in U.S. history. By the end of the nineteenth century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. Twentieth and twenty-first century The U.S. population has grown steadily throughout the past 120 years, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. In the past century, the U.S. established itself as a global superpower, with the world's largest economy (by nominal GDP) and most powerful military. Involvement in foreign wars has resulted in over 620,000 further U.S. fatalities since the Civil War, and migration fell drastically during the World Wars and Great Depression; however the population continuously grew in these years as the total fertility rate remained above two births per woman, and life expectancy increased (except during the Spanish Flu pandemic of 1918).

Since the Second World War, Latin America has replaced Europe as the most common point of origin for migrants, with Hispanic populations growing rapidly across the south and border states. Because of this, the proportion of non-Hispanic whites, which has been the most dominant ethnicity in the U.S. since records began, has dropped more rapidly in recent decades. Ethnic minorities also have a much higher birth rate than non-Hispanic whites, further contributing to this decline, and the share of non-Hispanic whites is expected to fall below fifty percent of the U.S. population by the mid-2000s. In 2020, the United States has the third-largest population in the world (after China and India), and the population is expected to reach four hundred million in the 2050s.

This training dataset includes a total of 34,913 manually transcribed text segments. It is dedicated to the handwritten text recognition (HTR) of historical sources, typically tabular records, such as censuses. This dataset is based on a sample of 83 pages from the 19th century (1805-1898) censuses of Lausanne, Switzerland. The primary language of the documents is French, although many germanic names and toponyms are also found.

The training data are formatted and provided on the model of the Bentham dataset. The format thus simply consists in a list of jpeg images, one per text segments, and their corresponding transcription, stored in a txt file. The file naming convention is 'yyyy-ppp-n', where 'y' stands for the year of publication of the census, and 'p' for the page number.

The digitized documents are provided by the Archives of the City of Lausanne.

Please note that the annotation and extraction methodology, as well as the complete evaluation of performance, including HTR benchmark and post-correction performance is published in :

Petitpierre R., Rappo L., Kramer M. (2023). An end-to-end pipeline for historical censuses processing. International Journal on Document Analysis and Recognition (IJDAR). doi: 10.1007/s10032-023-00428-9

Tabular dataset resulting from automatic extraction are also available on Zenodo :

Petitpierre R., Rappo L., Kramer M., di Lenardo I. (2023). 1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History. Zenodo. doi: 10.5281/zenodo.7711640

The 1940 Census Public Use Microdata Sample Project was assembled through a collaborative effort between the United States Bureau of the Census and the Center for Demography and Ecology at the University of Wisconsin. The collection contains a stratified 1-percent sample of households, with separate records for each household, for each "sample line" respondent, and for each person in the household. These records were encoded from microfilm copies of original handwritten enumeration schedules from the 1940 Census of Population. Geographic identification of the location of the sampled households includes Census regions and divisions, states (except Alaska and Hawaii), standard metropolitan areas (SMAs), and state economic areas (SEAs). Accompanying the data collection is a codebook that includes an abstract, descriptions of sample design, processing procedures and file structure, a data dictionary (record layout), category code lists, and a glossary. Also included is a procedural history of the 1940 Census. Each of the 20 subsamples contains three record types: household, sample line, and person. Household variables describe the location and condition of the household. The sample line records contain variables describing demographic characteristics such as nativity, marital status, number of children, veteran status, wage deductions for Social Security, and occupation. Person records also contain variables describing demographic characteristics including nativity, marital status, family membership, education, employment status, income, and occupation.

OCUL’s Historical Census Working Group (part of the OCUL Data Community) is working on scoping a comprehensive bilingual inventory of Canadian census data. Our dream is to eventually build a bilingual and openly available discovery platform for census data & statistical tables (print & digital) going back to the earliest Canadian censuses. This presentation will provide a status update on the project and dedicate time for attendees to discuss the project and provide input.