Abstract

The 2000 Republic of Palau Census of Population and Housing was the second census collected and processed entirely by the republic itself. This monograph provides analyses of data from the most recent census of Palau for decision makers in the United States and Palau to understand current socioeconomic conditions. The 2005 Census of Population and Housing collected a wide range of information on the characteristics of the population including demographics, educational attainments, employment status, fertility, housing characteristics, housing characteristics and many others.

Geographic coverage

National

Analysis unit

• Household;
• Individual.

Universe

The 1990, 1995 and 2000 censuses were all modified de jure censuses, counting people and recording selected characteristics of each individual according to his or her usual place of residence as of census day. Data were collected for each enumeration district - the households and population in each enumerator assignment - and these enumeration districts were then collected into hamlets in Koror, and the 16 States of Palau.

Kind of data

Census/enumeration data [cen]

Sampling procedure

No sampling - whole universe covered

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000 censuses of Palau employed a modified list-enumerate procedure, also known as door-to-door enumeration. Beginning in mid-April 2000, enumerators began visiting each housing unit and conducted personal interviews, recording the information collected on the single questionnaire that contained all census questions. Follow-up enumerators visited all addresses for which questionnaires were missing to obtain the information required for the census.

Cleaning operations

The completed questionnaires were checked for completeness and consistency of responses, and then brought to OPS for processing. After checking in the questionnaires, OPS staff coded write-in responses (e.g., ethnicity or race, relationship, language). Then data entry clerks keyed all the questionnaire responses. The OPS brought the keyed data to the U.S. Census Bureau headquarters near Washington, DC, where OPS and Bureau staff edited the data using the Consistency and Correction (CONCOR) software package prior to generating tabulations using the Census Tabulation System (CENTS) package. Both packages were developed at the Census Bureau's International Programs Center (IPC) as part of the Integrated Microcomputer Processing System (IMPS).

The goal of census data processing is to produce a set of data that described the population as clearly and accurately as possible. To meet this objective, crew leaders reviewed and edited questionnaires during field data collection to ensure consistency, completeness, and acceptability. Census clerks also reviewed questionnaires for omissions, certain inconsistencies, and population coverage. Census personnel conducted a telephone or personal visit follow-up to obtain missing information. The follow-ups considered potential coverage errors as well as questionnaires with omissions or inconsistencies beyond the completeness and quality tolerances specified in the review procedures.

Following field operations, census staff assigned remaining incomplete information and corrected inconsistent information on the questionnaires using imputation procedures during the final automated edit of the data. The use of allocations, or computer assignments of acceptable data, occurred most often when an entry for a given item was lacking or when the information reported for a person or housing unit on an item was inconsistent with other information for that same person or housing unit. In all of Palau’s censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable data in place of blanks or unacceptable entries enhanced the usefulness of the data.

Sampling error estimates

Human and machine-related errors occur in any large-scale statistical operation. Researchers generally refer to these problems as non-sampling errors. These errors include the failure to enumerate every household or every person in a population, failure to obtain all required information from residents, collection of incorrect or inconsistent information, and incorrect recording of information. In addition, errors can occur during the field review of the enumerators' work, during clerical handling of the census questionnaires, or during the electronic processing of the questionnaires. To reduce various types of non-sampling errors, Census office personnel used several techniques during planning, data collection, and data processing activities. Quality assurance methods were used throughout the data collection and processing phases of the census to improve the quality of the data.

Census staff implemented several coverage improvement programs during the development of census enumeration and processing strategies to minimize under-coverage of the population and housing units. A quality assurance program improved coverage in each census. Telephone and personal visit follow-ups also helped improve coverage. Computer and clerical edits emphasized improving the quality and consistency of the data. Local officials participated in post-census local reviews. Census enumerators conducted additional re-canvassing where appropriate.

This data collection contains the Census Software Package (CENSPAC), a generalized data retrieval system that the Census Bureau developed for use with its public use statistical data files. CENSPAC primarily provides processing capabilities for summary data files, but it also has some features that are applicable to microdata files. The actual software provides sample JCL for system installation, programs for system reconfiguration, source code for CENSPAC, and machine-readable data dictionaries for STF 1, STF 2, STF 3, and STF 4.

Abstract

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation.The census plays an essential role in public administration. The results are used to ensure: • equity in distribution of government services • distributing and allocating government funds among various regions and districts for education and health services • delineating electoral districts at national and local levels, and • measuring the impact of industrial development, to name a few The census also provides the benchmark for all surveys conducted by the national statistical office. Without the sampling frame derived from the census, the national statistical system would face difficulties in providing reliable official statistics for use by government and the public. Census also provides information on small areas and population groups with minimum sampling errors. This is important, for example, in planning the location of a school or clinic. Census information is also invaluable for use in the private sector for activities such as business planning and market analyses. The information is used as a benchmark in research and analysis.

Census 2011 was the third democratic census to be conducted in South Africa. Census 2011 specific objectives included: - To provide statistics on population, demographic, social, economic and housing characteristics; - To provide a base for the selection of a new sampling frame; - To provide data at lowest geographical level; and - To provide a primary base for the mid-year projections.

Geographic coverage

National

Analysis unit

Households, Individuals

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

About the Questionnaire : Much emphasis has been placed on the need for a population census to help government direct its development programmes, but less has been written about how the census questionnaire is compiled. The main focus of a population and housing census is to take stock and produce a total count of the population without omission or duplication. Another major focus is to be able to provide accurate demographic and socio-economic characteristics pertaining to each individual enumerated. Apart from individuals, the focus is on collecting accurate data on housing characteristics and services.A population and housing census provides data needed to facilitate informed decision-making as far as policy formulation and implementation are concerned, as well as to monitor and evaluate their programmes at the smallest area level possible. It is therefore important that Statistics South Africa collects statistical data that comply with the United Nations recommendations and other relevant stakeholder needs.

The United Nations underscores the following factors in determining the selection of topics to be investigated in population censuses: a) The needs of a broad range of data users in the country; b) Achievement of the maximum degree of international comparability, both within regions and on a worldwide basis; c) The probable willingness and ability of the public to give adequate information on the topics; and d) The total national resources available for conducting a census.

In addition, the UN stipulates that census-takers should avoid collecting information that is no longer required simply because it was traditionally collected in the past, but rather focus on key demographic, social and socio-economic variables.It becomes necessary, therefore, in consultation with a broad range of users of census data, to review periodically the topics traditionally investigated and to re-evaluate the need for the series to which they contribute, particularly in the light of new data needs and alternative data sources that may have become available for investigating topics formerly covered in the population census. It was against this background that Statistics South Africa conducted user consultations in 2008 after the release of some of the Community Survey products. However, some groundwork in relation to core questions recommended by all countries in Africa has been done. In line with users' meetings, the crucial demands of the Millennium Development Goals (MDGs) should also be met. It is also imperative that Stats SA meet the demands of the users that require small area data.

Accuracy of data depends on a well-designed questionnaire that is short and to the point. The interview to complete the questionnaire should not take longer than 18 minutes per household. Accuracy also depends on the diligence of the enumerator and honesty of the respondent.On the other hand, disadvantaged populations, owing to their small numbers, are best covered in the census and not in household sample surveys.Variables such as employment/unemployment, religion, income, and language are more accurately covered in household surveys than in censuses.Users'/stakeholders' input in terms of providing information in the planning phase of the census is crucial in making it a success. However, the information provided should be within the scope of the census.

1. The Household Questionnaire is divided into the following sections:
2. Household identification particulars

3. Individual particulars Section A: Demographics Section B: Migration Section C: General Health and Functioning Section D: Parental Survival and Income Section E: Education Section F: Employment Section G: Fertility (Women 12-50 Years Listed) Section H: Housing, Household Goods and Services and Agricultural Activities Section I: Mortality in the Last 12 Months The Household Questionnaire is available in Afrikaans; English; isiZulu; IsiNdebele; Sepedi; SeSotho; SiSwati;Tshivenda;Xitsonga

4. The Transient and Tourist Hotel Questionnaire (English) is divided into the following sections:

5. Name, Age, Gender, Date of Birth, Marital Status, Population Group, Country of birth, Citizenship, Province.

6. The Questionnaire for Institutions (English) is divided into the following sections:

7. Particulars of the institution

8. Availability of piped water for the institution

9. Main source of water for domestic use

10. Main type of toilet facility

11. Type of energy/fuel used for cooking, heating and lighting at the institution

12. Disposal of refuse or rubbish

13. Asset ownership (TV, Radio, Landline telephone, Refrigerator, Internet facilities)

14. List of persons in the institution on census night (name, date of birth, sex, population group, marital status, barcode number)

15. The Post Enumeration Survey Questionnaire (English)

These questionnaires are provided as external resources.

Cleaning operations

Data editing and validation system The execution of each phase of Census operations introduces some form of errors in Census data. Despite quality assurance methodologies embedded in all the phases; data collection, data capturing (both manual and automated), coding, and editing, a number of errors creep in and distort the collected information. To promote consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as invalid values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on defined rules (specifications).

The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing specifications and finalisation of specifications for the main Census.

Editing team The Census 2011 editing team was drawn from various divisions of the organisation based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors. Census 2011 editing team was drawn from various divisions of the organization based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors.

The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and skipping instructions. Editing of such complex, interlinked data items required application of a combination of editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset. CSPro software was used to resolve content related errors. The strategy used for Census 2011 data editing was implementation of automated error detection and correction with minimal changes. Combinations of logical and dynamic imputation/editing were used. Logical imputations were preferred, and in many cases substantial effort was undertaken to deduce a consistent value based on the rest of the household’s information. To profile the extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited dataset. Imputation flags values include the following: 0 no imputation was performed; raw data were preserved 1 Logical editing was performed, raw data were blank 2 logical editing was performed, raw data were not blank 3 hot-deck imputation was performed, raw data were blank 4 hot-deck imputation was performed, raw data were not blank

Data appraisal

Independent monitoring and evaluation of Census field activities Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381 Monitoring

Abstract

The Population and Housing Census 2000 was prepared and conducted according to the recommendations of the United Nations Economic Commission for Europe and the Statistical Office of the European Communities (Eurostat), which guarantee that the census data are internationally comparable. Also the comparability with the data of previous censuses carried out in Estonia was taken into account. Census 2000 was carried out from March 31 to April 9.

The Statistical Office of Estonia was responsible for conducting the Census. The purpose of the Census was to collect data on the size, composition and distribution of the country's population and access housing stock and conditions. The moment of the Census was 00.00 on 31 March 2000; the data collected in the Census reflect the characteristics of housing and of the population as of the moment of the Census.

The content of the Census data and the data collection methods were developed in the Statistical Office in cooperation with the experts of different fields. Regulation of the Government of the Republic 5 March 1999 approved the Census questionnaires and Census rules.

Geographic coverage

The Census covered all country.

The Statistical Office of Estonia (SOE) launched the mapping programme for the 2000 Population and Housing Census in 1995. After completing the test areas the specifications for the digital Census maps were finalized. According to the Specification, 1:50 000 maps in rural areas and 1:5 000 maps in urban areas were drawn. The specification was optimized to create a cartographic basis for the Census planning (Census area (CA) delineation) and for the Census itself (maps for enumerators, maps for supervisors, etc.). The Census mapping process was outsourced from SOE. The work was done by two companies - one in urban, another in rural areas. The production methodology was different in urban and rural areas. In rural areas, paper maps of the 1989 Census were used as a base source material, digitized by the mapping company and updated by local governments. In urban areas, the existing maps and orthophotos were used as a base source and the maps were updated by the mapping company. For rural and urban areas the municipalities compiled household lists including the number of inhabitants in each building or apartment. The purpose of household lists was to provide information about the number of inhabitants for the delineation of enumeration areas (EA).

The borders of Census units were marked on digital Population Census maps and the maps were printed for Census purposes. SOE stores digital maps in urban areas in Mapinfo, in rural areas in ArcView software and household lists in Foxpro software. The Census maps were ready by December 1999. Digital Population Census maps with the registered borders of administrative and settlement units are the basis for presenting the Census results in a cartographic way and for the development of Census GIS.

Universe

The Census covered: - persons who were in the Republic of Estonia at the moment of the Census (March 31, at 00.00) (excluding the diplomatic staff of foreign diplomatic missions and consular posts and their family members and persons in active service in foreign army); - persons who resided in the Republic of Estonia but who were in foreign states temporarily for a term of up to one year; - diplomatic staff of diplomatic missions and consular posts of the Republic of Estonia and their family members, who were in a foreign state at the moment of the Census; - residential buildings and other buildings used for habitation, and apartments and other dwellings situated therein (excluding buildings of foreign diplomatic missions and consular posts and dwellings situated therein).

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

PHC 2000 was conducted using two types of questionnaires - the Personal Questionnaire containing 31 questions, and the Housing Questionnaire with 12 questions. The Census questionnaires collected personal, household information as well as dwelling data.

1. Personal data include: 1.1. first and surname; personal identification code; 1.2. person’s and his/her parents’ place of birth, person’s permanent place of residence and location at the Census moment, person’s permanent place of residence on 12 January 1989, year of arrival in Estonia, address of the place of work; 1.3. sex, date of birth, citizenship, ethnic nationality, mother tongue, knowledge of languages (answering the question is voluntary), marital status, number of children given birth to, mother’s age at the time of birth of the first child; 1.4. main sources of subsistence, length of working week in the week preceding the Census (number of hours worked), social status (in military service, not working, actively seeking work, ready to start work, student (pupil), pensioner, homemaker, not working for other reasons), name of the main place of work / main employer (answering the question is voluntary), economic activity of the main place of work, employment status at the main place of work (employee with stable contract, other employee, entrepreneur-employer, farmer with salaried employees, self-employed person, freelancer, farmer without salaried employees, contributing family workers in a family enterprise, farm, member of commercial association), occupation at main place of work, length of usual working week; 1.5. level of curriculum that the person has completed or studies currently, highest level of vocational or professional education completed, highest level of general education completed; 1.6. long-term disability or illness determined by the medical commission of experts; 1.7. religious affiliation and faith confessed (answering the question is voluntary).

2. Household data describe: 2.1. type of institution; 2.2. list of household members, relationship of each household member to the reference person, family relationships between the household members, permanent and temporary members of the household, duration of absence of a permanent household member in months, duration of presence of a temporary household member; 2.3. legal basis for the use of the dwelling; 2.4. the links between the household and agricultural activity.

3. Data on dwelling include: 3.1. type, form of ownership, total area, number of rooms, existence of a kitchen, plumbing and heating (water supply system, sewage disposal system, hot water, bath (shower), sauna, flush toilet, electricity, gas, central heating, electric heating); 3.2. address, type and period of construction of the building containing dwellings.

Cleaning operations

Two scanners were used for optical data entry. The application software for data processing were worked out in co-operation with the company AS AboBase Systems and based on Oracle tools. The scanning of the Census questionnaires was performed in 2000 from 10 May to 22 September. During that period 3,505,451 questionnaires were scanned. 135 operators who had passed the training were engaged in the data processing.

Data appraisal

For evaluating the coverage of the Census and the quality of the Census data, a post-enumeration sample survey was organized. It covered about 1% of the population and a stratified random sample of enumeration areas was drawn. The post-enumeration survey was carried out from 14 to 19 April 2000 in 50 enumeration areas. Comparison of the Census data and the data collected in the post-enumeration survey showed that the undercoverage of the Census was on an average 1.2%.

Abstract

The first Agricultural Census in Finland was conducted in 1910, and the tenth in 2010. Since Finland joined the EU in 1995, the Information Centre of the Ministry of Agriculture and Forestry (Tike) has been responsible for implementing Farm Structure Surveys. Data for the 2010 Agricultural Census was collected during autumn 2010 and winter 2011. This data covered 2010. All farms and horticultural enterprises in Finland fell under the scope of the census. The Agricultural Census and Survey on Agricultural Production Methods were carried out at the same time. Data were collected both electronically and via telephone interviews. The information was collected in five batches. Tike carried out its own data collection using data collection software, and also ran the telephone service for farmers that was used during electronic data collection. The contract for carrying out the actual telephone interviews was put out to tender. The winner, Taloustutkimus Oy, is an independent and unaffiliated Finnish market research company. Data verification began during the collection period, as checks were carried out in online forms and by the software used to enter data during telephone interviews. Although information was checked during collection, more thorough verification and processing were carried out once the data collection period had ended. Preliminary information was published on Matilda (Tike’s online information service) during autumn 2011 and spring 2012. The final versions of the Agricultural Census and Survey on Agricultural Production Methods were completed in April 2012.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit in the AC 2010 was the agricultural holding. Two types of holdings were distinguished: "farms" and "horticultural enterprises" that were "engaged in commercial agricultural or horticultural production". A farm is defined as a holding/business that has a utilized arable land area of at least 1 ha or at least one animal unit of livestock. The farms do not include horticultural enterprises that are solely engaged in greenhouse production. A horticultural enterprise is a holding engaged in horticultural production intended for sale (for example, greenhouse enterprises).

Kind of data

Census/enumeration data [cen]

Sampling procedure

a. Frame The sample frame for the Agricultural Census included all farms recorded in the 2009 Farm Register, all horticultural enterprises in the 2009 Horticultural Enterprise Register, and farms that were new applicants for farming subsidies in 2010. A large proportion of horticultural businesses in the Horticultural Enterprise Register are also farms. The sample frame included a total of 66,313 farms and horticultural enterprises. The registers used to form the sample frame (Farm Register, Horticultural Enterprise Register and IACS) are updated annually. A farm is only removed from the Farm Register and Horticultural Enterprise Register if it is certain that the farm has ceased its activities. Statistical surveys querying the available agricultural land and number of livestock are carried out for farms that do not apply for subsidies. The sample frame for the Survey on Agricultural Production Methods did not include the smallest farms, that is, those whose economic size was under EUR 1,200 according to 2009 data. These farms are either very small or do not actively engage in agricultural production. The sample frame for the Survey on Agricultural Production Methods therefore consisted of 63,219 farms and horticultural enterprises. The sample frames were very up-to-date: at the time of sampling, most data were approximately one year old. The information for new farms dated from spring 2010, as it was retrieved from the administrative register (IACS) on the basis of subsidy applications submitted in spring 2010. When the results of the survey were estimated, the sample frame was updated on the basis of 2010 register data. Consequently, overcoverage due to the inclusion of farms that had ceased operation did not pose a problem at the estimation stage. As the Farm Register, Horticultural Enterprise Register and IACS use the same farm code, these registers could be successfully consolidated into a sample frame.

b. Survey design The bulk of the information for the Agricultural Census was collected as an exhaustive survey. Some of the information obtained as part of the exhaustive survey (the geographical location of the farm, the area under different crops, the number of livestock, organic production, and questions and coordination data relating to rural development subsidies) was obtained from registers, while the rest (labour force, education and training, other business activities on farms, renewable energy, and some data on irrigation) was collected using either an online questionnaire or telephone interview. Data for the Survey on Agricultural Production Methods were collected as a sample survey. Questions covered arable and horticultural production, livestock production, and irrigation. A stratified sample was used. The sample frame was constructed using three variables: geographical location (20 municipalities), production sector (8 classes) and economic size (5 classes). After initial stratification, the small strata (which only contained a few farms) were combined. There were a total of 566 strata.

Mode of data collection

Computer Assisted Web Interview (CAWI)

Research instrument

There were two questionnaires: one for the CA and one for the SAPM. The questionnaires covered all 16 core items recommended in the WCA 2010.

Questionnaire:

Agricultural area utilised for shared farming or other modes Storage facilities for slurry - lagoon Irrigation method: Surface irrigation Beehives Landscape features - Linear elements Livestock

Cleaning operations

a. DATA PROCESSING AND ARCHIVING Specific checks were used in both the online forms and the software used to enter data from telephone interviews. The results were produced using SAS software. Variances for the SAPM were estimated using the CLAN software developed by Statistics Sweden. Missing information on farms and horticultural enterprises that did not respond to the AC was filled in using imputation methods. The imputation method used varied, depending on the amount of background information available for the variable in question. The most common imputation method was to fill in a missing data item using an average obtained from similar farms, or to substitute information on a missing farm with data from a similar farm that had filled in the questionnaire. Missing geographical coordinates were obtained using the farm's address details.

b. CENSUS DATA QUALITY Data verification began during the collection period, as checks were carried out in online forms and by the software used to enter data during telephone interviews. Although information was checked during collection, more thorough verification and processing procedures were carried out once the data collection period had ended. The values for the most important crop areas and livestock numbers from the SAPM differed very little from the values from the complete enumeration of all holdings, the differences being usually of less than 5 percent and well within the coefficients of variation of the sample.

Data appraisal

Preliminary census results on different topics were published in five batches, from June to December 2011, on the website of the Natural Resources Institute Finland (Luke). The results of the AC were published using Tilastolaari's dynamic reporting service. The final results of the AC were published in May 2012 and those of the SAPM in September 2012. Detailed census data can be found at on the Luke website.

Abstract

The main aim and objectives of the census is to provide benchmark statistics and a comprehensive profile of the population and households of Niue at a given time. This information obtained from the census is very crucial and useful in providing evidence to decision making and policy formulation for the Government, Business Community, Local Communities or Village Councils, Non Government Organisations of Niue and The International Communities who have an interest in Niue and its people.

Geographic coverage

National Coverage

Analysis unit

A Population and Household Census have the following units of analysis: - Households - Individuals/Persons - Members Overseas

Universe

All households in Niue and all persons in the household including those temporarily overseas and those absent for not more than 12 months.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not Applicable to a complete Enumeration Census.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionaire was published in English, a translated questionnaire was on hand when on demand by the respondent.

The questionnaire design differed slightly from the design of previous census questionnaires. As usual, government departments were asked to submit a list of questions on any specific topic they would like to add. Responses were not forthcoming in this census, although a few new questions were included.

There were two types of questionaires used in the census: the household questionaire and the individual questionnaire. An enumerator manual was prepared to assist the enumerators in their duties.

The questionnaire was pre-tested by the enumerators before they were to go out for field enumeration.

Cleaning operations

Census processing began as soon as questionaires were checked and coded. Forms were checked, edited and coded before being entered into the computer database.

Data processing was assisted by the Secretariat of the Pacific Community (SPC) using the computer software program CSPro for data entry and for generating tables. Tables were then exported to Excel for analysis.

Occupation and Industry were coded using the United Nations International Standard Classification of Occupation and International Standard Industrial Classification.

It is standard practice that as each area was completed the forms were first checked by the field supervisors for missing information and obvious inconsistencies. Omissions and errors identified at this stage were corrected by the enumerators.

The next stage was for the field supervisors to go through the completed forms again in the office to check in more detail for omissions and logical inconsistencies. Where they were found, the supervisors were responsible to take the necessary action.

Once the questionnaires had been thoroughly checked and edited, they were then coded in preparation for data processing.

Checking, editing and coding of the questionnaires in office were done after normal working hours as to ensure that the confidentiality of the survey is well observed.

Response rate

Complete enumeration of all households

Sampling error estimates

Not Applicable

Abstract

The programme for the World Census of Agriculture 2000 is the eighth in the series for promoting a global approach to agricultural census taking. The first and second programmes were sponsored by the International Institute for Agriculture (IITA) in 1930 and 1940. Subsequent ones up to 1990 were promoted by the Food and Agriculture Organization of the United Nations(FAO). FAO recommends that each country should conduct at least one agricultural census in each census programme decade and its programme for the World Census of Agriculture 2000 for instance corresponds to agricultural census to be undertaken during the decade 1996 to 2005. Many countries do not have sufficient resources for conducting an agricultural census. It therefore became an acceptable practice since 1960 to conduct agricultural census on sample basis for those countries lacking the resources required for a complete enumeration.

In Nigeria's case, a combination of complete enumeration and sample enumeration is adopted whereby the rural (peasant) holdings are covered on sample basis while the modern holdings are covered on complete enumeration. The project named “National Agricultural Sample Census” derives from this practice. Nigeria through the National Agricultural Sample Census (NASC) participated in the 1970's, 1980's, 1990's programmes of the World Census of Agriculture. Nigeria failed to conduct the Agricultural Census in 2003/2004 because of lack of funding. The NBS regular annual agriculture surveys since 1996 had been epileptic and many years of backlog of data set are still unprocessed. The baseline agricultural data is yet to be updated while the annual regular surveys suffered set back. There is an urgent need by the governments (Federal, State, LGA), sector agencies, FAO and other International Organizations to come together to undertake the agricultural census exercise which is long overdue. The conduct of 2006/2008 National Agricultural Sample Census Survey is now on course with the pilot exercise carried out in the third quarter of 2007.

The National Agricultural Sample Census (NASC) 2006/08 is imperative to the strengthening of the weak agricultural data in Nigeria. The project is phased into three sub-projects for ease of implementation; the Pilot Survey, Modern Agricultural Holding and the Main Census. It commenced in the third quarter of 2006 and to terminate in the first quarter of 2008. The pilot survey was implemented collaboratively by National Bureau of Statistics.

The main objective of the pilot survey was to test the adequacy of the survey instruments, equipments and administration of questionnaires, data processing arrangement and report writing. The pilot survey conducted in July 2007 covered the two NBS survey system-the National Integrated Survey of Households (NISH) and National Integrated Survey of Establishment (NISE). The survey instruments were designed to be applied using the two survey systems while the use of Geographic Positioning System (GPS) was introduced as additional new tool for implementing the project.

The Stakeholders workshop held at Kaduna on 21st-23rd May 2007 was one of the initial bench marks for the take off of the pilot survey. The pilot survey implementation started with the first level training (training of trainers) at the NBS headquarters between 13th - 15th June 2007. The second level training for all levels of field personnels was implemented at headquarters of the twelve (12) concerned states between 2nd - 6th July 2007. The field work of the pilot survey commenced on the 9th July and ended on the 13th of July 07. The IMPS and SPSS were the statistical packages used to develop the data entry programme.

Geographic coverage

State

Analysis unit

Household crop farmers

Universe

Crop farming household

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey was carried out in 12 states falling under 6 geo-political zones. 2 states were covered in each geo-political zone. 2 local government areas per selected state were studied. 2 Rural enumeration areas per local government area were covered and
4 Crop farming housing units were systematically selected and canvassed .

Sampling deviation

No deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The NASC crop questionnaire was divided into the following sections: - Holding identification - Holding characteristics - Access to land - Access to credit and funds used - Production input utilization, quantity and cost - Sources of inputs/equipment - Area harvested - Agric machinery - Production - Farm expenditure - Processing facilities - Storage facilities - Employment in agric. - Farm expenditure - Sales - Consumption - Market channels - Livestock farming - Fish farming

Cleaning operations

The data processing and analysis plan involved five main stages: training of data processing staff; manual editing and coding; development of data entry programme; data entry and editing and tabulation. Census and Surveys Processing System (CSPro) software were used for data entry, Statistical Package for Social Sciences (SPSS) and CSPro for editing and a combination of SPSS, Statistical Analysis Software (SAS) and EXCEL for table generation. The subject-matter specialists and computer personnel from the NBS and CBN implemented the data processing work. Tabulation Plans were equally developed by these officers for their areas and topics covered in the three-survey system used for the exercise. The data editing is in 2 phases namely manual editing before the data entry were done. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already entered data. The completed questionnaires were collected and edited manually (a) Office editing and coding were done by the editor using visual control of the questionnaire before data entry (b) Cspro was used to design the data entry template provided as external resource (c) Ten operator plus two suppervissor and two progammer were used (d) Ten machines were used for data entry (e) After data entry data entry supervisor runs fequency on each section to see that all the questionnaire were enterd

Response rate

The response rate at EA level was 100 percent, while 98.44 percent was achieved at crop farming housing units level

Sampling error estimates

No computation of sampling error

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data. There were two levels of supervision involving the supervisors at the first level, NBS State Officers and Zonal Controllers at second level and finally the NBS Headquarters staff constituting the second level supervision.

Abstract

The General Census of Agriculture and Livestock i.e. Recensement Général de l’Agriculture et du Cheptel (RGAC) is the result of a successful cooperation between the Government of Niger, the European Union, the World Bank and the FAO. The objective of this important statistical operation is to improve the quality of the census, specifically the effectiveness of agricultural and rural development programmes, for which there is a need for a renewed statistical information system capable of :

• Providing the data to better analyse constraints and target interventions accurately;
• Evaluating the impact and measure the performance of the actions carried out;
• Monitoring progress towards achieving the results assigned to the Rural Development Strategy (RDS) and the policy aimed at reducing poverty in Niger.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural (farm) household, defined as "a household where any of its members practices agriculture without being only an employee in agriculture".

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The CA 2004-2008 adopted a modular approach, with a core module and nine supplementary/thematic modules.

ii. Frame The main source of sampling frame consisted in the EAs from the Population Census (PC) 2001. Approximately 7 500 out of 8 000 EAs were included. The PC also provided the list of households involved in agricultural activities. In addition, the livestock frame included all known water points and transhumant routes.

iii.Complete and/or sample enumeration method(s) A complete enumeration of households within the 7 465 EAs from the PC which engaged in agricultural activity was done to identify agricultural households. The supplementary/thematic modules were conducted using sample enumeration.

iv. Sample design A stratified two-stage sampling design was used for the supplementary/thematic modules. The 36 departments served as strata. A sample was then drawn at the departmental level. The EAs were the PSUs and households were the SSUs, and were selected at EA level with an equal probability. According to the module, relevant samples of EAs were designed. In total, approximately 13 000 households were selected for the crop production module and 12 000 households for the animal production module.

Mode of data collection

Face-to-face [f2f]

Research instrument

The CA used 19 questionnaires for the core and supplementary/thematic modules. The questionnaires addressed topics such as: irrigated and rainfed crops, crop farm productivity, livestock numbers by type of animal, sedentary, nomadic, and transhumant livestock, livestock productivity, food security, support for farmers' organizations. The questionnaires used were:

Q1: Pre-census of agricultural households Q2: Inventory and measurement of pastoral settlements Q3: Inventory and measurement of plots of land Q4: Laying and harvesting the yield squares Q7: Survey of water points in dry season concentration areas Q8: Census of terminal crossing points of transhumance corridors Q9: Census of sedentary livestock Q9-2 : Complementary study of the livestock numbers in the avian population Q10: Census of transhumant livestock Q11: Nomadic livestock census Q11-2 :Complementary study of the nomadic livestock Q12: Census of horticultural production sites Q14: Estimated areas and horticultural production Q15: Inventory of markets Q17: Village questionnaire Q18: Household questionnaire Q19: Plot questionnaire

The CA questionnaires covered 11 of the 16 core items3 recommended for the WCA 2010 round.

Cleaning operations

1. DATA PROCESSING AND ARCHIVING Data entry, editing and imputation were undertaken using the CSPro software. The SPSS software was used for data analysis and Microsoft Excel was used for tabulation.

2. CENSUS DATA QUALITY Efforts were made to minimize non-sampling errors through thorough preparation of training materials and formation of census personnel, and by making quality consciousness an important part of the work of enumerators and supervisors.

This data collection is a component of Summary Tape File 3, which consists of four sets of data containing detailed tabulations of the nation's population and housing characteristics produced from the 1980 Census. The STF 3 files contain sample data inflated to represent the total United States population. The files also contain 100-percent counts and unweighted sample counts of persons and housing units. All files in the STF 3 series are identical, containing 321 substantive data variables organized in the form of 150 "tables," as well as standard geographic identification variables. Population items tabulated for each person include demographic data and information on schooling, ethnicity, labor force status, and children, as well as details on occupation and income. Housing items include size and condition of the housing unit as well as information on value, age, water, sewage and heating, vehicles, and monthly owner costs. Each dataset provides different geographic coverage. STF 3A provides summaries for the states or state equivalents, counties or county equivalents, minor civil divisions (MCDs) or census county divisions (CCDs), places or place segments within MCD/CCDs and remainders of MCD/CCDs, census tracts or block numbering areas and block groups or, for areas that are not block numbered, enumeration districts, places, and congressional districts. There are 52 files, one for each state, the District of Columbia, and Puerto Rico. The information in the file for Puerto Rico is similar to but not identical to the data for the 50 states and the District of Columbia. Thus, this file is documented in a separate codebook. The Census Bureau's machine-readable data dictionary for STF 3 is also available through CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: CENSUS SOFTWARE PACKAGE (CENSPAC) VERSION 3.2 WITH STF4 DATA DICTIONARIES (ICPSR 7789), the software package designed specifically by the Census Bureau for use with the 1980 Census data files.

The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File.

The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the redistricting data portion of the 2010 Demonstration Data Products Suite – Redistricting and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Redistricting Data (P.L.94-171) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).

The data includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census.

The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.

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.

Release Date: 2020-12-17.Release Schedule:.The data in this file come from the 2017 Economic Census. For more information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.Includes only establishments of firms with payroll...Data Items and Other Identifying Records:.Number of establishments.Sales, value of shipments, or revenue ($1,000).Sales on own account ($1,000).Purchases ($1,000).Total inventories, beginning of year ($1,000).Total inventories, end of year ($1,000).Cost of goods sold ($1,000).Gross margin ($1,000).Gross margin as percent of sales on own account (%)..Geography Coverage:.The data are shown for employer establishments at the U.S. level only. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown at the 2- through 7-digit and selected 8-digit 2017 NAICS code levels. For information about NAICS, see Economic Census: Technical Documentation: Economic Census Code Lists...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector42/EC1742MARGIN.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.

This dataset contains model-based census tract level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the Census tract 2022 boundary file in a GIS system to produce maps for 40 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

Abstract

Dataset quality **: Medium/high quality dataset, not quality checked or modified by the EIDC team

Census data plays a pivotal role in academic data research, particularly when exploring relationships between different demographic characteristics. The significance of this particular dataset lies in its ability to facilitate the merging of various datasets with basic census information, thereby streamlining the research process and eliminating the need for separate API calls.

The American Community Survey is an ongoing survey conducted by the U.S. Census Bureau, which provides detailed social, economic, and demographic data about the United States population. The ACS collects data continuously throughout the decade, gathering information from a sample of households across the country, covering a wide range of topics

Methodology

The Census Data Application Programming Interface (API) is an API that gives the public access to raw statistical data from various Census Bureau data programs.

We used this API to collect various demographic and socioeconomic variables from both the ACS and the Deccenial survey on different geographical levels:

ZCTAs:

ZIP Code Tabulation Areas (ZCTAs) are generalized areal representations of United States Postal Service (USPS) ZIP Code service areas. The USPS ZIP Codes identify the individual post office or metropolitan area delivery station associated with mailing addresses. USPS ZIP Codes are not areal features but a collection of mail delivery routes.

Census Tract:

Census Tracts are small, relatively permanent statistical subdivisions of a county or statistically equivalent entity that can be updated by local participants prior to each decennial census as part of the Census Bureau’s Participant Statistical Areas Program (PSAP).

Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. A census tract usually covers a contiguous area; however, the spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over a long time so that statistical comparisons can be made from census to census.

Block Groups:

Block groups (BGs) are the next level above census blocks in the geographic hierarchy (see Figure 2-1 in Chapter 2). A BG is a combination of census blocks that is a subdivision of a census tract or block numbering area (BNA). (A county or its statistically equivalent entity contains either census tracts or BNAs; it can not contain both.) A BG consists of all census blocks whose numbers begin with the same digit in a given census tract or BNA; for example, BG 3 includes all census blocks numbered in the 300s. The BG is the smallest geographic entity for which the decennial census tabulates and publishes sample data.

Census Blocks:

Census blocks, the smallest geographic area for which the Bureau of the Census collects and tabulates decennial census data, are formed by streets, roads, railroads, streams and other bodies of water, other visible physical and cultural features, and the legal boundaries shown on Census Bureau maps.

This data collection contains the Census Software Package (CENSPAC), a generalized data retrieval system that the Census Bureau developed for use with its public use statistical data files. CENSPAC primarily provides processing capabilities for summary data files, but it also has some features that are applicable to microdata files. The actual software provides sample JCL for system installation, programs for system reconfiguration, source code for CENSPAC, and machine-readable data dictionaries for STF 1, STF 2, STF 3, and STF 4. (Source: ICPSR, retrieved 06/15/2011)

This is the Municipal Ward Layer that is generated by Wisconsin Land Information Office's participation in statewide data collection. This map is automatically updated when a county uploads data to the LTSB GeoData Collector software platform. 2025 Current Program TimelineJanuary 1, 2025The 2025 Boundary and Annexation Survey Program begins. The LTSB GeoData Collector software platform will be used.Boundary updates must be legally in effect on or before this date to be reported in the current survey year. Boundary updates effective after this date will be held until the following Boundary and Annexation Survey (BAS) cycle.*****First data collection of 2025 begins*****January 2, 2025Counties login to the LTSB GeoData Collector, upload their current municipal ward layer, and document any annexations that occurred between January 1, 2024 and December 31, 2024.*****First data collection of 2025 ends*****January 17, 2025County clerks must submit data to LTSB using the LTSB GeoData Collector software platform by this date. February 3, 2025LTSB will publish statewide municipal and ward boundaries to the HUB page: https://gis-ltsb.hub.arcgis.com/pages/download-data > State Collection Data tab.REST feature services will also be made available for CTVs and Wards via our ArcGIS Online home page, and will be live updated as data is submitted.February 7, 2025Deadline for BAS submissions to the Census Bureau. LTSB will submit on behalf of the entire State of Wisconsin. BAS boundary updates will be reflected in the American Community Survey (ACS) and Population Estimates Program (PEP) published data and in next year’s BAS materials.*****Second data collection of 2025 begins*****July 1, 2025Counties will login to the LTSB GeoData Collector web portal and upload a current municipal ward layer. *****Second data collection of 2025 ends*****July 15, 2025County clerks must submit data to LTSB using the LTSB GeoData Collector software platform by this date.August 5, 2025LTSB will publish statewide municipal and ward boundaries to the HUB Page: gis-ltsb.hub.arcgis.com > State Collection Data tab.REST feature services will also be made available for CTVs and Wards via our ArcGIS Online home page, and will be live updated as data is submitted . Questions about this data or on this project should be directed to: gis@legis.wisconsin.gov2024 Previous Program TimelineJanuary 1, 2024The 2024 Boundary and Annexation Survey Program begins. The LTSB GeoData Collector software platform will be used.Boundary updates must be legally in effect on or before this date to be reported in the current survey year. Boundary updates effective after this date will be held until the following Boundary and Annexation Survey (BAS) cycle.*****First data collection of 2024 begins*****January 3, 2024Counties login to the LTSB GeoData Collector, upload their current municipal ward layer, and document any annexations that occurred between January 1, 2023 and December 31, 2023.*****First data collection of 2024 ends*****January 17, 2024County clerks must submit data to LTSB using the LTSB GeoData Collector software platform by this date. February 29, 2024LTSB will publish statewide municipal and ward boundaries to the HUB pages: https://gis-ltsb.hub.arcgis.com/pages/bas-data-collection and https://gis-ltsb.hub.arcgis.com/pages/download-data > State Collection Data.REST feature services will also be made available for CTVs and Wards, and will be live updated as data is submitted.March 1, 2024Deadline for BAS submissions to the Census Bureau. LTSB will submit on behalf of the entire State of Wisconsin. BAS boundary updates will be reflected in the American Community Survey (ACS) and Population Estimates Program (PEP) published data and in next year’s BAS materials.*****Second data collection of 2024 begins*****July 1, 2024Counties will login to the LTSB GeoData Collector web portal and upload a current municipal ward layer. *****Second data collection of 2024 ends*****July 15, 2024County clerks must submit data to LTSB using the LTSB GeoData Collector software platform by this date.August 5, 2024LTSB will publish statewide municipal and ward boundaries to the HUB Page: gis-ltsb.hub.arcgis.com > State Collection Data tab.REST feature services will also be made available for CTVs and Wards, and will be live updated as data is submitted via our ArcGIS Online home page. Questions about this data or on this project should be directed to: gis@legis.wisconsin.gov

Annual Resident Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change; for the United States, States, Metropolitan Statistical Areas, Micropolitan Statistical Areas, Counties, and Puerto Rico: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through March. // Note: Total population change includes a residual. This residual represents the change in population that cannot be attributed to any specific demographic component. // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // The Office of Management and Budget's statistical area delineations for metropolitan, micropolitan, and combined statistical areas, as well as metropolitan divisions, are those issued by that agency in September 2018. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

The Bureau of Justice Statistics' (BJS) 2007 Census of Public Defender Offices (CPDO) collected data from public defender offices located across 49 states and the District of Columbia. Public defender offices are one of three methods through which states and localities ensure that indigent defendants are granted the Sixth and Fourteenth Amendment right to counsel. (In addition to defender offices, indigent defense services may also be provided by court-assigned private counsel or by a contract system in which private attorneys contractually agree to take on a specified number of indigent defendants or indigent defense cases.) Public defender offices have a salaried staff of full- or part-time attorneys who represent indigent defendants and are employed as direct government employees or through a public, nonprofit organization. Public defenders play an important role in the United States criminal justice system. Data from prior BJS surveys on indigent defense representation indicate that most criminal defendants rely on some form of publicly provided defense counsel, primarily public defenders. Although the United States Supreme Court has mandated that the states provide counsel for indigent persons accused of crime, documentation on the nature and provision of these services has not been readily available. States have devised various systems, rules of organization, and funding mechanisms for indigent defense programs. While the operation and funding of public defender offices varies across states, public defender offices can be generally classified as being part of either a state program or a county-based system. The 22 state public defender programs functioned entirely under the direction of a central administrative office that funded and administered all the public defender offices in the state. For the 28 states with county-based offices, indigent defense services were administered at the county or local jurisdictional level and funded principally by the county or through a combination of county and state funds. The CPDO collected data from both state- and county-based offices. All public defender offices that were principally funded by state or local governments and provided general criminal defense services, conflict services, or capital case representation were within the scope of the study. Federal public defender offices and offices that provided primarily contract or assigned counsel services with private attorneys were excluded from the data collection. In addition, public defender offices that were principally funded by a tribal government, or provided primarily appellate or juvenile services were outside the scope of the project and were also excluded. The CPDO gathered information on public defender office staffing, expenditures, attorney training, standards and guidelines, and caseloads, including the number and type of cases received by the offices. The data collected by the CPDO can be compared to and analyzed against many of the existing national standards for the provision of indigent defense services.

Abstract

A Census of Population and Housing is the single most extensive statistical undertaking of a country. In order to plan and implement programmes and activities, statistics are needed by the Government administrators of various levels, private users, research organizations and the general public.

The 2001 Census was conducted under the Census Ordinance, which was ammended by the Census Act No 55 of 2000. Census Ordinance places the legal obligation upon the public to give accurate information to the Census officers. The ordinance also gurantee the confidentiality of the information collected at individual level. The CPH 2001 has been designed to collect various information about the characteristics of the population, housing units and the households in Sri Lanka.

The CHP2001 provides

a. Reliable and detailed benchmark statistics on the size, distribution and composition of population.

b. Information pertaining to the characteristics of the housing units.

c. Information on the characteristics of the households

d. Information pertaining to the characteristics of the disable persons.

Geographic coverage

National coverage

Note : The 2001 census enumeration was able to be carried out completely in 18 districts. These include all the 17 districts in Western, Central, Southern, North Western, North Central, Uva and Sabaragamuwa Provinces and Amparai district in the Eastern Province.

Due to the disturbed conditions in Northern and Eastern provinces of Sri Lanka, certain areas could not be enumerated completely.

Analysis unit

(1) Individuals (2) Households

Universe

CPH 2001 covered all residents in each household and all units in each census block.

Population census did not cover diplomats.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

I) Population and Housing Schedule (F3): This schedule was used to collect 24 items from individuals pertaining to demographic and economic characteristics such as General information, Migration patterns, Educational characteristics, Economic characteristics, Nuptiality and Fertility and additional 9 items on Housing unit characteristics such as Occupancy status, Number of households in the unit, Number of occupants in the unit, Construction material of wall, floor, roof, Type of structure, Year of construction, Unit usage, Availability of rooms and Number of rooms and 7 items on Household such as Number of occupants in the household, Availability of toilet, Type of toilet, Source of drinking water, Type of lighting, Type of cooking fuel and Tenure

II) Schedule for Disabled Persons(F4): This schedule was used to collect information pertaining to 6 types of disabilities such as Vision, Hearing / Speaking, Manual/walking, Mental and Other Physical disabilities. [This is dealt with as a special census project and archived seperately].

Data Collection Forms:

F1 - List of all the building units located in a Census block F2 - Administrative/Technical form (Summary of F1) F3 - Population and Housing Schedule (all information of the population, housing and household information). F4 - Schedule for disabled persons F5 - Special schedule for Tourists and Foreign visitors. - Schedule for post enumeration survey.

Cleaning operations

Data processing consisted of two major phases. (1) Manual editing and coding, (2) Computer processing such as fixes while data entry, structure checking and completeness and secondary editing..

Manual editing was confined in the field to simple checks such as verification of area identification codes and the codes for certain questions (eg. district of birth). Coding was required only in respect of three questions, namely educational attainment, occupation and industry.

Data were entered for the second time to verify the original keyed data which is called the verification process. When the administrators fell that the overall error rate is diminishing, the verification process was mitigated step by step assuming that the operators are progressively improving in entering the questionnaires correctly.

A series of computer edit checks were carried out and records containing errors were printed for visual verification. These edit checks included both range and consistency checks. Finally limited number of imputations was done before the tabulation of data.

Processing was done on IBM S390 integrated server 3006 model B01 and several personal computers. Keyboard to disk type data entry was adopted for data capture.

The software Integrated Micro Computer Processing System (IMPS) developed by U.S. Bureau of Census was used to data processing activities including data entry

Data appraisal

The Districtwise data files were analysed. the breakups of the analysis such as

1. Male/female totals
2. Frequencies
3. Housing unit types

were filed as standard benchmarks for each district to be used to compare various District Table figures.

Abstract (en): The United States Census Bureau conducts a Census of Governments every five years -- in years ending in "2" or "7" -- to collect information about governments in the United States. The Government Organization branch of the 1997 Census of Governments describes the organization and activities of local governments. The 1997 Local Government Directory Survey covered all county, municipal, town or township, school district, special district governments, school systems, and education service agencies that met the Census Bureau criteria for independent governments. The counts of local governments reflect those in operation in June 1997. This collection includes eight parts, each including information regarding a different type of government: (1) county governments, (2) municipal governments, (3) township governments, (4) special district governments, (5) school district governments, (6) state dependent school systems, (7) local dependent school systems, and (8) education service agencies. The data include information on various codes used to identify the government unit, government name, population in 1996 (or enrollment in 1996 for data collected from schools), and government functions. Census statistics on governments are designed to account for the totality of public sector activity without omission or duplication. There are no weight variables included in this collection. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: The final response rate was 83.4 percent. Local governments in the United States. Data that are derived from a census are not subject to sampling variability. 2014-06-20 SPSS, SAS, and Stata setup files, as well as SPSS and Stata system files, a SAS transport (CPORT) file, a tab-delimited data file, and an R data file have been added to the collection. Additionally, a codebook has been created. mail questionnaire For additional information on the Census of Governments, 1997, please refer to the United States Census Bureau Web site.