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.

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Documentation

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

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

The historic US 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.

U.S. Census BlocksThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Census Blocks. A brief description of Census Blocks, per USCB, is that "Census blocks are statistical areas bounded by visible features such as roads, streams, and railroad tracks, and by nonvisible boundaries such as property lines, city, township, school district, county limits and short line-of-sight extensions of roads." Also, "the smallest level of geography you can get basic demographic data for, such as total population by age, sex, and race."Census Block 1007Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Census Blocks) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 69 (Series Information for 2020 Census Block State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (U.S. Census Blocks - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: What are census blocksFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

U.S. Census Block GroupsThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Census block groups in the 50 states, the District of Columbia, and Puerto Rico. Per the USCB, "Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same decennial census. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas".Block Group 2 - Census Tract 010400 (Santa Fe, NM area)Data version: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Census Block Groups) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 70 (Series Information for Block Group State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (U.S. Census Block Groups - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: What are census blocks?For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

Abstract

The 2009 Census falls within the 2010 Round of Pacific Census, ten years after the 1999 census.

The results of the 2009 census will be required to:

a. help produce high-quality information for planning, decision-making, and monitoring of development progress in Solomon Islands. This implies very heavy data requirements and these requirements are continuously increasing, particularly towards development planning, implementation monitoring and evaluation of Government policies outlined in NERDEP and the current Medium Term Development Strategies.

b. The data from the Census will also be used for monitoring the achievement of the Millennium Development Goals (MDG's) and other goals included in the International Conference for Population & Development (ICPD).

c. check whether the population policies, which were put in place after the 1986 census on the basis of 1976-86 population trends and then as reviewed in the early 2000s in respect of the 1999 population trends, proved effective, and

d. Establish a new benchmark and a new set of post-1999 population trends on which to base a reconsideration of existing (population) policies in the framework of sustained and sustainable development.

e. Also, the results of this census will help facilitate updating of constituencies in preparation to the 2010 national election of Solomon Islands.

f. Further to these, the results of the census will provide a sample Frame from which further household capability surveys which include a household income expenditure in 2010/2011, a second demographic and health survey (DHS) 2011/2012 and a Labour Force Survey before the next census can be undertaken.

g. The 2009 census will also provide the much needed village level data on population, resources and infrastructure for government's bottom-up approach development policy initiative.

Accepting the notion that a new census is required and that a number of overseas aid organisations will be able to support the government on an undertaking similar to the 1999 census, the following points are considered in more detail in this project proposal.

It is recommended that the present census interval should not exceed ten years and that the same month should be selected in 2009, for the period of enumeration as in 1999, mainly to ensure that seasonal factors would not reduce the comparability of the information provided by the two censuses. As a result of this recommendation, 22nd November 2009 is therefore proposed as the new census date. This date will be formally announced by the Prime Minister in line with the Census Act.

For making current administrative decisions and prepare longer term socio-economic development policies governments and private organisations need reliable up-to-date knowledge about available natural and human resources. In a country like Solomon Islands one of the most important statistical systems for obtaining the required socio-economic information is the population census. This does not only provide a numerical description of the population at a given census date - through comparison with previous census results - but also of the ongoing trends in a sustained and sustainable development of certain population characteristics such as changes in population growth, age composition, direction of mobility and levels of urbanisation, economic activities and educational status. Such knowledge may allow the development planner to devise policies that will stem the flow of trends considered not in line with development aims. Alternatively, trends considered fitting can be identified and fostered by the introduction of appropriate policies. The success thereof can then be assessed when a next census is held some ten years later.

Geographic coverage

The 2009 Population and Housing Census Covers 100% of geography as in Urban and Rural Areas for the Entire Country :

The Solomon Islands as a whole by:

• 10 Provinces
• Constituencies
• Wards
• Enumeration Areas
• Household Level

Analysis unit

1. Population ( Urban and Rural )
2. Household ( Urban and Rural )
3. Provincial records
4. National Records
5. Geography

Universe

The National Population and Housing Census 2009,covers the entire Population,the ones in the Hotels,Motels,Ships which was collected when all ship arrived at wharf during the Census times. It covers all overseas people living in and aorund Solomon Islands,Urban and Rural,excluded the Diplomats. In overroll:- This is any individual member of the household or institution who is present on the census night and is therefore counted in the census. This includes every young and old, male of female, expatriates or residents, tourist and locals alike.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Census - Not applicable for complete enumeration survey

This section only apply for Sample Surveys.

Mode of data collection

Face-to-face [f2f]

Research instrument

1. QUESTIONNAIRE AND SCANNING

The need to set up the questionnaire in terms of suitability for local printing have done, using a software package called in-design, or whatever is most appropriate, which will then allow “optimisation ” for scanning with check boxes, drop-out colours (colours which are then filtered out by the scanner) etc. It is important that the questions are laid out correctly to make sure the results of the scan are possible and legible and eligible or recorded. Prior to the pilot census, the questionnaire needs to be finalised and come up with something everyone is happy with, finalise it and then make sure it works (if questions/formatting needs amendments as a result of the pilot, such changes will of course be done).

The questionnaire was finalised and a reliable printer to print the questionnaires was sought in advance through the tender bidding process. There are a whole series of things the Census office need to check here to make sure that the job gets done to a sufficient standard and that the scanning works well (good quality machines, paper, ink, air conditioned operating environment etc). There was no printing company in Honiara who can do this thus the printing done in Australia

In addition the questionnaire develop and were all in English language as people normally understand the english reading than the Solomons pidgin.The quetionnaire was design in Adobe Illustrator as to make sure the lines and writtings all well linned and parallel to what had written.Hence the census form have to have the right color which the scannning has to read and can easily collect the characters and values. As such the census forms had been well protected while in field and properly manage in a way which the forms will not distroyed easily by rain or sea. Hence,the census questionnaire covers Households and Housing.All Persons and GPS,more detailed in Scope section.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

a) After Scanning data exported to CSPro4.0 edited done by data proccessing officer. b) Secondly the Data proccessing officer pass the data to Data verifiers c) Structure checking and completeness by verifiers in terms of wrong written numbers and spellings

d) Batch editing: - Variables out of range - Fertility Questions - Coding and Value sets - Editing of Variables..eg.age,date of birth and etc.

Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Sampling error estimates

Not apply for Census

Data appraisal

The 2009 Census data was involved people from SPC and SINSO for checking and assisting in terms of cleaning,and verifying.After Census dataset cleaned on 19/09/2011,Census dataset has checked my running tabulation on Male and female by villages,and checking Villages were all coded and no village coded with zero "0".mean makesure all villages has values and makesure the villages with same name coded with unique code where they located by their on provinces.

Content

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

The full documentation is available here. For basic field details, please see the data dictionary.

Note: 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.

Acknowledgements

This dataset was created by the United States Census Bureau.

Inspiration

Which countries have made the largest improvements in life expectancy? Based on current trends, how long will it take each country to catch up to today’s best performers?

Use this dataset with BigQuery

You can use Kernels to analyze, share, and discuss this data on Kaggle, but if you’re looking for real-time updates and bigger data, check out the data on BigQuery, too: https://cloud.google.com/bigquery/public-data/international-census.

Abstract

The key objective of every census is to count every person (man, woman, child) resident in the country on census night, and also collect information on assorted demographic (sex, age, marital status, citizenship) and socio-economic (education/qualifications; labour force and economic activity) information, as well as data pertinent to household and housing characteristics. This count provides a complete picture of the population make-up in each village and town, of each island and region, thus allowing for an assessment of demographic change over time.

With Vanuatu, as many of her Pacific island neighbours increasingly embracing a culture of informed, or evidence-based policy development and decision-making, national census databases, and the possibility to extract complex cross-tabulations as well as a host of important sub-regional and small-area relevant information, are essential to feed a growing demand for data and information in both public and private sectors.

Educational, health and manpower planning, for example, including assessments of future demands for staffing, facilities, and programmed budgets, would not be possible without periodic censuses, and Government efforts to monitor development progress, such as in the context of its Millennium Development Goal (MDG) commitments, would also suffer greatly, if not be outright impossible, without reliable data provided by regular national population counts and updates.

While regular national-level surveys, such as Household Income and Expenditure Surveys, Labour force surveys, agriculture surveys and demographic and health surveys - to name but just a few - provide important data and information across specific sectors, these surveys could not be sustained or managed without a national sampling frame (which a census data provides). And the calculation and measurement of all population-based development indicators, such as most MDG indicators, would not be possible without up-to-date population statistics, which usually come from a census or from projections and estimates that are based on census data.

With most of this information now already 9 years old (and thus quite outdated), and in the absence of reliable population-register type databases, such as those provided from well-functional civil registration (births and deaths) and migration-recording systems, the 2009 Vanuatu census of population and housing, will provide much needed demographic, social and economic statistics that are essential for policy development, national development planning, and the regular monitoring of development progress.

Apart from achieving its general aims and objectives in delivering updated population, social and economic statistics, the 2009 census also represented a major national capacity building exercise, with most Vanuatu National Statistics Office (VNSO) staff who were involved with the census, having no prior census experience. Having been carefully planned and resourced, all 2009 census activities have potentially provided very useful (and desired) on-the-job-training for VNSO staff, right across the spectrum of professional rank and responsibilities. It also provided for short-term overseas training and professional attachments (at SPC or ABS, or elsewhere) for a limited number of professional staff, who subsequently mentored other staff in the Vanuatu National Statistics Office (VNSO).

With some key senior VNSO members involved with the 1999 census, they provided a wealth of experience that was available in-house and not to mention the ongoing surveys such HIES and Agriculture Census that the office has conducted before the census proper. The VNSO has also professional officers who have qualified in the fields of Population and Demography who had manned the project, and with this type of resources, we managed to conduct yet another successful project of the 2009 census.

While some short-term census advisory missions were fielded from SPC Demography/ Population programme staff, standard SPC technical assistance policy arrangements could not cater for long-term, or repeated in-country assignments. However, other relevant donors were invited for the longer-term attachments of TA expertise to the VNSO.

Geographic coverage

The 2009 Population and Housing Census Geographical Coverage included:

• National (Vanuatu)
• Provinces (Torba, Sanma, Penama, Malampa, Shefa, tafea)
• Inhabited Islands (From Hiu, Torres Islands to Aneityum, Southern Islands)
• Ennumeration Areas (EA assigned to each enumerator)
• Villages / Towns
• Household or Dwelling

Analysis unit

The Unit Analysis of the 2009 Population and Housing Census included: - Household - Person (Population)

Universe

The census covered all households and individuals throguhout Vanuatu

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire basically has 5 sections; the geographical identifiers, the general population questions and education, labour force questions, the women and fertility questions and the housing questions.The geographical identifiers include the Village name, GPS code, EA number, household number and the Enumerator ID.The Person questions contain the person demographics including the education level and labour force status. A section on fertility for women in the reproductive age is also included. All have been guided by 'skip patterns' to guide the flow of questions asked.Household questions contained the basic description of the house materials, tenure, access to water and sanitation, energy, durables, use of treated mosquito nest and internet access.

Cleaning operations

In the Census proper, the Optical Character Recognition (OCR) system (ReadSoft Application System) was used to capture information from the completed forms. The captured data were then exported to MS Access database system for further editing and cleaning before the final data is transferred to CSPro for more editing and quality checks before the data was finalised. All system files and data files were stored in the server under 2009PopCensus folder. Three temporary data operators were hired to do the job, under the supervision of Rara Soro, the system analyst for VNSO. No data was stored in work stations, because all data were directly written to the DATA folder in the server.

Range checks and basic checks (online edits) were built in the manual data entry system, while the complex edits were written in a separate batch edit program. If the system encounter and error during data entry, an error message will be displayed and the data operator cannot proceed unless the error displayed is fixed. e.g Males + Females = Total Persons. Please re-enter. It was strongly recommended to the data operators not to make up answers but consult the supervisor if he/she cannot fix it. Listed below are the checks that were built into the data entry system.

01 Person 1 must be the head of household 02 Sex against relationship 03 Age against date of birth 04 Marital status - Married people should be age 15+ 05 Spouse should be married 06 P9, P10, P11 against village enumerated 07 Never been to school but can use internet - Is this possible 08 Check for multiple head or spouse in the household 09 Husband and wife of same sex 10 Total persons match total people in personal form 11 Total children born and live in household (F2a) against total persons total 12 Age difference of head and child is less than 13 13 Total children born (F4) against total alive(F2) + total died(F3)

A separate batch edit program was developed for further data cleaning. All online edits were also re-written in this program to make sure that all errors flagged out during data entry were fixed. Some of the errors detected are not really errors, but still requires double checking, and if the answer recorded is the correct answer, don't change it. The batch edit was performed on each batch, and also on the concatenated batch. Below is the summary list of errors generated from manual data entry data before batch editing.

MDE Error message summary
Age does not match date of birth 272 Total children born and living in household (F2a) > total in 1
Attend school full-time in P12 but also working 16
Too young for highest education recorded 14
Highest education completed does not match with grade currently attending 80

Age had the highest errors rate, and this is due to an error in the logic statement, otherwise all ages that do not match their date of birth are corrected during data entry.

The Data capturing (Scanning) and Editing process took about 6 months to be completed but then more checks were made after that to finalise the dataset before publishing the results.

During re-coding of zero's and blanks, a couple of batch edit statement written in the batch edit program were wrong, and it created errors in the scanned data. The batch edit was suppose to recode only those people that didn't answer questions P19, P23 - P25, but instead it recoded valid codes as well to blanks. This was only picked up when tables were generated and numbers were found to be so much different in manual data entry and scanned data. Another batch edit program was developed to recode and fix this problem.

Data appraisal

Household characteristics and basic demographic variables for the census data was used in comparision with the 1999 census data to determine the accuracy of the pilot data. Some of the key indicators used for comparision are the household size, sex ratio, educational attainment, employment status. A pyramid was also used

Abstract

The 2011 Population and Housing Census is the third national Census to be conducted in Namibia after independence. The first was conducted 1991 followed by the 2001 Census. Namibia is therefore one of the countries in sub-Saharan Africa that has participated in the 2010 Round of Censuses and followed the international best practice of conducting decennial Censuses, each of which attempts to count and enumerate every person and household in a country every ten years. Surveys, by contrast, collect data from samples of people and/or households.

Censuses provide reliable and critical data on the socio-economic and demographic status of any country. In Namibia, Census data has provided crucial information for development planning and programme implementation. Specifically, the information has assisted in setting benchmarks, formulating policy and the evaluation and monitoring of national development programmes including NDP4, Vision 2030 and several sector programmes. The information has also been used to update the national sampling frame which is used to select samples for household-based surveys, including labour force surveys, demographic and health surveys, household income and expenditure surveys. In addition, Census information will be used to guide the demarcation of Namibia's administrative boundaries where necessary.

At the international level, Census information has been used extensively in monitoring progress towards Namibia's achievement of international targets, particularly the Millennium Development Goals (MDGs).

The latest and most comprehensive Census was conducted in August 2011. Preparations for the Census started in the 2007/2008 financial year under the auspices of the then Central Bureau of Statistics (CBS) which was later transformed into the Namibia Statistics Agency (NSA). The NSA was established under the Statistics Act No. 9 of 2011, with the legal mandate and authority to conduct population Censuses every 10 years. The Census was implemented in three broad phases; pre-enumeration, enumeration and post enumeration.

During the first pre-enumeration phase, activities accomplished including the preparation of a project document, establishing Census management and technical committees, and establishing the Census cartography unit which demarcated the Enumeration Areas (EAs). Other activities included the development of Census instruments and tools, such as the questionnaires, manuals and field control forms.

Field staff were recruited, trained and deployed during the initial stages of the enumeration phase. The actual enumeration exercise was undertaken over a period of about three weeks from 28 August to 15 September 2011, while 28 August 2011 was marked as the reference period or 'Census Day'.

Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultat.The post-enumeration phase started with the sending of completed questionnaires to Head Office and the preparation of summaries for the preliminary report, which was published in April 2012. Processing of the Census data began with manual editing and coding, which focused on the household identification section and un-coded parts of the questionnaire. This was followed by the capturing of data through scanning. Finally, the data were verified and errors corrected where necessary. This took longer than planned due to inadequate technical skills.

Geographic coverage

National coverage

Analysis unit

Household and person/individual

Universe

The sampling universe is defined as all households (private and institutions) from 2011 Census dataset.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sample Design The stratified random sample was applied on the constituency and urban/rural variables of households list from Namibia 2011 Population and Housing Census for the Public Use Microdata Sample (PUMS) file. The sampling universe is defined as all households (private and institutions) from 2011 Census dataset. Since urban and rural are very important factor in the Namibia situation, it was then decided to take the stratum at the constituency and urban/rural levels. Some constituencies have very lower households in the urban or rural, the office therefore decided for a threshold (low boundary) for sampling within stratum. Based on data analysis, the threshold for stratum of PUMS file is 250 households. Thus, constituency and urban/rural areas with less than 250 households in total were included in the PUMS file. Otherwise, a simple random sampling (SRS) at a 20% sample rate was applied for each stratum. The sampled households include 93,674 housing units and 418,362 people.

Sample Selection The PUMS sample is selected from households. The PUMS sample of persons in households is selected by keeping all persons in PUMS households. Sample selection process is performed using Census and Survey Processing System (CSPro).

The sample selection program first identifies the 7 census strata with less than 250 households and the households (private and institutions) with more than 50 people. The households in these areas and with this large size are all included in the sample. For the other households, the program randomly generates a number n from 0 to 4. Out of every 5 households, the program selects the nth household to export to the PUMS data file, creating a 20 percent sample of households. Private households and institutions are equally sampled in the PUMS data file.

Note: The 7 census strata with less than 250 households are: Arandis Constituency Rural, Rehoboth East Urban Constituency Rural, Walvis Bay Rural Constituency Rural, Mpungu Constituency Urban, Etayi Constituency Urban, Kalahari Constituency Urban, and Ondobe Constituency Urban.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following questionnaire instruments were used for the Namibia 2011 Population and and Housing Census: - Form A (Long Form): For conventional households and residential institutions - Form B1 (Short Form): For special population groups such as persons in transit (travellers), police cells, homeless and off-shore populations - Form B2 (Short Form): For hotels/guesthouses - Form B3 (Short Form): For foreign missions/diplomatic corps - Form C: For recording Emigrant characteristics

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) During data collection in the field b) Manual editing and coding in the office c) During data entry (Primary validation/editing) Structure checking and completeness using Structured Query Language (SQL) program d) Secondary editing: i. Imputations of variables ii. Structural checking in Census and Survey Processing System (CSPro) program

Sampling error estimates

Sampling Error The standard errors of survey estimates are needed to evaluate the precision of the survey estimation. The statistical software package such as SPSS or SAS can accurately estimate the mean and variance of estimates from the survey. SPSS or SAS software package makes use of the Taylor series approach in computing the variance.

Data appraisal

Data Quality Great efforts were made to check and ensure that the Census data was of high quality to enhance its credibility and increase its usage. Various quality controls were implemented to ensure relevance, timeliness, accuracy, coherence and proper data interpretation. Other activities undertaken to enhance quality included the demarcation of the country into small enumeration areas to ensure comprehensive coverage; the development of structured Census questionnaires after consultation with government ministries, university expertise and international partners; the preparation of detailed supervisors' and enumerators' instruction manuals to guide field staff during enumeration; the undertaking of comprehensive publicity and advocacy programmes to ensure full Government support and cooperation from the general public; the testing of questionnaires and other procedures; the provision of adequate training and undertaking of intensive supervision using four supervisory layers; the editing of questionnaires at field level; establishing proper mechanisms which ensured that all completed questionnaires were properly accounted for; ensuring intensive verification, validating all information and error corrections; and developing capacity in data processing with support from the international community.

U.S. Census BlocksThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Census Blocks in the United States. A brief description of Census Blocks, per USCB, is that "Census blocks are statistical areas bounded by visible features such as roads, streams, and railroad tracks, and by nonvisible boundaries such as property lines, city, township, school district, county limits and short line-of-sight extensions of roads." Also, "the smallest level of geography you can get basic demographic data for, such as total population by age, sex, and race."Census Block 1007Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Census Blocks) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 69 (Series Information for 2020 Census Block State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (U.S. Census Blocks - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: What are census blocksFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by modified ZIP Code Tabulation Area (ZCTA) of residence. Modified ZCTA reflects the first non-missing address within NYC for each person reported with an antibody test result. This unit of geography is similar to ZIP codes but combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. It can be challenging to map data that are reported by ZIP Code. A ZIP Code doesn’t refer to an area, but rather a collection of points that make up a mail delivery route. Furthermore, there are some buildings that have their own ZIP Code, and some non-residential areas with ZIP Codes. To deal with the challenges of ZIP Codes, the Health Department uses ZCTAs which solidify ZIP codes into units of area. Often, data reported by ZIP code are actually mapped by ZCTA. The ZCTA geography was developed by the U.S. Census Bureau. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-modzcta.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis wi

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 National Family Health Survey 2019-21 (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India, each state/union territory (UT), and for 707 districts.

The primary objective of the 2019-21 round of National Family Health Surveys is to provide essential data on health and family welfare, as well as data on emerging issues in these areas, such as levels of fertility, infant and child mortality, maternal and child health, and other health and family welfare indicators by background characteristics at the national and state levels. Similar to NFHS-4, NFHS-5 also provides information on several emerging issues including perinatal mortality, high-risk sexual behaviour, safe injections, tuberculosis, noncommunicable diseases, and the use of emergency contraception.

The information collected through NFHS-5 is intended to assist policymakers and programme managers in setting benchmarks and examining progress over time in India’s health sector. Besides providing evidence on the effectiveness of ongoing programmes, NFHS-5 data will help to identify the need for new programmes in specific health areas.

The clinical, anthropometric, and biochemical (CAB) component of NFHS-5 is designed to provide vital estimates of the prevalence of malnutrition, anaemia, hypertension, high blood glucose levels, and waist and hip circumference, Vitamin D3, HbA1c, and malaria parasites through a series of biomarker tests and measurements.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15 to 54

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, all men age 15-54, and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A uniform sample design, which is representative at the national, state/union territory, and district level, was adopted in each round of the survey. Each district is stratified into urban and rural areas. Each rural stratum is sub-stratified into smaller substrata which are created considering the village population and the percentage of the population belonging to scheduled castes and scheduled tribes (SC/ST). Within each explicit rural sampling stratum, a sample of villages was selected as Primary Sampling Units (PSUs); before the PSU selection, PSUs were sorted according to the literacy rate of women age 6+ years. Within each urban sampling stratum, a sample of Census Enumeration Blocks (CEBs) was selected as PSUs. Before the PSU selection, PSUs were sorted according to the percentage of SC/ST population. In the second stage of selection, a fixed number of 22 households per cluster was selected with an equal probability systematic selection from a newly created list of households in the selected PSUs. The list of households was created as a result of the mapping and household listing operation conducted in each selected PSU before the household selection in the second stage. In all, 30,456 Primary Sampling Units (PSUs) were selected across the country in NFHS-5 drawn from 707 districts as on March 31st 2017, of which fieldwork was completed in 30,198 PSUs.

For further details on sample design, see Section 1.2 of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four survey schedules/questionnaires: Household, Woman, Man, and Biomarker were canvassed in 18 local languages using Computer Assisted Personal Interviewing (CAPI).

Cleaning operations

Electronic data collected in the 2019-21 National Family Health Survey were received on a daily basis via the SyncCloud system at the International Institute for Population Sciences, where the data were stored on a password-protected computer. Secondary editing of the data, which required resolution of computer-identified inconsistencies and coding of open-ended questions, was conducted in the field by the Field Agencies and at the Field Agencies central office, and IIPS checked the secondary edits before the dataset was finalized.

Field-check tables were produced by IIPS and the Field Agencies on a regular basis to identify certain types of errors that might have occurred in eliciting information and recording question responses. Information from the field-check tables on the performance of each fieldwork team and individual investigator was promptly shared with the Field Agencies during the fieldwork so that the performance of the teams could be improved, if required.

Response rate

A total of 664,972 households were selected for the sample, of which 653,144 were occupied. Among the occupied households, 636,699 were successfully interviewed, for a response rate of 98 percent.

In the interviewed households, 747,176 eligible women age 15-49 were identified for individual women’s interviews. Interviews were completed with 724,115 women, for a response rate of 97 percent. In all, there were 111,179 eligible men age 15-54 in households selected for the state module. Interviews were completed with 101,839 men, for a response rate of 92 percent.

About this dataset

Context

The dataset tabulates the population of State Line by race. It includes the population of State Line across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of State Line across relevant racial categories.

Key observations

The percent distribution of State Line population by race (across all racial categories recognized by the U.S. Census Bureau): 100% are white.

Content

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

Racial categories include:

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

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the State Line
• Population: The population of the racial category (excluding ethnicity) in the State Line is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of State Line total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State Line Population by Race & Ethnicity. You can refer the same here

Biological data from the IPY-CAML voyage (TAN0802) by the R/V Tangaroa. The TAN0802 voyage departed from Wellington, New Zealand on Jan 26th 2008 and returned to Wellington, New Zealand, on Mar 21st 2008. The survey was concentrated mainly on the Ross Sea and the waters around Scott and the Balleny Islands. Biological data was collected using a variety of gear, including: bottom trawls, beam trawls, epibenthic sleds, Van Veen grabs, Rosette water bottle and MOCNESS tows.The voyage resulted from a announcement by the Prime Minister in 2007 for new government funding for a New Zealand Census of Antarctic Marine Life (CAML) project to support biodiversity studies in the Southern Ocean and Ross Sea Region as part of the governments Ocean Survey 20/20 (OS2020) programme and the International Polar Year (IPY) activities. The overall Project includes two phases a) data collection voyage and b) data analysis and reporting. The recognition of International Polar Year (IPY) throughout the globe from March 2007 to March 2009 has provided the impetus for a large international effort to conduct collaborative research both in Antarctica and the Arctic, spanning two summer seasons in both regions. New Zealand is participating in a range of both terrestrial and marine projects for IPY that are important, not only nationally, but also in the international science arena.The Census of Antarctic Marine Life (CAML) is a major multi-national IPY Programme that New Zealand’s project is part of. This project forms a particularly important component of the international CAML Programme, as it will not only be part of the circum-polar national surveys, but will provide an opportunity to compare fauna and ecosystems from opposite sides of the globe including the two most significant shelf areas in Antarctica-the Ross Sea and the Weddell Sea.For the IPY-CAML project, the Ross Sea region was subdivided into three survey areas, each of which was stratified by depth, and had a different balance of core versus additional stations to reflect the multiple objectives of the project. The core stations allowed broad-scale comparisons between areas on a regional scale. Their distribution within depth strata of each survey area also allowed comparisons to be made within each area. The additional stations were designed to support objectives that are specifically relevant to high priority objectives within a particular area.Biological data was collected using a variety of gear, including: bottom trawls, beam trawls, epibenthic sleds, Van Veen grabs, Rosette water bottle and MOCNESS tows.The scientific names have been mapped to the World Register of Marine Species (WoRMS), using the online taxon match tool. All sampling locations have been plotted on a map to perform a visual check. The most important check would be to see if all sampling locations are (1) in the marine and/or brackish environment and (2) within the described sampling area.The project is a major collaboration between Land Information New Zealand (LINZ), Ministry of Fisheries, Ministry of Foreign Affairs and Trade, Antarctica New Zealand, Te Papa, the National Institute of Water and Atmospheric Research (NIWA), and New Zealand universities. The voyage also connects to New Zealand’s whole-of-government, Ocean Survey 20/20 programme, where it is one of several voyages proposed over a number of years. In addition, there is international collaboration with Italian, USA, Canadian, and Australian scientists. The biological components sampled during the voyage included viruses, bacteria, plankton, benthic fauna, cephalopods, fish and top predators. Analyses to describe the biodiversity in the Ross Sea and contribute to the Census of Antarctic Marine Life Programme explored measures of endemism, species richness, complexity, taxonomic distinctness and genetic diversity throughout the region. The relationships between the biological patterns observed and different environmental gradients included the water column from surface to seabed at different bottom depths, substrate type, bottom slope, water mass, ice cover and ice-berg scour. To understand how the ecosystem functions dynamically, studies of feeding patterns was carried out across as many biological groups as possible and the information used to improve ecosystem modelling of the Ross Sea region. Understanding ecosystem function and the effects of toothfish fishing in the Ross Sea is a key requirement for fisheries management under CCAMLR. Ocean acidity and other water chemistry attributes are critical pieces of information that were collected throughout the voyage to not only characterise the hydrological setting of the region, but to also provide baseline measures for monitoring environmental change. Other potential indicators investigated for their utility in longterm ecosystem monitoring. The data collected will provide a host of other new information. For example, habitat and biological mapping will greatly improve progress on “bioregionalisation” of the area. Many hundreds of species will be taxonomically described and genetically “barcoded” to facilitate species identification in the region. The project will allow New Zealand and other international collaborators to explore concepts of evolution and species divergence in the Southern Ocean. Seamounts east of the Balleny Islands will be sampled to provide a comparison with previous surveys at the Balleny Islands and improve understanding of the role that seamounts and island outcrops play in marine biodiversity and faunal refuges in the Southern Ocean.Citation: Ocean Survey 20/20 (2013). International Polar Year and Census of Antarctic Marine Life Ross Sea voyage (TAN0802) biodiversity data. Southwestern Pacific OBIS, National Institute of Water and Atmospheric Research, Wellington, New Zealand, 8748 record.Online: https://nzobisipt.niwa.co.nz/resource?r=mbis_caml Released on Dec 12, 2013.

Abstract copyright UK Data Service and data collection copyright owner.

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

The Great Britain Historical GIS Project has also produced digitised boundary data, which can be obtained from the UK Data Service Census Support service. Further information is available at census.ukdataservice.ac.uk

Main Topics:

The Great Britain Historical Database is a large database of British nineteenth and twentieth-century statistics. Where practical the referencing of spatial units has been integrated, data for different dates have been assembled into single tables.

The Great Britain Historical Database currently contains :

• Statistics from the 1861 Census and the Registrar General's reports, 1851-1861
  
• Employment statistics from the census, 1841-1931
  
• Demographic statistics from the census, 1841-1931
  
• Mortality statistics from the Registrar General's reports, 1861-1920
  
• Marriage statistics from the Registrar General's reports, 1841-1870
  
• Trade union statistics for the Amalgamated Society of Engineers (ASE), 1851-1918
  
• Trade union statistics for the Amalgamated Society of Carpenters and Joiners (ASCJ), 1863-1912
  
• Official poor law statistics, 1859-1915 and 1919-1939
  
• Wage statistics, 1845-1906
  
• Hours of work statistics, 1900-1913
  
• Small debt statistics from county courts, 1847-1913 and 1938
  

There are five tables in this part of the Great Britain Historical Database :

Rd_pop holds population totals for all registration districts in England and Wales for each census from 1841 to 1911.

Pop_chan holds details of population changes between censuses for all registration districts in England and Wales for each inter-censal period from 1851-1861 to 1901-1911.

Age_sex holds the number of males and females in 5 year age groups for all registration districts in England and Wales for each census from 1851 to 1911, and for all local government districts for each census from 1921 to 1931.

Age_1901 holds a full transcript of the number of males and females in 5 year age groups for all registration districts in England and Wales for the 1901 census with greater detail for ages 13 to 20.

Rd_gaz converts the names of registration districts which appear in the database into the forms used in the GIS.

Rd_gis holds the names and counties of registration districts as they appear in the GIS, and is used for checking names and constructing rd_gaz.

Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by ZIP Code Tabulation Area (ZCTA) neighborhood poverty group. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-poverty.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Neighborhood-level poverty groups were classified in a manner consistent with Health Department practices to describe and monitor disparities in health in NYC. Neighborhood poverty measures are defined as the percentage of people earning below the Federal Poverty Threshold (FPT) within a ZCTA. The standard cut-points for defining categories of neighborhood-level poverty in NYC are: • Low: <10% of residents in ZCTA living below the FPT • Medium: 10% to <20% • High: 20% to <30% • Very high: ≥30% residents living below the FPT The ZCTAs used for classification reflect the first non-missing address within NYC for each person reported with an antibody test result. Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Rates for poverty were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certain

Abstract

This dataset includes all individuals from the 1860 US census.

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

This dataset was developed through a collaboration between the Minnesota Population Center and the Church of Jesus Christ of Latter-Day Saints. The data contain demographic variables, economic variables, migration variables and race variables. Unlike more recent census datasets, pre-1900 census datasets only contain individual level characteristics and no household or family characteristics, but household and family identifiers do exist.

The official enumeration day of the 1860 census was 1 June 1860. The main goal of an early census like the 1860 U.S. census was to allow Congress to determine the collection of taxes and the appropriation of seats in the House of Representatives. Each district was assigned a U.S. Marshall who organized other marshals to administer the census. These enumerators visited households and recorder names of every person, along with their age, sex, color, profession, occupation, value of real estate, place of birth, parental foreign birth, marriage, literacy, and whether deaf, dumb, blind, insane or “idiotic”.

Sources: Szucs, L.D. and Hargreaves Luebking, S. (1997). Research in Census Records, The Source: A Guidebook of American Genealogy. Ancestry Incorporated, Salt Lake City, UT Dollarhide, W.(2000). The Census Book: A Genealogist’s Guide to Federal Census Facts, Schedules and Indexes. Heritage Quest, Bountiful, UT

Abstract

A national population census may be thought of as a “stock-taking” of the whole country, particularly of its most precious resource, its people. It is not just a count of people. Information is needed on the structure of the population for instance, the number of males and females and their ages together with a variety of other characteristics related to their civil and economic status.

Information on education, migration, work and employment are needed also for measuring the progress made over the last ten years in educating the population, in using their skills and developing the economy so that the quality of life in Tonga is improved and the national development objectives achieved. Successful national planning for the future needs of children for schools and trained teachers, of young people for employment and of older people for a rewarding retirement, is related to the availability of accurate information about the numbers and characteristics of these groups now and in the projected future.

Geographic coverage

National coverage. The Population Census covers the whole of the Kingdom of Tonga, which includes the 5 Divisions and both Urban and Rural Areas.

Analysis unit

Individuals and Households

Universe

The 1996 census covered all households and individuals in the selected areas excluding institutions and diplomats and non residents

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed in English, but enumerators were specifically trained to be able to clearly translate these questions into Tongan.

The questionnaires were designed into 4 parts which are:

1. Individual
2. Economic Activity/Labour Force
3. Fertility
4. Household & Housing Characteristics

Cleaning operations

On arrival at the Statistics Department, census forms were checked and responses coded for entry to computers.

Data entry was performed by three staff members of the Statistics Department namely Mrs ‘Ana P. Fifita, Computer Operator Grade1, Miss Tupou Tausisi, Temporary Computer Assistant, and Mr ‘Olini Sapoi, Temporary Computer Assistant. Mrs ‘Ana P. Fifita also did the editing of the Census database before the final editing programme was run.

Data entry, editing and tabulation of Census results were done using IMPS, an Integrated Microcomputer Processing System developed by the US Bureau of the Census. The set of General Tables were produced using IMPS and imported to Excel for final formatting. The programme for final editing of the census database was written by Dr Michael Levin from the US Bureau of the Census during his two days visit to the Statistics Department.

Abstract

The Agricultural Census aims in general to establish an updated, detailed and accurate holdings database to assist in planning and policy making at all levels related to the agricultural sector. It also aims in specific to provide data on the structure of agriculture, especially for small administrative and geographical units, rare items, and to enable detailed cross-tabulations, and to provide data that can be used as a benchmark for reconciliation of current agricultural statistics; and for setting estimates for subsequent years, in addition to provide frames for agricultural sampling surveys.

Geographic coverage

The census also covered all geographical levels in the West Bank and Gaza Strip, so that: 1. Implementation of a comprehensive listing in Gaza Strip that enumeration areas represent more than 5% of households that practice agricultural activity, according to the Population, Housing and Establishments Census, 2017 data. 2. Visiting the households that practiced agricultural activity according to data of Population, Housing and Establishments Census, 2017 in the enumerated areas, where the percentage of households that practiced an agricultural activity is 1-4%. 3. Implementation of a comprehensive listing in the West Bank for all localities except camps and city centers in the following governorates (Nablus, Ramallah & Al-Bireh, Hebron and J2 in Jerusalem Governorate). 4. Implementation of a comprehensive listing in the enumeration areas of camps and city centers in the following governorates (Nablus, Ramallah, Al-Bireh, Hebron and J2 of Jerusalem Governorate), for households that practiced agricultural activity according to data of Population, Housing and Establishments Census 2017, more than 5%, and visiting the households that practiced agricultural activity according to data of Population, Housing and Establishments Census 2017 in the enumerated areas, where the percentage of households that practiced an agricultural activity is 1-4% in the same locality mentioned above. 5. About Jerusalem J1, a different methodology is applied in two phases. In the first phase, research and investigation are carried out in cooperation with responsible and dignitaries in Jerusalem J1 on agricultural holdings and holders, and in the second phase, enumeration of the holdings that were monitored in the first phase.

Analysis unit

Agricultural Holding

Universe

Includes agricultural holdings in Palestine in 2021

Kind of data

Census/enumeration data [cen]

Sampling procedure

The frame of the Agriculture Census includes a complete record of households and non-household agricultural holdings, where all households are enumerated and the household agricultural holdings are identified, in addition to a list of non-households holdings that is obtained by listing all buildings as well as a list from the Ministry of Agriculture which includes cooperative societies/charity societies, companies, and government and private holdings…etc.

The census also covered all geographical levels in the West Bank and Gaza Strip, so that: 1. Implementation of a comprehensive listing in Gaza Strip that enumeration areas represent more than 5% of households that practice agricultural activity, according to the Population, Housing and Establishments Census, 2017 data. 2. Visiting the households that practiced agricultural activity according to data of Population, Housing and Establishments Census, 2017 in the enumerated areas, where the percentage of households that practiced an agricultural activity is 1-4%. 3. Implementation of a comprehensive listing in the West Bank for all localities except camps and city centers in the following governorates (Nablus, Ramallah & Al-Bireh, Hebron and J2 in Jerusalem Governorate). 4. Implementation of a comprehensive listing in the enumeration areas of camps and city centers in the following governorates (Nablus, Ramallah, Al-Bireh, Hebron and J2 of Jerusalem Governorate), for households that practiced agricultural activity according to data of Population, Housing and Establishments Census 2017, more than 5%, and visiting the households that practiced agricultural activity according to data of Population, Housing and Establishments Census 2017 in the enumerated areas, where the percentage of households that practiced an agricultural activity is 1-4% in the same locality mentioned above. 5. About Jerusalem J1, a different methodology is applied in two phases. In the first phase, research and investigation are carried out in cooperation with responsible and dignitaries in Jerusalem J1 on agricultural holdings and holders, and in the second phase, enumeration of the holdings that were monitored in the first phase.

Sampling deviation

Not applicable

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Computerized program

Cleaning operations

Post enumeration data processing phase was limited to final examination and cleaning of Agricultural Census databases, with documentation of examinations on all topics of Agricultural Census 2021 questions. Data processing phase focused on the following: 1. Checking the allowed transfers and values. 2. Checking the consistency between different questions of the census questionnaire based on logical relationships. 3. Checking on the basis of relations between certain questions so that a list of non-identical cases was extracted, reviewed and identified the source of the error case by case, and if such errors were immediately modified and corrected based on the source of the error3. Checking on the basis of relations between certain questions so that a list of non-identical cases was extracted, reviewed and identified the source of the error case by case, and if such errors were immediately modified and corrected based on the source of the error.

Response rate

Not Applicable.

Sampling error estimates

The sampling errors occur during the sample-based surveys but not in censuses as it is a comprehensive inventory of all agricultural holdings. These errors are easy to measure with the error point estimate also, since it is considered as an error in the sample.

Data appraisal

The non-sampling errors occur at any stage during the implementation of censuses and surveys. Therefore, it is necessary to provide for a data quality control system to ensure maximum accuracy. Many of these stages were used during the agriculture census planning and implementation where are-interview was carried out as follows:

• There are two models that were used to collect data and were uploaded to tablets. The first model is to enumerate households in all enumeration areas; in which the percentage of households that practiced an agricultural activity (according to the data of the Population, Housing and Establishments Census, 2017) is 5% or more, and the second model was used if the household had agricultural holdings.

• The enumerator visited Palestinian households in the enumeration areas in which the percentage of households that practiced agricultural activity (according to the data of the Population, Housing and Establishments Census, 2017) is less than 5%, so that the inventory model and the model prepared for agricultural holdings were if the tenure conditions were met.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

For the second edition of the study, the depositor supplied a re-weighted version of the data file. The re-weighting has been done to bring LFS data in line with the population estimates from the 2001 Census.

This dataset has been retired as of February 17, 2023. This dataset will be kept for historical purposes, but will no longer be updated. Similar data are available on the state’s open data portal: https://data.chhs.ca.gov/dataset/covid-19-time-series-metrics-by-county-and-state.

A. DATASET DESCRIPTION This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2019 American Community Survey (ACS) 5-year population estimates are included to calculate the cumulative rate per 10,000 residents.

Dataset covers cases going back to March 18th, 2020 when the first person in Marin County tested positive for COVID-19. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily.

COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated.

Geographic areas summarized are: 1. City, Town, or Community Area 2. Census Tracts 3. Census ZIP Code Tabulation Areas (ZCTAs)

B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by Marin County HHS. Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date.

The 2019 ACS estimates for population provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date).

C. UPDATE PROCESS Geographic analysis is scripted by Marin HHS staff and synced to this dataset each day.

D. HOW TO USE THIS DATASET This dataset can be used to track the spread of COVID-19 throughout Marin County in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date.

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. For example if a zip code did not have 10 cumulative cases until June 1, 2020 that location will not be included in the dataset until June 1. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. 3. Cases are dropped altogether for areas where acs_population < 1000. Some adjacent geographic areas may be combined until the ACS population exceeds 1,000 to still provide information for these regions.

Note: 14-day case rate or 30-day case rate where the counts are lower than 20 may be unstable. We advise caution in interpreting rates at these small numbers.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes.