94 datasets found
  1. s

    Automatic national census pre-Enumeration Areas for Zimbabwe in 2021,...

    • eprints.soton.ac.uk
    Updated Dec 5, 2024
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    Qader, Sarchil; Kuepie, K; Tatem, Andrew (2024). Automatic national census pre-Enumeration Areas for Zimbabwe in 2021, version 1.0 [Dataset]. http://doi.org/10.5258/SOTON/WP00797
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    Dataset updated
    Dec 5, 2024
    Dataset provided by
    University of Southampton
    Authors
    Qader, Sarchil; Kuepie, K; Tatem, Andrew
    Area covered
    Zimbabwe
    Description

    Automatic national census pre-Enumeration Areas for Zimbabwe in 2021, version 1.0. These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the Geo-referenced Infrastructure and Demographic Data for Development (GRID3) programme, supported with funding from the Bill & Melinda Gates Foundation and the United Kingdom’s Foreign, Commonwealth & Development Office (OPP1182425). Programme partners included the United Nations Population Fund (UNFPA), the Center for International Earth Science Information Network (CIESIN) within the Earth Institute at Columbia University, and the Flowminder Foundation.

  2. i

    Population and Household Census 2011 - Niue

    • dev.ihsn.org
    • catalog.ihsn.org
    • +1more
    Updated Apr 25, 2019
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    Niue Statistics (2019). Population and Household Census 2011 - Niue [Dataset]. https://dev.ihsn.org/nada/catalog/study/NIU_2011_PHC_v01_M
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    Dataset updated
    Apr 25, 2019
    Dataset authored and provided by
    Niue Statistics
    Time period covered
    2011
    Area covered
    Niue
    Description

    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

    Analysis unit

    • Household
    • Individual/Person
    • Members Oversea

    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]

    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.

  3. R

    Data from: Tree Enumeration Dataset

    • universe.roboflow.com
    zip
    Updated Apr 27, 2024
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    student (2024). Tree Enumeration Dataset [Dataset]. https://universe.roboflow.com/student-0d063/tree-enumeration-oftck
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    zipAvailable download formats
    Dataset updated
    Apr 27, 2024
    Dataset authored and provided by
    student
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Trees Bounding Boxes
    Description

    Tree Enumeration

    ## Overview
    
    Tree Enumeration is a dataset for object detection tasks - it contains Trees annotations for 2,948 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
    
  4. R

    Enumeration Dataset

    • universe.roboflow.com
    zip
    Updated Mar 5, 2025
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    ensit (2025). Enumeration Dataset [Dataset]. https://universe.roboflow.com/ensit-eccci/enumeration/dataset/2
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    zipAvailable download formats
    Dataset updated
    Mar 5, 2025
    Dataset authored and provided by
    ensit
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Variables measured
    Objects Bounding Boxes
    Description

    Enumeration

    ## Overview
    
    Enumeration is a dataset for object detection tasks - it contains Objects annotations for 622 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [ODbL v1.0 license](https://creativecommons.org/licenses/ODbL v1.0).
    
  5. w

    XV National Population and IV Housing Census - IPUMS Subset - Colombia

    • microdata.worldbank.org
    Updated Aug 1, 2025
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    Departmento Administrativo Nacional de Estadística (DANE) (2025). XV National Population and IV Housing Census - IPUMS Subset - Colombia [Dataset]. https://microdata.worldbank.org/index.php/catalog/488
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    Dataset updated
    Aug 1, 2025
    Dataset provided by
    Departmento Administrativo Nacional de Estadística (DANE)
    IPUMS
    Time period covered
    1985 - 1986
    Area covered
    Colombia
    Description

    Analysis unit

    Persons and households

    UNITS IDENTIFIED: - Dwellings: no - Vacant Units: Yes - Households: yes - Individuals: yes - Group quarters: no

    UNIT DESCRIPTIONS: - Dwellings: Separated space with independent access that serves as a human lodging - Households: Individuals living in the same dwelling. For indigenous population definition of household requires sharing at least one meal. - Group quarters: Group of persons who share a common roof and food because of work, health, religion, etc.

    Universe

    Population census included people in territories, sailors, diplomats and their families. The indigenous population was enumerated. Due to guerrilla activity, approximately 3,000 dwellings (out of 6 million) could not be enumerated. The microdata sample consists of the 10% survey. It excludes population in group quarters and indigenous population.

    Kind of data

    Population and Housing Census [hh/popcen]

    Sampling procedure

    MICRODATA SOURCE: Departmento Administrativo Nacional de Estadística (DANE)

    SAMPLE SIZE (person records): 2643125.

    SAMPLE DESIGN: Systematic sample of dwellings pre-selected before fieldwork based on pre-census enumeration. In rural areas selection was determined in the field by the enumerator.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    5 enumeration forms applied to 5 different target populations: (f1) short form for private dwellings (90%) of the population, requested information on age, sex, and relationship to householder; (f2) long form for private dwellings (10%); (f3) group quarters, 0.17% of dwellings; (f4) indigenous private dwellings (100%), representing 0.95% of dwellings; and (f5) indigenous group-quarters, 0.01% of dwellings.

  6. s

    Population and Housing Census 2011 - Samoa

    • microdata.sbs.gov.ws
    • microdata.pacificdata.org
    Updated May 23, 2025
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    Samoa Bureau of Statistics (2025). Population and Housing Census 2011 - Samoa [Dataset]. https://microdata.sbs.gov.ws/index.php/catalog/15
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    Dataset updated
    May 23, 2025
    Dataset authored and provided by
    Samoa Bureau of Statistics
    Time period covered
    2011
    Area covered
    Samoa
    Description

    Abstract

    The 2011 Population and Housing Census of Samoa was taken on the midnight of November the 7th 2011. It counted every person in the country on that night and collected a wide range of social, economic and demographic information about each individual and their housing status.

    The information were used to develop statistical indicators to support national plannning and policy-making and also to monitor MDG indicators and all other related conventions. This included population growth rates, educational attainment, employment rates, fertility rates, mortality rates, internal movements, household access to water supply, electricity, sanitation, and many other information. The full report is available at SBS website: http://www.sbs.gov.ws under the section on Population statistics and demography.

    Geographic coverage

    National coverage Regions Districts Village Enumeration areas

    Analysis unit

    Private households Institutional households Individuals Women 15-49 Housing/Buildings

    Universe

    The PHC 2011 covered all de facto household members, institutional households such as boarding schools, hospitals, prison inmates and expatriates residing in Samoa for more than 3 months. The PHC excluded all tourists visiting Samoa during the enumeration period and all Samoans residing overseas.

    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

    Users' consultation seminars were conducted for three consecutive days (June 8th -10th, 2010) with financial support provided by the office of UNFPA in Suva via the Samoa Parliamentary Group for Population Development (SPGPD) annual programs. For the first time in census history, the SPGPD or members of parliament have become the target group of users to get involved in any census questionnaire consultations.

    All government ministries and non-governmental organizations were invited to the consultation seminars and each was asked to make a presentation of data needs for consideration in the final census 2011 questionnaire. To avoid re-inventing the wheel in the compilation of the list of census questions for census 2011, the questionnaire from the census 2006 was reprinted and distributed to all participants and presenters to select questions that they would consider again for the census 2011 in addition to their new data needs. Users were also advised that any new question would need good justifications of how it links to national interests.

    At the end of the three days seminar, all new questions were compiled for final selection by Samoa Bureau of Statistics. Not all the users' data needs have been included in the final 2011 census questionnaire due mainly to the cost involved and limited time for census enumeration. Therefore, the final selection of questions was purely based on the linkage of the data being requested to the list of statistical indicators in the 'Strategy for the Development of Samoa 2008-2012' (SDS) and the 'Millennium Development Goals' (MDGs) 2015. All data requests outside of the two frameworks were put aside to be integrated in other more appropriate survey activities by the bureau.

    From July 2010-December 2010, the questionnaire was formatted using the In-Design CS4 software. It is important to note that the PHC 2011 was the first ever census using the scanning technology to process data from the census questionnaires as a replacement of the usual manual data entry process. The scanning was pilot tested in April 2011, before it was finally used for final census enumeration.

    The questionnaire was designed using A3 paper size.

    The Population questionnaire was administered in each household, which collected various information on household members including age, sex, citizenship, disability, orphanhood, marital status, residence (birth, usual, previous), religion, education and employment.

    In the Population questionnaire, a special section was administered in each household for women age 15-49, which also asked information on their children ever born still living, died or living somewhere else. Mothers of children under one year were also asked whether their last born children were still living at the time of the census.

    The Housing questionnaire was also administered in each household which collected information on the types of building the household lived, floor materials, wall materials, roof materials, land tenure, house tenure, water supply, drinking water, lighting, cooking fuel, toilet facility, telephone, computer, internet, refrigerator, radio, television and others.

    Cleaning operations

    Data editing was done in several stages. 1. Office manual editing and coding 2. Automatic scanning data entry edits 3. Visual verification questionnaire edits 3. Structure checking and completeness 4. Structure checks of the CSPro data files Editing program can be enquired at the Division of IT and Data Processing at email address: info.stats@sbs.gov.ws

    Sampling error estimates

    The census is a full-coverage of the population, therefore it is not a sample where sampling errors can be estimated.

    Data appraisal

    There was no post-enumeration in the census 2011. One of the normal practices by the bureau to validate the total population counts from all villages, districts and regions of Samoa in any census is the manual count of the population in all areas during the on-going census enumeration.That information is collected by the enumerators and field supervisors during the enumeration using the Enumerators and Supervisors control forms. At the end of the enumeration, the control forms which mainly contained the number of males and females per enumeration area will be collected and compiled by the Census and Survey division as the first preliminary count of the census. In the census 2011, the preliminary population counts were compiled and launched as the 'Village Directory 2011' report after 4 weeks from end of the enumeration period.

    The significance of the Village Directory report is it helps to provide a qiuick overall picture of the population growth and population distribution in all villages of the country relative to previous censuses. Most important of all is that the preliminary count will provide the basis for a decision whether a post-enumeration is warrant or otherwise. If the preliminary country is close to the projected population then the post-enumeration is assumed not worth the cost because it is expensive and it will delay all other census processes. In the census 2011, the preliminary count arrived at 186,340 which was more than the projected population of 184,032 as depicted in the Statistical Abstract 2009. Therefore the decision was made that post-enumeration was not worth it.

  7. R

    Tooth Enumeration Fdi Dataset

    • universe.roboflow.com
    zip
    Updated Dec 15, 2023
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    DATN (2023). Tooth Enumeration Fdi Dataset [Dataset]. https://universe.roboflow.com/datn-d0dnd/tooth-enumeration-fdi/dataset/1
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    zipAvailable download formats
    Dataset updated
    Dec 15, 2023
    Dataset authored and provided by
    DATN
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Teeth LmxU Bounding Boxes
    Description

    Tooth Enumeration FDI

    ## Overview
    
    Tooth Enumeration FDI is a dataset for object detection tasks - it contains Teeth LmxU annotations for 623 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
    
  8. w

    Human Resource Development Survey 1993 - Tanzania

    • microdata.worldbank.org
    • dev.ihsn.org
    • +2more
    Updated Jan 30, 2020
    + more versions
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    University of Dar es Salaam (2020). Human Resource Development Survey 1993 - Tanzania [Dataset]. https://microdata.worldbank.org/index.php/catalog/403
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    Dataset updated
    Jan 30, 2020
    Dataset authored and provided by
    University of Dar es Salaam
    Time period covered
    1993 - 1994
    Area covered
    Tanzania
    Description

    Abstract

    The objectives of the survey were to provide information regarding the following: a. Household use of, and expenditure patterns for, social services; b. Reasons for low levels of household investment in education and health services for children; c. The distribution of the benefits of public spending for social services and how to improve targeting; d. Households' evaluation of the social services available to them; e. The potential for demand-side interventions to increase human capital investment directly (especially for girls and the poor); and f. The feasibility of repeated national monitoring surveys to assess the impact of future Bank and government projects in the social sectors, and to increase Tanzania's capacity to perform household survey work.

    Geographic coverage

    National coverage

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    Sample size is 5,184 households

    The HRDS is national in scope and uses all the 222 clusters of the National Master Sample (NMS) maintained by the Bureau of Statistics as its sampling frame.4 Two NMS clusters were not surveyed because of weather conditions. For example, Nyamburi village in the Mara region was inaccessible. Heavy rains had washed away a bridge 8 kms (14 miles) from the village. All household surveys conducted by the Bureau of Statistics (e.g. Agricultural Sample Survey since 1986/87, Labor Force Survey in 1990/91) have used the framework of the NMS. This permits obtaining estimates at the national level and by area: rural, Dar es Salaam (DSM), and other urban towns. The current NMS covers 222 clusters: 100 rural villages representing the rural areas, and 122 Enumeration Areas (EAs) representing the urban areas. Fifty-two EAs are from the capital city, itself, 40 EAs are from the nine municipalities (Arusha, Dodoma, Moshi, Tanga, Morogoro, Iringa, Mbeya, Tabora, and Mwanza), and 10 EAs are from the remaining regional headquarters.

    Selection of households and non-response.

    Household selection was done in the field. In each cluster the team supervisor would first obtain the list of ten-cell leaders from the local authorities, and then, from each ten cell-leader, the list of households belonging to his/her cell. Each household was assigned a unique number, and then, using a table of random numbers, randomly selected. In each cluster, a list of about 30 households was then obtained, the last households in the list being alternates. With the collaboration of local authorities, the field workers were able to have an almost 100 percent reponse rate, except for the cases in which no member of the household was present for intervieing, and returning to the household was not feasible. Refusals to cooperate were rare. In those cases--absent households or refusals--, new households were drawn from the list of alternates.

    The survey covered a total of 4,953 households in the 20 regions of Mainland Tanzania: 2,135 rural and 2,818 urban (see Table 1). In a second stage, the survey was extended to Zanzibar, where 230 households, in 24 clusters, were interviewed.

    Region / Rural / Urban / Total Dodoma / 100 / 80 / 180 Arusha / 118 / 121 / 239 Kilimanjaro / 124 / 154 / 278 Tanga / 132 / 167 / 299 Morogoro / 88 / 120 / 208 Coast / 79 / 88 / 167 Dar es Salaam / 0 / 1127 / 1127 Lindi / 84 / 50 / 134 Mtwara / 114 / 44 / 158 Ruvuma / 69 / 49 / 118 Iringa / 124 / 128 / 252 Mbeya / 174 / 153 / 327 Singida / 82 / 41 / 123 Tabora / 99 / 72 / 171 Rukwa / 59 / 56 / 115 Kigoma / 83 / 35 / 118 Shinyanga / 153 / 54 / 207 Kagera / 193 / 24 / 217 Mwanza / 163 / 192 / 355 Mara / 97 / 63 / 160 Mainland Tanzania / 2135 / 2818 / 4953 Zanzibar / 127 / 104 / 231

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Development of Survey Instrument.

    The first draft of the household survey was developed in English in July, 1993. Training of enumerators, based on this draft, began on August 2, 1993. The month of August was devoted to training the enumerators and pre-testing the questionnaire. The first pre-test of the questionnaire took place in mid-August. The household questionnaire was almost completely precoded to eliminate coding errors and time delays. A category labeled "other: specify" was added to several questions. For those questions for which answers were not mutually exclusive, we precoded them with letters, rather than numbers, to allow for unambiguously coding of multiple answers. To minimize nonsampling errors, the questionnaire was in a form that reduced to a minimum the number of decisions required of interviewers while in the field. In anticipation of pages becoming detached from the questionnaire, every page contained a space for the household number and the last digit of the cluster code. Despite the fact that questions were written exactly as they were supposed to be asked by the interviewer, interviewers were granted some flexibility to give the interview greater semblance to a conversation, rather than an inquisition.

    Pre-Test of Questionnaire.

    The "pre-pre-test" of the questionnaire (August 16, 1993) was done only to discern whether the questions were understood, how long the administration of the survey required, whether all responses had been anticipated, which sections needed to be stressed during the training, etc. In this pre-pre-test, each questionnaire required an average of 4 hours to complete, far longer than the planned 1.5 hour maximum. The survey was consequently shortened and streamlined.

    The true pre-test was conducted in two different types of clusters: Ubungo ward in DSM (urban) and Kibaha in the Coast Region (rural) over a period of two days. We chose these clusters because they are representative of two distinct groups, so a broader spectrum of answers and problems with the instrument could be anticipated. In the pre-test each questionnaire required an average of 2.5 hours. After a couple weeks of interviewing, the enumerators became more familiar with the instrument, resulting in their spending an average of 1.5 to 2 hours per questionnaire.

    During the pre-test, each supervisor was asked to comment on each interview. The supervisor was asked to pay special attention to questions that seemed to make the respondent uncomfortable, that the respondent had difficulty understanding, or that the respondent seemed to dislike. The supervisor also evaluated which sections seemed to go slowly, had the most difficult questions, or provided insufficient opportunity for a complete response.

    Revision of questionnaire.

    Given the results of the two pre-tests, several areas for improvement in the questionnaire were identified. Perhaps most importantly, the willingness-to-pay amounts were adjusted. The sample distributions of the maximum willingness-to-pay questions were analyzed, and, based on that analysis, we decided to change some of the values. For example, in the child spacing question, the "pay Tsh 1,000" responses unexpectedly accounted for a large share of the bids. Thus, we provided the option of paying more by introducing "pay Tsh 50,000" and "pay Tsh 25,000" as answer choices. For the other contigent valuation sections--health and education--the first pre-test determined that there was also a large lumping of responses at the high end of the scale. We adjusted the ranges accordingly, although there remains some lumping at the high end in the final data.

    We also changed the order of the sections. Based on the pre-test and judgment of the field workers, we decided to first ask the questions in the individual section, then the contigent valuation questions, then the household questions. Because the respondents enjoyed the contigent valuation questions so much, this decision helped increase interest in the questionnaire and re-energized the respondent before proceeding with the household questions--the last part of the questionnaire. The final survey instrument, incorporating all of the changes dictated by the pre-tests and other expert advice, was completed on September 12, 1993.

    Translation.

    Translation of the survey instrument was a joint effort of the enumerators and supervisors. Given the specific characteristics of the Kswahili language, this was a much better approach than asking one translator to translate from English to Kswahili, and another one to translate from Kswahili to English. The "group" translation, involving those who would ask the questions, was intended to avoid different interpretations of the same question and achieve uniformity. In this way the enumerators were able to better convey the message/objective of each question.

    The majority of the interviews were conducted in swahili. In very few cases, because no one in the selected household could speak swahili, the need arose to use interpreters.

    Our initial plan called for the field work to start no later than August 29. However, unforeseen circumstances, including both financial and logistical problems, delayed the first field trip. Both the money and the materials were available by September 6, and five of the six teams left for Tanga region on that day. Initially we had planned to have the sixth team based full-time in Dar es Salaam; however, tighter time constraints imposed by the above and subsequent delays eventually made it necessary to send the sixth team into the field as well, as detailed below.

    Description of questionnaires

    The main objective of the survey was to obtain data on the use of, and spending on, the social sectors. The primary emphasis was on education and health--the areas in which the major gaps in availability of data were identified. The survey was divided into five major components, each of which was further subdivided, as described below:

    I. Individual Questionnaire A. Household Roster; B. Information on

  9. p

    Agricultural Census 2009 - Samoa

    • microdata.pacificdata.org
    • catalog.ihsn.org
    • +1more
    Updated Apr 1, 2019
    + more versions
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    Samoa Bureau of Statistics (2019). Agricultural Census 2009 - Samoa [Dataset]. https://microdata.pacificdata.org/index.php/catalog/142
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    Dataset updated
    Apr 1, 2019
    Dataset provided by
    Ministry of Agriculture and Fisheries
    Samoa Bureau of Statistics
    Time period covered
    2009
    Area covered
    Samoa
    Description

    Abstract

    The 2009 Agricultural Census was undertaken by the Samoa Bureau of Statistics in collaboration with the Ministry of Agriculture and Fisheries. The Census collected a large volume of information pertaining to the agricultural activities of households. Enumeration was carried out for 5 weeks in November/December 2009 by enumerators selected from the villages through interview and a basic test. The test included basic mathematical skills, knowledge of agricultural practices and map reading. This was to ensure that the enumerators are of high quality. The officers of the Samoa Bureau of Statistics and the Ministry of Agriculture and Fisheries were allocated to specified areas as supervisors.

    Geographic coverage

    National

    Analysis unit

    Households (Agricultural and non-Agricultural) Agricultural Holdings

    Kind of data

    Census/enumeration data [cen]

    Sampling procedure

    For any census to be successfully carried out, good household lists and enumeration area maps are pre-requisites. A list of households in respect of each enumeration block in the country was prepared in 2005 for the 2006 Population Census. The updated household list from the 2006 Population Census was used as a frame for the Agricultural Census.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The methodology for carrying out the census of Agriculture in Samoa was a combination of complete count and sample survey. Thus the census was basically two part operation. The first part involved all households who were required to complete the Household Form. The households identified as agriculturally active from the Household Forms (Subsistence, Subsistence and Cash and Commercial) were required to complete the Holding Form for every holding operated.

    The second part of the questionnaire was designed to cover 25 percent of all agricultural holdings as identified in the first part, with selection made on systematic sample basis (every fourth holding selected). Thus while the Household Form was canvassed in respect of all households, the Holding Form was to be completed by agriculturally active Households only and the Parcel Form was completed in respect of 25 percent of the agricultural holdings.

    Printing of Questionnaires and Instruction Manuals In all there were three questionnaires and two instruction manuals one in Samoan and one in English. The three questionnaires were printed on different coloured paper for ease of identification. All census documents were printed and distributed well in advance of the start of the field work.

    Cleaning operations

    The Secretariat of Pacific community (SPC) provided technical assistance for data processing. The TA was delivered in two separate missions, first to implement data entry, and the second mission was to perform data editing and generate final tabulation for final report. Prior to the start of data entry, Siaumau Misela of Samoa Bureau of Statistics was invited to SPC in December 2009 for a two weeks attachment. Misela worked closely with the SPC data processing specialist in developing the data entry system using CSPro (Census and Survey Processing System). The first mission of the data processing specialist in January 2010 was to finalize and implement data entry. The second mission in October 2010 concentrated mainly on data editing, data recode and generating final tables. The data processing (manual and computer) was done in the Data Processing Section of the Samoa Bureau of Statistics. To facilitate the manual and machine processing of the forms, questionnaires from the same enumeration area were bound together in a batch / folio and assigned a batch id. This id consists of the District, Village and the enumeration area codes. These forms were subjected to manual data scrutiny and corrections. The data entry was implemented using ENTRY of CSPro, and BATCH EDIT for the validation of encoded data items. Data entry was run through a network, which link all data entry work station to a server. A team of 6 staff (1 permanent and 5 temporary) were assigned to do the data processing.

    Data appraisal

    Fifty percent key verification was done on all the batches, and questionnaires with key verification error rate higher than the tolerance limit was subjected to 100 percent key verification. Additional checks were added in the validation program. Detected errors and inconsistencies were corrected in the batch files.

  10. w

    National Agricultural Sample Census Pilot (Private Farmer) Fishery 2007 -...

    • microdata.worldbank.org
    • microdata.fao.org
    • +2more
    Updated Oct 30, 2024
    + more versions
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    National Bureau of Statistics (2024). National Agricultural Sample Census Pilot (Private Farmer) Fishery 2007 - Nigeria [Dataset]. https://microdata.worldbank.org/index.php/catalog/6382
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    Dataset updated
    Oct 30, 2024
    Dataset provided by
    National Bureau of Statistics, Nigeria
    Authors
    National Bureau of Statistics
    Time period covered
    2007
    Area covered
    Nigeria
    Description

    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 based of fish farmers

    Universe

    The survey covered all de jure household members (usual residents), who were into fish production

    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 3 Fishing farming housing units were systematically selected and canvassed .

    Sampling deviation

    There was deviations from the original sample design

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The NASC fishery questionnaire was divided into the following sections: - Holding identification: This is to identify the holder through HU serial number, HH serial number, and demographic characteristics. - Type of fishing sites used by holder. - Sources and quantities of fishing inputs. - Quantity of aquatic production by type. - Quantity sold and value of sale of aquatic products. - Funds committed to fishing by source and others

    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 enterd data. The completed questionnaires were collated 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

    Both Enumeration Area (EA) and Fish holders' level Response Rate was 100 per cent.

    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

  11. R

    Quad Enum Detect Cat3 Dataset

    • universe.roboflow.com
    zip
    Updated Apr 26, 2024
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    Khalid (2024). Quad Enum Detect Cat3 Dataset [Dataset]. https://universe.roboflow.com/khalid-x9ahn/quad-enum-detect-cat3
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    zipAvailable download formats
    Dataset updated
    Apr 26, 2024
    Dataset authored and provided by
    Khalid
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Dental Bounding Boxes
    Description

    Quad Enum Detect Cat3

    ## Overview
    
    Quad Enum Detect Cat3 is a dataset for object detection tasks - it contains Dental annotations for 705 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
    
  12. Namibia Population and Housing Census 2011 - Namibia

    • microdata.nsanamibia.com
    Updated Sep 30, 2024
    + more versions
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    Namibia Statistics Agency (2024). Namibia Population and Housing Census 2011 - Namibia [Dataset]. https://microdata.nsanamibia.com/index.php/catalog/9
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    Dataset updated
    Sep 30, 2024
    Dataset authored and provided by
    Namibia Statistics Agencyhttps://nsa.org.na/
    Time period covered
    2011
    Area covered
    Namibia
    Description

    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

    Households and persons

    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

    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.

  13. u

    Somali High Frequency Survey 2016 - Somalia

    • microdata.unhcr.org
    • catalog.ihsn.org
    • +2more
    Updated Oct 9, 2023
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    Utz J. Pape (2023). Somali High Frequency Survey 2016 - Somalia [Dataset]. https://microdata.unhcr.org/index.php/catalog/1017
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    Dataset updated
    Oct 9, 2023
    Dataset authored and provided by
    Utz J. Pape
    Time period covered
    2016
    Area covered
    Somalia
    Description

    Abstract

    Between February and March 2016, the World Bank, in collaboration with Somali statistical authorities conducted the first wave of the Somali High Frequency Survey to monitor welfare and perceptions of citizens in all accessible areas of 9 regions within Somalia’s pre-war borders including Somaliland which self-declared independence in 1991. The survey interviewed 2,882 urban households, 822 rural and 413 households in Internally Displaced People (IDP) settlements. The sample was drawn randomly based on a multi-level clustered design. This dataset contains information on economic conditions, education, employment, access to services, security and perceptions. It also includes comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption methodology as detailed in Pape and Mistiaen (2014).

    Geographic coverage

    The following pre-war regions: Awdal, Banadir, Bari, Mudug, Nugaal, Sanaag, Sool, Togdheer and Woqooyi Galbeed (Somaliland self-declared independence in 1991).

    Analysis unit

    Household

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The sample employs a stratified two-staged clustered design with the Primary Sampling Unit (PSU) being the enumeration area. Within each enumeration area, 12 households were selected for interviews.

    Two different listing approaches were used. In 2 strata with more volatile security as well as for IDP camps, a multi-stage cluster design was employed (micro-listing). Each selected enumeration area was divided into multiple segments and each segment was further divided into blocks. Within each enumeration area, one segment was randomly selected and within the segment 12 blocks were chosen. In each block, all structures were listed before selecting randomly one structure. Within the selected structure, all households were listed and one household randomly selected for interview. In strata less volatile (14 strata), the complete enumeration area was listed before 12 households were randomly selected for interviews (full-listing).

    Sampling deviation

    EAs were replaced if security rendered field work unfeasible. Replacements were approved by the project manager. Replacement of households were approved by the supervisor after a total of three unsuccessful visits of the household.

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Research instrument

    Questionnaire Modules - Household Roster (110 questions) - Household Characteristics (38 questions) - Consumption - Food (30 questions per item) - Non-Food (14 questions per item) - Livestock (39 questions per item) - Durables (16 questions per item) - Perception (24 questions) - Food Security* (24 questions) - Income and Remittances* (14 questions) - Household Enterprise* (172 questions) - Shocks* (15 questions)

  14. f

    Agreement between observed and reported DQQ responses. Pre-data quality...

    • plos.figshare.com
    xls
    Updated Jun 25, 2025
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    Rhys Manners; Anna W. Herforth; Maria Delfine; Rosil Hesen; Didier Nkubito; Karin Borgonjen-van den Berg; Eric Matsiko; Marguerite Niyibituronsa; Betül T. M. Uyar; Elise F. Talsma (2025). Agreement between observed and reported DQQ responses. Pre-data quality check presents the agreement of DQQ responses for enumerator (n = 154) and mobile-phone (n = 134) respondents compared to observed responses. Post-data quality check presents the agreement of DQQ responses for enumerator (n = 150) and mobile phone (n = 127) respondents following removal of respondents who exceeded the data quality threshold. Agreement rates (reported versus observed) are average rates for all respondents, across the 29 DQQ questions. [Dataset]. http://doi.org/10.1371/journal.pone.0317611.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 25, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Rhys Manners; Anna W. Herforth; Maria Delfine; Rosil Hesen; Didier Nkubito; Karin Borgonjen-van den Berg; Eric Matsiko; Marguerite Niyibituronsa; Betül T. M. Uyar; Elise F. Talsma
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Agreement between observed and reported DQQ responses. Pre-data quality check presents the agreement of DQQ responses for enumerator (n = 154) and mobile-phone (n = 134) respondents compared to observed responses. Post-data quality check presents the agreement of DQQ responses for enumerator (n = 150) and mobile phone (n = 127) respondents following removal of respondents who exceeded the data quality threshold. Agreement rates (reported versus observed) are average rates for all respondents, across the 29 DQQ questions.

  15. w

    Service Delivery Indicators Education Survey 2016 - Harmonized Public Use...

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Apr 13, 2021
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    Waly Wane (2021). Service Delivery Indicators Education Survey 2016 - Harmonized Public Use Data - Madagascar [Dataset]. https://microdata.worldbank.org/index.php/catalog/3884
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    Dataset updated
    Apr 13, 2021
    Dataset authored and provided by
    Waly Wane
    Time period covered
    2016
    Area covered
    Madagascar
    Description

    Abstract

    The Service Delivery Indicators (SDI) are a set of health and education indicators that examine the effort and ability of staff and the availability of key inputs and resources that contribute to a functioning school or health facility. The indicators are standardized allowing comparison between and within countries over time.

    The Education SDIs include teacher effort, teacher knowledge and ability, and the availability of key inputs (for example, textbooks, basic teaching equipment, and infrastructure such as blackboards and toilets). The indicators provide a snapshot of the learning environment and the key resources necessary for students to learn.

    Madagascar Service Delivery Indicators Education Survey was implemented from April 2016 (for enumerator training and pre-testing of the instruments) to May and June 2016 (for fieldwork and data collection) by CAETIC Development, a strong local think-tank and survey firm. The sampling strategy was done by INSTAT the national institute for statistics. Information was collected from 473 primary schools, 2,130 teachers (for skills assessment), 2,475 teachers (for absence rate), and 3,960 pupils across Madagascar. The survey also collected basic information on all the 3,049 teachers or staff that teach in the 473 primary schools visited or are non-teaching directors.

    Geographic coverage

    National

    Analysis unit

    Schools, teachers, students.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A two-stage sampling method was adopted. First, in each stratum schools were chosen within the selected councils. Once at a selected school, the enumerator selected teachers and pupils depending on the structure of the classrooms.

    The schools were chosen using probability proportional to size (PPS), where size was the number of standard two pupils as provided by the 2014 EMIS database. As for the selection of the cluster, the use of PPS implied that each standard four pupil within a stratum had an equal probability for her school to be selected.

    Finally, within each school, up to 10 standard four pupils and 10 teachers were selected. Pupils were randomly selected among the grade-four pupil body, whereas for teachers, there were two different procedures for measuring absence rate and assessing knowledge. For absence rate, 10 teachers were randomly selected from the teachers’ roster and the whereabouts of those teachers was ascertained in a return surprise visit. For the knowledge assessment, however, all teachers who were currently teaching in primary four or taught primary three the previous school year were included in the sample. Then a random number of teachers in upper grades were included to top up the sample. These procedures implied that pupils across strata, as well as teachers across strata and within a school (for the knowledge assessment) did not all have the same probability of selection. It was, therefore, warranted to compute weights for reporting the survey results.

    The sampling strategy for the SDI in Madagascar was done by INSTAT the national statistics office.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The SDI Education Survey Questionnaire consists of six modules:

    Module 1: School Information - Administered to the head of the school to collect information about school type, facilities, school governance, pupil numbers, and school hours. Includes direct observations of school infrastructure by enumerators.

    Module 2a: Teacher Absence and Information - Administered to headteacher and individual teachers to obtain a list of all school teachers, to measure teacher absence, and to collect information about teacher characteristics.

    Module 2b: Teacher Absence and Information - Unannounced visit to the school to assess absence rate.

    Module 3: School Finances - Administered to the headteacher to collect information about school finances (this data is unharmonized).

    Module 4: Classroom Observation - An observation module to assess teaching activities and classroom conditions.

    Module 5: Pupil Assessment - A test of pupils to have a measure of pupil learning outcomes in mathematics and language in grade four.

    Module 6: Teacher Assessment - A test of teachers covering mathematics and language subject knowledge and teaching skills.

    Cleaning operations

    Data quality control was performed in Stata.

  16. i

    Time Use Survey 2007 - Pakistan

    • dev.ihsn.org
    • catalog.ihsn.org
    • +1more
    Updated Apr 25, 2019
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    Federal Bureau of Statistics (2019). Time Use Survey 2007 - Pakistan [Dataset]. https://dev.ihsn.org/nada/catalog/74333
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    Dataset updated
    Apr 25, 2019
    Dataset authored and provided by
    Federal Bureau of Statistics
    Time period covered
    2007
    Area covered
    Pakistan
    Description

    Abstract

    A primary objective of the national Time Use Survey in Pakistan is to account for the 24 hours time in term of the full spectrum of activities carried out during the duration. The objectives of the survey are specified as under:- - To profile the quantum and distribution of paid/unpaid work as a means to infer policy/programme implications from the perspective of gender equity. - To collect and analyze the time use pattern of the individuals in order to help draw inferences for employment and welfare programmes. - To collect and analyze the comprehensive information about the time spent by people on marketed and non-marketed economic activities covered under the 1993-SNA, non-marketed non-SNA activities within the General Production Boundary and personal care and related activities that cannot be delegated to others. - To use the data in generating more reliable estimates on work force.

    Geographic coverage

    The survey covers all urban and rural areas of the four provinces of Pakistan defined as such by 1998 Population Census excluding Federally Administered Tribal Areas (FATA) and certain administrative areas of NWFP. The population of geographic areas excluded from the survey constitutes about 2 percent of the total population as enumerated in 1998 Population Census. The population excluded is located in difficult terrain and its enumeration through personal interview is not possible within the given constraints of time, access and cost.

    Analysis unit

    Households Individuals

    Universe

    The universe consists of all urban and rural areas of the four provinces of Pakistan, defined as such by Population Census 1998, excluding FATA & Military Restricted Areas. The population of excluded area constitutes about 3% of the total population and is located in different terrain.

    Sampling procedure

    Sampling Frame Federal Bureau of Statistics has developed its own sampling frame for all urban areas of the country. Each city/town has been divided into a number of enumeration blocks. Each enumeration block consists of 200-250 households on the average with well-defined boundaries and maps. The sampling frame i.e. lists of enumeration blocks as up-dated through Economic Census 2003-04 and the lists of villages/mouzas/dehs published by Population Census Organization as a result of 1998 Population Census have been taken as sampling frame. Enumeration blocks and villages are considered as primary sampling unites (PSUs) for urban and rural domain respectively.

    Stratification a) Urban Domain i) Large Sized Cities Karachi, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Sialkot, Sargodha, Bahawapur, Hyderabad, Sukkur, Peshawar, Quetta and Islamabad are considered as large sized cities. Each of these cities constitutes a separate stratum which is further sub-stratified according to low, middle, high income groups based on the information collected in respect of each enumeration block at the time of demarcation/up-dating of urban area sampling frame. ii) Remaining urban areas After excluding the population of large sized cities from the population of respective administrative division, the remaining urban population of administrative division of four provinces is grouped together to form a stratum called other urban. Thus ex-division in remaining urban areas in the four provinces constitutes a stratum. b) Rural Domain In rural domain, each administrative district in the Punjab, Sindh and NWF Provinces is considered as independent and explicit stratum whereas, in Balochistan, each administrative division constitutes a stratum.

    Sample size and its Allocation Keeping in view the resources available, a sample size of 19600 sample households has been considered appropriate to provide estimates of key characteristics at the desired level. The entire sample of households (SSUs) has been drawn from 1388 Primary Sampling Units (PSUs) out of which 652 are urban and 736 are rural. In order to control seasonal variation etc. sample has been distributed evenly over four quarters. This has facilitated to capture the variation due to any seasonal activity as urban population is more heterogeneous therefore, a higher proportion of sample size has been allocated to urban domain. Similarly NWFP and Balochistan being the smaller province, have been assigned higher proportion of sample in order to get reliable estimates. After fixing the sample size at provincial level, further distribution of sample PSUs to different strata in rural and urban domains in each province has been made proportionately.

    Sample Design A three-stage stratified sample design has been adopted for the survey. Sample Selection Procedure a) Selection of Primary Sampling Unites (PSUs) Enumeration blocks in urban domain and mouzas/dehs/villages in rural domain are taken as primary sampling unites (PSUs). In the urban domain, sample PSUs from each ultimate stratum/sub-stratum is selected with probability proportional to size (PPS) method of sampling scheme. In urban domain, the number of households in enumeration block as up-dated through Economic Census 2003-04 and population of 1998 Census for each village/mouza/deh are considered as measure of size. b) Section of Secondary Sampling Units (SSUs) Households within sample PSUs are taken as secondary sampling unites (SSUs). A specified number of households i.e. 12 from each urban sample PSU and 16 from each rural sample PSU are selected with equal probability using systematic sampling technique with a random start. Different households are selected in each quarter. c) Selection of Third Stage Sampling Units i.e. Individuals/Persons (TSUs) From the sample households, individuals/persons aged 10+ years within each sample households (SSUs) have been taken as third stage sampling units (TSUs). Two individuals aged 10 years and above among the eligible individuals/persons from each sample household have been interviewed using a selection grid.The grid and selection steps are detailed on p13 of the survey report available under external resources.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The questionnaire has been framed in the light of contemporary precedents and practices in vogue in the developing countries. The recommendations of Gender Responsive Budgeting Initiatives (GRBI) expert who visited Pakistan in June 2006 have been taken into account. Further, the advice of local experts hailing both from data producing and using agencies has also been considered. Survey Questionnaire and Manual of Instructions, for the Supervisors & Enumerators, was finalized jointly by Federal Bureau of Statistics and GRBI Project staff. The questionnaire was also pre-tested and reviewed accordingly. The questionnaire adopted for the survey is given at Annexure-A. All the households selected in the sample stand interviewed. Diary part of the questionnaire is filled-in from two respondents selected from each of the enumerated households. The questionnaire consists of the following six parts. Section-1: Identification of the area, respondents, detail of field visits and staff entrusted with supervision, editing and coding. Section-2: Detailed information about the socio-economic and demographic particulars of the selected households and individuals. Some of the important household characteristics i.e. ownership status and type of the household, earthquake damage, household items, sources of energy, drinking water, transport, health & education facilities, sources of income, monthly income, age and sex composition of the population. Section-3: Demographic detail such as age, sex, marital status, educational level, having children, employment status, source of income etc. of the selected respondent of that household Section-4: Comprised of diary to record the activities performed by the first selected respondent through the 24 hours period between 4.00 a.m. of the day preceding the day of interview and 3.00 a.m. on the day of the interview. Section-5 and 6 pertain to the second selected respondent of the selected household. The diary which is the core instrument of the time use study is divided into forty eight half-hour slots. An open ended question about the activities performed during the thirty minutes was asked from the respondent. Provision for minimum of recording three activities through half hour slot was made. In case of reporting more than one activity, the respondent was probed whether these activities were carried out simultaneously or one after the other. Similarly, the two locations of performing the activities were also investigated in the diary part of the questionnaire. The activities recorded in the diary are then coded by the field enumerator according to the activity classification given at Annex-B.

    Cleaning operations

    Soon after data collection, the field supervisors manually clean, edit and check the filled in questionnaire and refer back to field where necessary. This does not take much time since most of the manual editing is done in the field. Further editing is done by the subject matter section at the Headquarter. Also during data entry, further editing of error identified by applying computer edit checks is done. In edit checks, data ranges in numerical values are used to eliminate erroneous data as a result of mistakes made during coding. Thus, the survey records are edited and corrected through a series of computer processing stages.

  17. W

    Census 2016 Preliminary Results

    • cloud.csiss.gmu.edu
    • data.europa.eu
    • +1more
    csv, html, url
    Updated Aug 16, 2019
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    Ireland (2019). Census 2016 Preliminary Results [Dataset]. https://cloud.csiss.gmu.edu/uddi/bg/dataset/census-2016-preliminary-results
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    html, url, csvAvailable download formats
    Dataset updated
    Aug 16, 2019
    Dataset provided by
    Ireland
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    On 14th June 2016, the Central Statistics Office (CSO) released the Preliminary Report for Census 2016. The preliminary results are the initial count of the census. They are based on the summary counts for each enumeration area which were compiled by the 4,663 census enumerators and which have been returned to the CSO in advance of the census forms themselves. Further detailed results will be released in different phases as they become available during 2017, commencing with the Principal Demographic Results

  18. e

    South African Social Attitudes Survey (SASAS) 2009: Questionnaire 2 Cell...

    • b2find.eudat.eu
    Updated Jul 29, 2025
    + more versions
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    (2025). South African Social Attitudes Survey (SASAS) 2009: Questionnaire 2 Cell phone usage - All provinces - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/bb716301-dbfc-5506-9e20-41805642e99d
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    Dataset updated
    Jul 29, 2025
    Area covered
    South Africa
    Description

    Description: Topics included in the questionnaire two are cell phone usage, Batho Pele, voting, demographics and other classificatory variables. The data set has 3307 cases and 122 variables. Abstract: The primary objective of the South African Social Attitudes Survey (SASAS) is to design, develop and implement a conceptually and methodologically robust study of changing social attitudes and values in South Africa. In meeting this objective, the HSRC is carefully and consistently monitoring and providing insight into changes in attitudes among various socio-demographic groupings. SASAS is intended to provide a unique long-term account of the social fabric of modern South Africa, and of how its changing political and institutional structures interact over time with changing social attitudes and values. The survey has been designed to yield a national representative sample of adults aged 16 and older, using the Human Sciences Research Council's (HSRC) second Master Sample, which was designed in 2007 and consists of 1000 primary sampling units (PSUs). These PSUs were drawn, with probability proportional to size from a pre-census 2001 list of 80780 enumerator areas (EAs). As the basis of the 2009 SASAS round of interviewing, a sub-sample of 500 EAs (PSUs) was drawn from the second master sample. Three explicit stratification variables were used, namely province, geographic type and majority population group. The survey is conducted annually and the 2009 survey is the seventh wave in the series. Face-to-face interview National population: Adults (aged 16 and older) The South African Social Attitudes Survey (SASAS) is a nationally representative survey series that has been conducted on an annual basis by the Human Sciences Research Council's (HSRC) since 2003. The survey has been designed to yield a representative sample of adults aged 16 years and older. The sampling frame for the survey is the HSRC's second Master Sample, which was designed in 2007 and consists of 1 000 primary sampling units (PSUs). The 2001 population census enumerator areas (EAs) were used as PSUs. These PSUs (EAs) were drawn, with probability proportional to size, from a sampling frame created by Professor David Stoker containing all 80,787 of the 2001 EAs. This sampling frame uses the estimated number of dwelling units (DUs) in an EA (PSU) as a measure of size. The sampling frame was annually updated to coincide with StatsSA's mid-year population estimates in respect of the following variables: province, gender, population group and age group. In updating the 2007 version of this sampling frame, additional use was made of (a) the GeoTerraImage (GTI) residential structure count in all metropolitan EAs in 2004/2006 and (b) the ESKOM counts of dwelling units in all cities, towns, townships and villages. The HSRC's second master sample excludes special institutions (such as hospitals, military camps, old age homes, school and university hostels), recreational areas, industrial areas, vacant EAs as well as the 1000 EAs included in the first HSRC's master sample (2003-2006). It therefore focuses on dwelling units or visiting points as secondary sampling units (SSUs), which have been defined as 'separate (non-vacant) residential stands, addresses, structures, flats, homesteads, etc.'. For the 2009 SASAS round of interviewing, a sub-sample of 500 PSUs was drawn from the HSRC's 2nd Master Sample. Three explicit stratification variables were used, namely province, geographic type and majority population group. Within each stratum, the allocated number of PSUs was drawn using proportional to size probability sampling with the estimated number of dwelling units in the PSU as measure of size. In each of these drawn PSUs, 14 dwelling units were selected and systematically grouped into two sub-samples of seven, each corresponding to the two SASAS questionnaire versions. Selection of individuals Interviewers called at each visiting point selected from the 2nd HSRC master sample and listed all those eligible for inclusion in the sample, that is, all persons currently aged 16 or over and resident at the selected visiting point. The interviewer then selected one respondent using a random selection procedure based on a Kish grid.

  19. TIGER/Line Shapefile, 2016, Series Information for the Point Landmark...

    • datasets.ai
    • catalog.data.gov
    • +1more
    0, 21, 55
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    U.S. Census Bureau, Department of Commerce, TIGER/Line Shapefile, 2016, Series Information for the Point Landmark State-based Shapefile [Dataset]. https://datasets.ai/datasets/tiger-line-shapefile-2016-series-information-for-the-point-landmark-state-based-shapefile
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    21, 0, 55Available download formats
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    U.S. Census Bureau, Department of Commerce
    Description

    The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

                   The Census Bureau includes landmarks in the MTDB for locating special features and to help enumerators during field operations. Some
                   of the more common landmark types include area landmarks such as airports, cemeteries, parks, mountain peaks/summits, schools, and
                   churches and other religious institutions. The Census Bureau has added landmark features to MTDB on an as-needed basis and made no
                   attempt to ensure that all instances of a particular feature were included. The presence or absence of a landmark such as a hospital
                   or prison does not mean that the living quarters associated with that landmark were geocoded to that census tabulation block or
                   excluded from the census enumeration.
    
  20. A

    Malawi - INFORM-based prioritization of Enumeration Areas

    • data.amerigeoss.org
    • cloud.csiss.gmu.edu
    • +1more
    csv, shp
    Updated Jun 19, 2024
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    UN Humanitarian Data Exchange (2024). Malawi - INFORM-based prioritization of Enumeration Areas [Dataset]. https://data.amerigeoss.org/ca/dataset/inform-based-prioritization-of-enumeration-areas-in-malawi
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    csv(1528894), csv(184268), shp(21066915)Available download formats
    Dataset updated
    Jun 19, 2024
    Dataset provided by
    UN Humanitarian Data Exchange
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Malawi
    Description

    A crude version of the INFORM risk-framework is applied to Enumeration Areas (which is unofficial, but is deeper than admin-3), in Southern Malawi. This is done specifically for area selection regarding the ECHO2 project in 3 TA's: Mwambo (Zomba district), Makhwira (Chikwawa district) and Ndamera (Nsanje district).

    Scope

    Enumeration areas are retrieved from http://www.masdap.mw/layers/geonode%3Aeas_bnd. These are used, because we want to prioritize on a deeper level than Traditional Authority (admin-3) level, and there are no other official boundaries available.

    The dataset in principle data for the whole of Malawi, but contains 4 filters, which can be applied, which are the following:

    • Filter_south: this filters out only the South of Malawi, for which the drough and flood analysis has been carried out (see details below).
    • Filter_district: contains all EA's from the 3 pre-identified districts Zomba, Chikwawa and Nsanje.
    • Filter_TA: contains all EA's from the 3 pre-identified TAs Mwambo, Makhwira and Ndamera.
    • Filter_GVH: there are also 44 Group Village Heads pre-identified for the project. As these GVH's are points on a map, all EA's are selected here which have a GVH within their boundaries or very close to their boundaries.

    INFORM risk-framework

    The INFORM framework (http://www.inform-index.org/) is applied to assess risk per community, which is considered the main criteria for prioritization within the project.

    Because of low data availability we apply a crude version for now, with only some important indicators of the framework actually used. Since we feel that these indicators (see below) still constitute together a current good assessment of risk, and we want to stimulate the use and acceptance of the INFORM-framework, we choose to use it anyway.

    The INFORM risk-score consists of 3 main components: hazards, vulnerability and coping capacity.

    • Hazard: For hazard we focus - in line with the ECHO2 project - on floods and droughts only. Analysis has been carried out (see more details below), to determine flood and drought risk on a scale from 0-10 with a resolution of 250meter grid cells. This has subsequently been aggregated to Enumeration Areas, by taking a population-weighted average. Thereby taking into account where people actually live within the Enumeration Areas. (Population data source: Worldpop: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00155)

    • Vulnerability: Vulnerability is operationalized here through poverty incidence. Poverty rate (living below $1.25/day) is retrieved from Worldpop (http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00157) and again transformed from a 1km resolution grid to Enumeration Areas through a population-weighted average.

    • Lack of Coping capacity: Coping capacity is measured through traveltimes to various facilities, namely traveltime to nearest hospistal, traveltime to nearest trading centre and traveltime to nearest secondary school. Together these are all proxies of being near/far to facilities, and thereby an indicator of having higher/lower coping capacity. See https://510.global/developing-and-field-testing-a-remoteness-indicator-in-malawi/ for more information on how these traveltimes were calculated and validated.

    Use

    All features are stored in a CSV, but can easily be joined to the geographic shapefile to make maps on EACODE.

    Flood and Drought calculations

    Drought layer

    The drought risk map was created by analyzing rainfall data in the past 20 years using standard precipitation index (SPI) , which is a widely used index in drought analysis. Based on SPI6 values for the period October-march, which is the main rainy season in Malawi. Each pixel is classified to drought or no drought for each year based on SPI6 values, drought year if SPI value for a pixel is less than -1. Next, relative frequency is calculated, the number of times drought has occurred in the considered 20 year period. This frequency is then converted to probability of drought occurrence in a given year. We validated our analysis by comparing NDVI values for the drought year against long term average values.

    Flood layer

    To identify flood moments in Malawi Landsat imagery was studied (1984-2017). Floods were clearly evidenced in 9 dates. For the clearest and most representative layers the mNDWI (modified Normalized Water Index) was calculated. The index mNDWI (McFeeters 1996; Xu 2006) for Landsat bands is calculated as follows: (b2GREEN-b7MIRSWIR/b2GREEN+b7MIRSWIR). In this variation of the index the higher values are the wettest. A threshold was applied to the mNDWI to separate flood from non-flood or water from non-water pixels. The resulting layers were aggregated and the final stretched from 0-10, where 0 are the pixels where no flood is expected while pixels with 10 are where most frequent flood has been evidenced and therefore expected. The largest flood was observed in 2015, as the scenes were cloudy the flood extent was manually interpreted from several scenes. The evidenced flood dates are: 29 Feb. 1988 low flood, 19 march 1989, 17 march 1997, Feb 1998, March 1999 low flood, 2001 since February 16 until end of April, 2007 17 February since early Feb., 2008 Feb. medium flood, 2015 January – March. The water bodies in this layer are not represented and have a value of 0 like the rest of land where flood is absent.

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Qader, Sarchil; Kuepie, K; Tatem, Andrew (2024). Automatic national census pre-Enumeration Areas for Zimbabwe in 2021, version 1.0 [Dataset]. http://doi.org/10.5258/SOTON/WP00797

Automatic national census pre-Enumeration Areas for Zimbabwe in 2021, version 1.0

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Dataset updated
Dec 5, 2024
Dataset provided by
University of Southampton
Authors
Qader, Sarchil; Kuepie, K; Tatem, Andrew
Area covered
Zimbabwe
Description

Automatic national census pre-Enumeration Areas for Zimbabwe in 2021, version 1.0. These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the Geo-referenced Infrastructure and Demographic Data for Development (GRID3) programme, supported with funding from the Bill & Melinda Gates Foundation and the United Kingdom’s Foreign, Commonwealth & Development Office (OPP1182425). Programme partners included the United Nations Population Fund (UNFPA), the Center for International Earth Science Information Network (CIESIN) within the Earth Institute at Columbia University, and the Flowminder Foundation.

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