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

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

The 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.

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

This 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).

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: No - Individuals: Yes - Group quarters: No - Special populations: No

UNIT DESCRIPTIONS: - Dwellings: A dwelling is a separate set of living quarterwith a private entrace from outside or from a common hallway or stairway inside the building. This entrance must not be through someone else's living quarters. - Households: Refers to a person or group of persons (other than foreign residents) who occupy a dwelling and do not have a usual place of residence elsewhere in Canada. It usually consists of a family group with or without other non-family persons, of two or more families sharing a dwelling, of a group of unrelated persons, or of one person living alone. Household members who are temporarily absent on Census Day (e.g., temporary residents elsewhere) are considered as part of their usual household. For census purpose, every person is a member of one and only one household.

Universe

Canadian citizens and landed immigrants having a usual place of residence in Canada or residing aroad, on a military base or on a diplomatic mission. The file also includes data on non-permanent residents of Canada. The inclusion of non-permanent residents in the population universe of the 1991 Census marks a change from previous census coverage. The file excludes institutional residents, residents of partial refusal Indian reserves or Indian settlements, and foreign residents, namely foreign diplomats, members of the armed forces of another country who are stationed in Canada, and resdients of another country who are visiting Canada temporarily.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Statistics Canada

SAMPLE DESIGN: (a) Systematic sample of every 5th household with a random start was given a long form. (b) The long form sample was then stratified within each georgraphic region. (c) The final sample was selected systematically using a sampling interval of 100/9, with a random start between 0 and the sampling interval. The sample size is equal to 3% of the target population.

SAMPLE UNIT: Household

SAMPLE FRACTION: 3%

SAMPLE SIZE (person records): 809,654

Mode of data collection

Face-to-face [f2f]

Research instrument

The long form which requested information about dwellings, households and individuals.

Abstract

The 2008 Sudan Population and Housing Census is the 5th Sudan Population and Housing Census conducted, and one of the most important censuses in the history of Sudan. It is based on the comprehensive peace agreement. It provides hope for Sudanese people to build a new Sudan, with a fair share in power, resources, services and development. To achieve these goals a population census with a high accuracy and a full coverage is a necessity.

Integrated Public Use Microdata Series (IPUMS)-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National

Analysis unit

• Households;
• Individual.

Universe

The de facto method is applied for the enumeration of the population.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The sample size (person records) is equal to 5'066'530.

Sampling deviation

Long form questionnaire for sedentary households (selected enumeration areas) and a sample of nomad households.

Mode of data collection

Face-to-face [f2f]

Research instrument

As mentioned above the census data is to be collected in two forms. A short form to be used for 90% of EAs with a minimum number of questions ( 11 questions ) and to satisfy the basic population data needed for the election and other basic demographic needs. A long form to be administered in10% of the enumeration areas (EAS) and will provide all other standard social and economic information. The details of these questionnaires are following closely the UN principles and recommendations for censuses as decided by the TWG. That had put sometimes the TWG in conflicts with the governing councils and politicians at the national and regional levels. For e.g. the MOC had requested the deletion of the questions on ethnicity after its endorsement by the PCC in its second meeting. The PCC decided to raise it to the Presidency as the TWG had reconfirmed its technical importance. Based on the understanding that ethnicity and religion are causes of conflicts in Sudan, the Presidency decided to delete these questions. It was suggested as a compromise to use the question on previous residence to give information about Southern people living in the North. The South Sudan Population Census Council (SSPCC) requested an amplification of the question to reflect household origin from the nine 1956 Provinces (Northern, Khartoum, Central, Eastern, Kordofan, Darfur, Upper Nile, Bahr Elghazal and Equatoria) in stead of (north/south). But that was not accepted by many members of the PCC and some politicians in the north who believe that it is another way of bringing back the ethnicity question. The SSPCC then insisted on the re-inclusion of the ethnicity and religion questions. That led to a lot of delays in printing the questionnaires. In order to get out of this dilemma the TWG with support of UNFPA had decided to stick firmly to the UN standards. That is to stick to the previous residence question (origin) which is core one and to neglect the ethnicity question which is an optional one.

Cleaning operations

For census data entry the Technical Working Group (TWG) decided with endorsement of the PCC that the data entry was to be decentralized. Nine centers were suggested. These are the capitals of old British provinces. The TWG also decided that the short and long forms to be scanned using optical mark recognition (OMR) technology. That decision was based on the field visits to some African countries which used the same technology in their censuses. For quality assurance a high level team from both CBS and SSCCSE were sent to DRS Company in UK to ensure that the forms were correctly printed in both Arabic and English so as to avoid occurrence of any errors or faults during enumeration and the scanning process. It was decided that the census data was to be processed, the results produced and the tabulation prepared centrally. The national and regional tabulation to be analyzed and published using different data dissemination methods such as:-printed reports, electronic media (websites, Emails), data archiving, seminars and workshops. The use of internet as another tool for data dissemination was also suggested.

Part of the What Works Cities criterion to achieve Certification, we need to meet the industry standard of at least 75% of our households have subscriptions / access to high-speed broadband servicesPart of the American Community Survey (ACS) asks the levels of internet access residents have. We use the 5-Year Estimates to have a greater level of precision to our data, according to the Distinguishing features of ACS 1-year, 1-year supplemental, 3-year, and 5-year estimates table.We query attributes of the DP02 (Selected Social Characteristics in the United States) Group of questions for years available.This dataset has been narrowed down to Cary township using following the geographies codes supported for the ACS dataset:state: 37county: 183county subdivision: 90536

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.

Original provider: Observatoire PELAGIS UAR 3462 University La Rochelle - CNRS

Dataset credits: Observatoire PELAGIS UMS 3462, University La Rochelle - CNRS -Agence des Aires Marines Protégées - Direction de l'Eau et de la Biodiversité

Abstract: In order to establish a baseline map of cetaceans and other pelagic megafauna across the French EEZ, the French agency for marine protected areas (AAMP) decided to conduct a series of surveys allowing hotspots of abundance and diversity to be identified and a future monitoring scheme to be established. A dedicated aerial survey methodology, following standard protocols, was preferred to ship surveys. The general design corresponds to published protocols prepared for small cetaceans, but data for other marine mammals (large whales, sirenians), seabirds, sea-turtles, large teleosts and large elasmobranchs) are collected as well. Data collected include species, group size, angle to survey track for cetaceans located within 500m on both sides of survey track, allowing line transect data analyses. For seabirds all encounters located within 200m on both sides of survey track are recorded for strip-transect analysis. Covariates collected on board include sea-state, turbidity, glare and cloud coverage. The study areas include all sectors of the French EEZ: North-East Atlantic, the tropical Atlantic (French Caribbean and Guiana), Indian (Reunion Island, Mayotte and the Scattered Islands) and south Pacific oceans (French Polynesia, New Caledonia, Wallis and Futuna). These surveys follow the general SCANS methodology (Hiby and Lovell, 1998) adapted to aircrafts. A zigzag track layout is used and transects are sampled at a target altitude of 180 m and ground speed of 90 nm.h-1 (167 km.h-1). Survey platforms are high-wing, double-engine aircrafts fitted with bubble windows; a Partenavia P68 was used in 2008 in the Atlantic and two Britten Norman BN-2 in 2009-10 in the southwest Indian Ocean. Survey crew typically consists in two trained observers observing with naked eyes and a flight leader in charge of data collection.

Purpose: In order to establish a baseline map of cetaceans and other pelagic megafauna across the French EEZ, it was decided to conduct a series of surveys from 2008 onwards following a standardized methodology that would allow comparisons within and between regions as well as temporally, for the sake of the identification of hotspots of abundance and diversity and the establishment of a future monitoring scheme. These surveys are named the REMMOA and SAMM surveys for REcensement des Mammifères marins et autre Mégafaune pélagique par Observation Aérienne (Census of marine mammals and other pelagic megafauna by aerial survey) and Suivi Aérien de la Megafaune Marine (Aerial survey for marine megafauna). Additionally, considering the fragmented nature of the French EEZ, notably compared to the spatial scale that is relevant for the species of interest, the implementation of these surveys at regional scale by collaboration with neighboring countries was encouraged.

Supplemental information: [2022-01-18] Data in 2020 were appended. [2018-04-26] Data in 2016 and 2017 were appended.

Time and group size of the sightings are not available online. They may be released upon request.

There are records for plankton observations but these records are not visible online.

The animals the provider identified as Sterninae spp were registered as Laridae spp. However, you can still see the original species identification online.

This publication is the metadata only. Please find the dataset described below at: https://doi.org/10.5061/dryad.q2bvq83v4 Lianas are a common and diverse plant growth form in tropical forests, where they add considerably to the vascular plant diversity. Lianas contribute approximately 25% of the woody vascular plant species diversity in tropical forests (Gentry 1991, Schnitzer & Bongers 2002), with liana diversity varying systematically with forest mean annual rainfall and rainfall seasonality (Schnitzer 2005, 2018, Swaine & Grace 2007, DeWalt et al. 2010, 2015, Parolari et al. 2020). However, liana taxonomy and diversity have been described for relatively few tropical forest communities, thus the proportion of diversity that lianas contribute and how this diversity varies among forests is poorly understood. Here we describe and release the species present in the 2007 and 2017 liana censuses of the Barro Colorado Island, Panama (BCI) 50-ha plot. Our liana species dataset includes the current and former family, genus, species, and authority for all liana stems 1 cm diameter or larger that were rooted within the BCI 50-ha plot in either of the 2007 or 2017 censuses. Lianas were identified in the forest during the 2007 and 2017 censuses (Schnitzer et al. 2012, 2015, 2021, Schnitzer & DeFilippis 2024). In 2023, we compared our species list to the Plants of the World Online, a global list compiled by Kew Gardens to ensure that our nomenclature was consistent with the most recent taxonomic changes (see Schnitzer et al. 2024). Methods used for the liana census study were published in Gerwing et al. 2006 and Schnitzer et al. 2008; see also Parren et al. 2005, Schnitzer et al. 2006). We found a total of 178 species distributed among 117,100 rooted individuals (1 cm diameter or larger, excluding clonal stems) that were present in either the 2007 or 2017 liana censuses of the BCI 50-ha plot. We were able to positively identify > 98% of these stems to species (Schnitzer et al. 2012, 2021). Liana species contributed 35% of the woody species (lianas, trees, and shrubs 1 cm diameter or larger) using the tree and shrub data from the 2015 tree census (Condit et al. 2019). This liana species list, in combination with the spatially explicit liana and tree stem datasets (Condit et al. 2019, 2020, Schnitzer & DeFilippis 2024) provides a unique opportunity to test conceptual questions on how lianas and trees coexist in tropical forests (e.g., Schnitzer 2018, DeFilippis et al. in review, Medina-Vega et al. in review, Mello et al. in review). We welcome opportunities to collaborate with research groups interested in using this dataset; however, the data are free to be used with no restrictions other than citing this data paper and acknowledging NSF grants DEB-0613666 and IOS 15-58093, which funded the 2007 and 2017 liana censuses.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

UNITS IDENTIFIED: - Dwellings: No - Vacant units: Yes - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: A site used for the accommodation of persons - Households: The entire group of people related or not by kinship, who live in the same house and share household expenses. If there are dependencies in a house or rooms occupied by domestic servants with their families or rented to another family, these groups should be considered as separate households. - Group quarters: An establishment where people live, temporarily or permanently, not as families or households but where they do share meals and are subject to an interest or common scheme. For the purposes of the census, hotels, homes, schools, asylums, barracks, hospitals, pensions, prisons, and other similar establishments fall into this category. If there are family habitations within the collective dwelling for administrative or service purposes, they must be considered as independent households.

Universe

All persons who spent the night of 31 July 2007 in the country except diplomats residing in foreign embassies

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Instituto Nacional de Estatística

SAMPLE DESIGN: Systematic sample of every 10th household with a random start, drawn by the country

SAMPLE UNIT: household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 2,047,048

Mode of data collection

Face-to-face [f2f]

Research instrument

The census questionnaire was designed to obtain information related to:

1) Population: Sex and age Civil Status Religion Civil registration Place of birth and nationality Disability Language Literacy School attendance Educational attainment Economic activity (occupation and industry) Fertility Infant and maternal mortality

2) Housing: Housing materials used Water and sanitation Source of energy Durable goods Access to and use of computers Access to and use of the Internet

Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

Key Features:

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Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!

AP VoteCast is a survey of the American electorate conducted by NORC at the University of Chicago for Fox News, NPR, PBS NewsHour, Univision News, USA Today Network, The Wall Street Journal and The Associated Press.

AP VoteCast combines interviews with a random sample of registered voters drawn from state voter files with self-identified registered voters selected using nonprobability approaches. In general elections, it also includes interviews with self-identified registered voters conducted using NORC’s probability-based AmeriSpeak® panel, which is designed to be representative of the U.S. population.

Interviews are conducted in English and Spanish. Respondents may receive a small monetary incentive for completing the survey. Participants selected as part of the random sample can be contacted by phone and mail and can take the survey by phone or online. Participants selected as part of the nonprobability sample complete the survey online.

In the 2020 general election, the survey of 133,103 interviews with registered voters was conducted between Oct. 26 and Nov. 3, concluding as polls closed on Election Day. AP VoteCast delivered data about the presidential election in all 50 states as well as all Senate and governors’ races in 2020.

Using this Data - IMPORTANT

This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use statistical software such as R or SPSS to weight the data.

National Survey

The national AP VoteCast survey of voters and nonvoters in 2020 is based on the results of the 50 state-based surveys and a nationally representative survey of 4,141 registered voters conducted between Nov. 1 and Nov. 3 on the probability-based AmeriSpeak panel. It included 41,776 probability interviews completed online and via telephone, and 87,186 nonprobability interviews completed online. The margin of sampling error is plus or minus 0.4 percentage points for voters and 0.9 percentage points for nonvoters.

State Surveys

In 20 states in 2020, AP VoteCast is based on roughly 1,000 probability-based interviews conducted online and by phone, and roughly 3,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.3 percentage points for voters and 5.5 percentage points for nonvoters.

In an additional 20 states, AP VoteCast is based on roughly 500 probability-based interviews conducted online and by phone, and roughly 2,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 2.9 percentage points for voters and 6.9 percentage points for nonvoters.

In the remaining 10 states, AP VoteCast is based on about 1,000 nonprobability interviews conducted online. In these states, the margin of sampling error is about plus or minus 4.5 percentage points for voters and 11.0 percentage points for nonvoters.

Although there is no statistically agreed upon approach for calculating margins of error for nonprobability samples, these margins of error were estimated using a measure of uncertainty that incorporates the variability associated with the poll estimates, as well as the variability associated with the survey weights as a result of calibration. After calibration, the nonprobability sample yields approximately unbiased estimates.

As with all surveys, AP VoteCast is subject to multiple sources of error, including from sampling, question wording and order, and nonresponse.

Sampling Details

Probability-based Registered Voter Sample

In each of the 40 states in which AP VoteCast included a probability-based sample, NORC obtained a sample of registered voters from Catalist LLC’s registered voter database. This database includes demographic information, as well as addresses and phone numbers for registered voters, allowing potential respondents to be contacted via mail and telephone. The sample is stratified by state, partisanship, and a modeled likelihood to respond to the postcard based on factors such as age, race, gender, voting history, and census block group education. In addition, NORC attempted to match sampled records to a registered voter database maintained by L2, which provided additional phone numbers and demographic information.

Prior to dialing, all probability sample records were mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Postcards were addressed by name to the sampled registered voter if that individual was under age 35; postcards were addressed to “registered voter” in all other cases. Telephone interviews were conducted with the adult that answered the phone following confirmation of registered voter status in the state.

Nonprobability Sample

Nonprobability participants include panelists from Dynata or Lucid, including members of its third-party panels. In addition, some registered voters were selected from the voter file, matched to email addresses by V12, and recruited via an email invitation to the survey. Digital fingerprint software and panel-level ID validation is used to prevent respondents from completing the AP VoteCast survey multiple times.

AmeriSpeak Sample

During the initial recruitment phase of the AmeriSpeak panel, randomly selected U.S. households were sampled with a known, non-zero probability of selection from the NORC National Sample Frame and then contacted by mail, email, telephone and field interviewers (face-to-face). The panel provides sample coverage of approximately 97% of the U.S. household population. Those excluded from the sample include people with P.O. Box-only addresses, some addresses not listed in the U.S. Postal Service Delivery Sequence File and some newly constructed dwellings. Registered voter status was confirmed in field for all sampled panelists.

Weighting Details

AP VoteCast employs a four-step weighting approach that combines the probability sample with the nonprobability sample and refines estimates at a subregional level within each state. In a general election, the 50 state surveys and the AmeriSpeak survey are weighted separately and then combined into a survey representative of voters in all 50 states.

State Surveys

First, weights are constructed separately for the probability sample (when available) and the nonprobability sample for each state survey. These weights are adjusted to population totals to correct for demographic imbalances in age, gender, education and race/ethnicity of the responding sample compared to the population of registered voters in each state. In 2020, the adjustment targets are derived from a combination of data from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, Catalist’s voter file and the Census Bureau’s 2018 American Community Survey. Prior to adjusting to population totals, the probability-based registered voter list sample weights are adjusted for differential non-response related to factors such as availability of phone numbers, age, race and partisanship.

Second, all respondents receive a calibration weight. The calibration weight is designed to ensure the nonprobability sample is similar to the probability sample in regard to variables that are predictive of vote choice, such as partisanship or direction of the country, which cannot be fully captured through the prior demographic adjustments. The calibration benchmarks are based on regional level estimates from regression models that incorporate all probability and nonprobability cases nationwide.

Third, all respondents in each state are weighted to improve estimates for substate geographic regions. This weight combines the weighted probability (if available) and nonprobability samples, and then uses a small area model to improve the estimate within subregions of a state.

Fourth, the survey results are weighted to the actual vote count following the completion of the election. This weighting is done in 10–30 subregions within each state.

National Survey

In a general election, the national survey is weighted to combine the 50 state surveys with the nationwide AmeriSpeak survey. Each of the state surveys is weighted as described. The AmeriSpeak survey receives a nonresponse-adjusted weight that is then adjusted to national totals for registered voters that in 2020 were derived from the U.S. Census Bureau’s November 2018 Current Population Survey Voting and Registration Supplement, the Catalist voter file and the Census Bureau’s 2018 American Community Survey. The state surveys are further adjusted to represent their appropriate proportion of the registered voter population for the country and combined with the AmeriSpeak survey. After all votes are counted, the national data file is adjusted to match the national popular vote for president.

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

This is the ONS Postcode Directory (ONSPD) for the United Kingdom as at February 2024 in Comma Separated Variable (CSV) and ASCII text (TXT) formats. This file contains the multi CSVs so that postcode areas can be opened in MS Excel. To download the zip file click the Download button. The ONSPD relates both current and terminated postcodes in the United Kingdom to a range of current statutory administrative, electoral, health and other area geographies. It also links postcodes to pre-2002 health areas, 1991 Census enumeration districts for England and Wales, 2001 Census Output Areas (OA) and Super Output Areas (SOA) for England and Wales, 2001 Census OAs and SOAs for Northern Ireland and 2001 Census OAs and Data Zones (DZ) for Scotland. It now contains 2021 Census OAs and SOAs for England, Wales and Northern Ireland. It helps support the production of area-based statistics from postcoded data. The ONSPD is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The ONSPD is issued quarterly. (File size - 231 MB) Please note that this product contains Royal Mail, Gridlink, LPS (Northern Ireland), Ordnance Survey and ONS Intellectual Property Rights.

Abstract

A crosswalk matching US ZIP codes to corresponding CBSA (core-based statistical area)

Documentation

The denominators used to calculate the address ratios are the ZIP code totals. When a ZIP is split by any of the other geographies, that ZIP code is duplicated in the crosswalk file.

**Example: **ZIP code 03870 is split by two different Census tracts, 33015066000 and 33015071000, which appear in the tract column. The ratio of residential addresses in the first ZIP-Tract record to the total number of residential addresses in the ZIP code is .0042 (.42%). The remaining residential addresses in that ZIP (99.58%) fall into the second ZIP-Tract record.

So, for example, if one wanted to allocate data from ZIP code 03870 to each Census tract located in that ZIP code, one would multiply the number of observations in the ZIP code by the residential ratio for each tract associated with that ZIP code.

https://redivis.com/fileUploads/4ecb405e-f533-4a5b-8286-11e56bb93368%3E" alt="">(Note that the sum of each ratio column for each distinct ZIP code may not always equal 1.00 (or 100%) due to rounding issues.)

CBSA definition

A core-based statistical area (CBSA) is a U.S. geographic area defined by the Office of Management and Budget (OMB) that consists of one or more counties (or equivalents) anchored by an urban center of at least 10,000 people plus adjacent counties that are socioeconomically tied to the urban center by commuting. Areas defined on the basis of these standards applied to Census 2000 data were announced by OMB in June 2003. These standards are used to replace the definitions of metropolitan areas that were defined in 1990. The OMB released new standards based on the 2010 Census on July 15, 2015.

Further reading

The following article demonstrates how to more effectively use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files when working with disparate geographies.

Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294. URL: https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf

Contact authors

Questions regarding these crosswalk files can be directed to Alex Din with the subject line HUD-Crosswalks.

Acknowledgement

This dataset is taken from the U.S. Department of Housing and Urban Development (HUD) office: https://www.huduser.gov/portal/datasets/usps_crosswalk.html#codebook

Abstract

The 2022 Bangladesh Demographic and Health Survey (2022 BDHS) is the ninth national survey to report on the demographic and health conditions of women and their families in Bangladesh. The survey was conducted under the authority of the National Institute of Population Research and Training (NIPORT), Medical Education and Family Welfare Division, Ministry of Health and Family Welfare (MOHFW), Government of Bangladesh.

The primary objective of the 2022 BDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the BDHS collected information on: • Fertility and childhood mortality levels • Fertility preferences • Awareness, approval, and use of family planning methods • Maternal and child health, including breastfeeding practices • Nutrition levels • Newborn care

The information collected through the 2022 BDHS is intended to assist policymakers and program managers in designing and evaluating programs and strategies for improving the health of the population of Bangladesh. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Bangladesh.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2022 BDHS is the Integrated Multi-Purpose Sampling Master Sample, selected from a complete list of enumeration areas (EAs) covering the whole country. It was prepared by the Bangladesh Bureau of Statistics (BBS) for the 2011 population census of the People’s Republic of Bangladesh. The sampling frame contains information on EA location, type of residence (city corporation, other than city corporation, or rural), and the estimated number of residential households. A sketch map that delineates geographic boundaries is available for each EA.

Bangladesh contains eight administrative divisions: Barishal, Chattogram, Dhaka, Khulna, Mymensingh, Rajshahi, Rangpur, and Sylhet. Each division is divided into zilas and each zila into upazilas. Each urban area in an upazila is divided into wards, which are further subdivided into mohallas. A rural area in an upazila is divided into union parishads (UPs) and, within UPs, into mouzas. These administrative divisions allow the country to be separated into rural and urban areas.

The survey is based on a two-stage stratified sample of households. In the first stage, 675 EAs (237 in urban areas and 438 in rural areas) were selected with probability proportional to EA size. The BBS drew the sample in the first stage following specifications provided by ICF. A complete household listing operation was then carried out by Mitra and Associates in all selected EAs to provide a sampling frame for the second-stage selection of households.

In the second stage of sampling, a systematic sample of an average of 45 households per EA was selected to provide statistically reliable estimates of key demographic and health variables for urban and rural areas separately and for each of the eight divisions in Bangladesh.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four types of questionnaires were used for the 2022 BDHS: the Household Questionnaire, the Woman’s Questionnaire (completed by ever-married women age 15–49), the Biomarker Questionnaire, and two verbal autopsy questionnaires. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect population and health issues relevant to Bangladesh. In addition, a selfadministered Fieldworker Questionnaire collected information about the survey’s fieldworkers. The questionnaires were adapted for use in Bangladesh after a series of meetings with a Technical Working Group (TWG). The questionnaires were developed in English and then translated to and printed in Bangla.

Cleaning operations

The survey data were collected using tablet PCs running Windows 10.1 and Census and Survey Processing System (CSPro) software, jointly developed by the United States Census Bureau, ICF, and Serpro S.A. The Bangla language questionnaire was used for collecting data via computer-assisted personal interviewing (CAPI). The CAPI program accepted only valid responses, automatically performed checks on ranges of values, skipped to the appropriate question based on the responses given, and checked the consistency of the data collected. Answers to the survey questions were entered into the PC tablets by each interviewer. Supervisors downloaded interview data to their computer, checked the data for completeness, and monitored fieldwork progress

Each day, after completion of interviews, field supervisors submitted data to the servers. Data were sent to the central office via the internet or other modes of telecommunication allowing electronic transfer of files. The data processing manager monitored the quality of the data received and downloaded completed files into the system. ICF provided the CSPro software for data processing and offered technical assistance in preparation of the data editing programs. Secondary editing was conducted simultaneously with data collection. All technical support for data processing and use of PC tablets was provided by ICF.