22 datasets found
  1. e

    Tanzania - Population density - Dataset - ENERGYDATA.INFO

    • energydata.info
    Updated Sep 20, 2022
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    (2022). Tanzania - Population density - Dataset - ENERGYDATA.INFO [Dataset]. https://energydata.info/dataset/tanzania-population-density-2015
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    Dataset updated
    Sep 20, 2022
    License

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

    Area covered
    Tanzania
    Description

    Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Tanzania data available from WorldPop here. Data and Resources TIFF Tanzania - Population density (2015)

  2. W

    United Republic of Tanzania - Population

    • cloud.csiss.gmu.edu
    • data.wu.ac.at
    geotiff
    Updated Jun 18, 2019
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    UN Humanitarian Data Exchange (2019). United Republic of Tanzania - Population [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/worldpop-united-republic-of-tanzania-population
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    geotiffAvailable download formats
    Dataset updated
    Jun 18, 2019
    Dataset provided by
    UN Humanitarian Data Exchange
    Area covered
    Tanzania
    Description

    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets

    WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

  3. POMELO - Tanzania High Resolution Population Density

    • data.amerigeoss.org
    • data.humdata.org
    geotiff
    Updated Sep 13, 2023
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    UN Humanitarian Data Exchange (2023). POMELO - Tanzania High Resolution Population Density [Dataset]. https://data.amerigeoss.org/dataset/pomelo-tanzania-high-resolution-population
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    geotiff(41078559)Available download formats
    Dataset updated
    Sep 13, 2023
    Dataset provided by
    United Nationshttp://un.org/
    License

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

    Area covered
    Tanzania
    Description

    This dataset presents a fine-grained population map of Tanzania with a resolution of 100 meters for 2020, generated using the POMELO super-resolution technique that is based on deep learning. Please refer to our Nature Scientific Reports publication for more details.

    Background: Traditionally, many countries, including those in sub-Saharan Africa, rely on aggregated census data over expansive spatial units, which are not always timely or accurate. The need for detailed population maps is paramount in several sectors, including urban development, environmental supervision, public health, and humanitarian initiatives. Addressing this gap, the POMELO methodology leverages coarse census data in conjunction with open geodata to produce high precision population maps.

    Key Features: Resolution: The map offers a granular view with a 100m ground sampling distance, providing intricate details about population distributions in Tanzania. Data Sources: Utilizing a combination of projected admisistrative census data (UN), and supplementing it with open geodata. Reliability: In comparative experiments conducted in sub-Saharan Africa, POMELO's ability to disaggregate coarse census counts achieved R2 values of 85-89%. Furthermore, its potential to predict population numbers without any census data reached accuracy levels of 48-69%.

  4. a

    Average Household Size in Tanzania

    • africageoportal.com
    Updated Jul 5, 2013
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    Esri (2013). Average Household Size in Tanzania [Dataset]. https://www.africageoportal.com/maps/0daa0d195c844fcb91b2f7936614d081
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    Dataset updated
    Jul 5, 2013
    Dataset authored and provided by
    Esri
    Area covered
    Description

    This map shows the average household size in Tanzania in 2019, in a multiscale map (Country and Region). Nationally, the average household size is 4.8 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by marital status The source of this data is Michael Bauer Research. The vintage of the data is 2019.Additional Esri Resources:Esri DemographicsPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

  5. Population of Tanzania 1800-2020

    • statista.com
    Updated Aug 12, 2024
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    Statista (2024). Population of Tanzania 1800-2020 [Dataset]. https://www.statista.com/statistics/1067105/total-population-tanzania-historical/
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    Dataset updated
    Aug 12, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Tanzania
    Description

    In 1800, the population of the region now known as Tanzania was just over three million people. Throughout the 19th century, both as a collection of small kingdoms and later as a German colony (1897), Tanzania would see modest growth in its population, growing from three million at the start of the century to approximately four million by 1900. Following the handover of the region (then known as German East Africa) to the United Kingdom in 1919, the region would begin to see first a noticeable, and later an exponential increase in its population, rising to just under 8 million by 1950, and 33.5 million by the century’s end. This growth would slow somewhat beginning in the late 1980s to 1990s, in part the result of a rapid spread of the HIV/AIDS epidemic throughout the country. By 2020, Tanzania will have a population just under 60 million.

  6. e

    Tanzania - Region & District Boundary - Dataset - ENERGYDATA.INFO

    • energydata.info
    Updated Jul 23, 2024
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    (2024). Tanzania - Region & District Boundary - Dataset - ENERGYDATA.INFO [Dataset]. https://energydata.info/dataset/tanzania-region-district-boundary-2012
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    Dataset updated
    Jul 23, 2024
    License

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

    Area covered
    Tanzania
    Description

    The datasets are curated from the Tanzania National Bureau of Statistics (NBS) 2012 Population and Housing Census (PHC) of Tanzania which was preceded by the preparatory geographic work, which involved field visiting of all regions, districts, wards/shehia, villages/mitaa, localities and sub-villages in the country, primarily to create and delineate Enumeration Area boundaries (EAs) so as to produce maps required for census operations. The most important principle followed in delineating an EA was that under no circumstance should an EA overlap the existing administrative boundaries of regions, districts, wards/shehia or villages/mitaa. Adherence to this principle was necessary since the census results were to be presented at the level of these administrative units. The National Bureau of Statistics (NBS) intends to provide a geo-database with spatial and non-spatial information at five levels of geography, to facilitate presentation of data from censuses and other surveys. These levels are regional (level one), district (level two), ward/shehia (level three), villages/mitaa (level four) and enumeration areas (level five). Levels one and two have been put onto the NBS website in June, 2013 for use by various stakeholders, and the web-page will be updated to include other levels of shapefiles when they are ready for use. To learn more, please visit website https://sensa.nbs.go.tz/

  7. Average Household Size in Tanzania

    • wb-sdgs.hub.arcgis.com
    Updated Jul 5, 2013
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    Esri (2013). Average Household Size in Tanzania [Dataset]. https://wb-sdgs.hub.arcgis.com/maps/esri::average-household-size-in-tanzania
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    Dataset updated
    Jul 5, 2013
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This map shows the average household size in Tanzania in 2023, in a multiscale map (Country and Region). Nationally, the average household size is 4.6 people per household. It is calculated by dividing the household population by total households.The pop-up is configured to show the following information at each geography level:Average household size (people per household)Total populationTotal householdsCounts of population by marital status The source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

  8. f

    Population Growth Indicators (%)(Countrystat - Tanzania - National)

    • data.apps.fao.org
    Updated Jul 12, 2024
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    (2024). Population Growth Indicators (%)(Countrystat - Tanzania - National) [Dataset]. https://data.apps.fao.org/map/catalog/srv/resources/datasets/D3S_80865015762009003858276173279879269442
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    Dataset updated
    Jul 12, 2024
    Area covered
    Tanzania
    Description

    Table content - YEAR - INDICATOR - RESIDENCE - VALUE - FLAG - UNIT Value assigned to No-data:m

  9. Tanzania Mkoa Boundaries

    • rwanda.africageoportal.com
    • cartong-esriaiddev.opendata.arcgis.com
    • +1more
    Updated Sep 21, 2023
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    Esri (2023). Tanzania Mkoa Boundaries [Dataset]. https://rwanda.africageoportal.com/maps/e722f8b63e214664b436710c0ecd7255
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    Dataset updated
    Sep 21, 2023
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    Tanzania Mkoa Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.The 2023 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Tanzania National Bureau of Statistics 2018. These were published in October 2023. A new layer will be published in 12-18 months. Other administrative boundaries for this country are also available: Country

  10. w

    Demographic and Health Survey and Malaria Indicator Survey 2022 - Tanzania

    • microdata.worldbank.org
    • datacatalog.ihsn.org
    • +1more
    Updated Oct 31, 2023
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    National Bureau of Statistics (NBS) (2023). Demographic and Health Survey and Malaria Indicator Survey 2022 - Tanzania [Dataset]. https://microdata.worldbank.org/index.php/catalog/6102
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    Dataset updated
    Oct 31, 2023
    Dataset provided by
    Office of the Chief Government Statistician Zanzibar (OCGS)
    National Bureau of Statistics (NBS)
    Time period covered
    2022
    Area covered
    Tanzania
    Description

    Abstract

    The primary objective of the 2022 Tanzania Demographic and Health Survey and Malaria Indicator Survey (2022 TDHSMIS) is to provide current and reliable information on population and health issues. Specifically, the 2022 TDHS-MIS collected information on marriage and sexual activity, fertility and fertility preferences, family planning, infant and child mortality, maternal health care, disability among the household population, child health, nutrition of children and women, malaria prevalence, knowledge, and communication, women’s empowerment, women’s experience of domestic violence, adult maternal mortality via sisterhood method, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), female genital cutting, and early childhood development. Other information collected on health-related issues included smoking, blood pressure, anaemia, malaria, and iodine testing, height and weight, and micronutrients.

    The information collected through the 2022 TDHS-MIS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of Tanzania’s population. The 2022 TDHS-MIS also provides indicators to monitor and evaluate international, regional, and national programmes, such as the Global Agenda 2030 on Sustainable Development Goals (2030 SDGs), Tanzania Development Vision 2025, the Third National Five-Year Development Plan (FYDP III 2021/22–2025/26), East Africa Community Vision 2050 (EAC 2050), and Africa Development Agenda 2063 (ADA 2063).

    Geographic coverage

    National coverage

    Analysis unit

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

    Universe

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

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The sample design for the 2022 TDHS-MIS was carried out in two stages and was intended to provide estimates for the entire country, for urban and rural areas in Tanzania Mainland, and for Zanzibar. For specific indicators such as contraceptive use, the sample design allows for estimation of indicators for each of the 31 regions—26 regions in Tanzania Mainland and 5 regions in Zanzibar.

    The sampling frame excluded institutional populations, such as persons in hospitals, hotels, barracks, camps, hostels, and prisons. The 2022 TDHS-MIS followed a stratified two-stage sample design. The first stage involved selection of sampling points (clusters) consisting of enumeration areas (EAs) delineated for the 2012 Tanzania Population and Housing Census (2012 PHC). The EAs were selected with a probability proportional to their size within each sampling stratum. A total of 629 clusters were selected. Among the 629 EAs, 211 were from urban areas and 418 were from rural areas.

    In the second stage, 26 households were selected systematically from each cluster, for a total anticipated sample size of 16,354 households for the 2022 TDHS-MIS. A household listing operation was carried out in all the selected EAs before the main survey. During the household listing operation, field staff visited each of the selected EAs to draw location maps and detailed sketch maps and to list all residential households found in each EA with addresses and the names of the heads of the households. The resulting list of households served as a sampling frame for the selection of households in the second stage. During the listing operation, field teams collected global positioning system (GPS) data—latitude, longitude, and altitude readings—to produce one GPS point per EA. To estimate geographic differentials for certain demographic indicators, Tanzania was divided into nine geographic zones. Although these zones are not official administrative areas, this classification system is also used by the Reproductive and Child Health Section of the Ministry of Health. Grouping of regions into zones allows for larger denominators and smaller sampling errors for indicators at the zonal level.

    For further details on sample design, see APPENDIX A of the final report.

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Research instrument

    Five questionnaires were used for the 2022 TDHS-MIS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, and the Micronutrient Questionnaire. The questionnaires, based on The DHS Program’s Model Questionnaires, were adapted to reflect the population and health issues relevant to Tanzania. In addition, a self-administered Fieldworker’s Questionnaire collected information about the survey’s fieldworkers.

    Cleaning operations

    In the 2022 TDHS-MIS survey, CAPI was used during data collection. The devices used for CAPI were Android-based computer tablets programmed using a mobile version of CSPro. Programming of questionnaires into the android application was done by ICF, while configuration of tablets was done by NBS and OCGS in collaboration with ICF. All fieldwork personnel were assigned usernames, and devices were password protected to ensure the integrity of the data collected. Selected households were assigned to CAPI supervisors, whereas households were assigned to interviewers’ tablets via Bluetooth. The data for all interviewed households were sent back to CAPI supervisors, who were responsible for initial data consistency and editing, before being sent to the central servers hosted at NBS Headquarters via Syncloud.

    The data processing of the 2022 TDHS-MIS ran concurrently with the data collection exercise. The electronic data files from each completed cluster were transferred via Syncloud to the NBS central office server in Dodoma. The data files were registered and checked for inconsistencies, incompleteness, and outliers. Errors and inconsistencies were communicated to the field teams for review and correction. Secondary central data editing was done by NBS and OCGS survey staff at the central office. A CSPro batch editing tool was used for cleaning data and included coding of open-ended questions and resolving inconsistencies.

    The Biomarker paper questionnaires were collected by field supervisors and compared with the electronic data files to check for any inconsistencies that may have occurred during data entry. The concurrent data collection and processing offered an advantage because it maximised the likelihood of having error-free data. Timely generation of field check tables allowed effective monitoring. The secondary data editing exercise was completed in October 2022.

    Response rate

    A total of 16,312 households were selected for the 2022 TDHS-MIS sample. This number is slightly less than the targeted sample size of 16,354 because one EA could not be reached due to security reasons, while a few EAs had less than the targeted 26 households. Of the 16,312 households selected, 15,907 were found to be occupied. Of the occupied households, 15,705 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 15,699 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 15,254 women, yielding a response rate of 97%. In the subsample (50% of households) of households selected for the male questionnaire, 6,367 men age 15–49 were identified as eligible for individual interviews, and 5,763 were successfully interviewed, yielding a response rate of 91%.

    Sampling error estimates

    The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Tanzania Demographic and Health Survey and Malaria Indicator Survey (2022 TDHS-MIS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

    Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 TDHS-MIS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

    A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

    If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 TDHS-MIS sample was the result of a multistage stratified design, and,

  11. i

    Afrobarometer Survey 2005-2006 - Africa

    • dev.ihsn.org
    Updated Apr 25, 2019
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    Institute for Democracy in South Africa (IDASA) (2019). Afrobarometer Survey 2005-2006 - Africa [Dataset]. https://dev.ihsn.org/nada/catalog/study/AFR_2005_AFB-18_v01_M
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    Dataset updated
    Apr 25, 2019
    Dataset provided by
    Michigan State University (MSU)
    Ghana Centre for Democratic Development (CDD-Ghana)
    Institute for Democracy in South Africa (IDASA)
    Time period covered
    2005 - 2006
    Area covered
    Africa
    Description

    Abstract

    The Afrobarometer project assesses attitudes and public opinion on democracy, markets, and civil society in several sub-Saharan African.This dataset was compiled from the studies in Round 3 of the Afrobarometer survey, conducted from 2005-2006 in 18 African countries (Benin, Botswana, Cape Verde, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South Africa, Tanzania, Uganda, Zambia, Zimbabwe).

    Geographic coverage

    The Afrobarometer surveys have national coverage

    Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe Ghana Mali Nigeria Tanzania Uganda Cape Verde Mozambique Senegal Kenya Benin Madagascar

    Analysis unit

    Basic units of analysis that the study investigates include: individuals and groups

    Universe

    The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

    What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

    The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

    Sample Universe

    The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

    What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

    Sample Design

    The sample design is a clustered, stratified, multi-stage, area probability sample.

    To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

    In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

    The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

    A first-stage to stratify and randomly select primary sampling units;

    A second-stage to randomly select sampling start-points;

    A third stage to randomly choose households;

    A final-stage involving the random selection of individual respondents

    We shall deal with each of these stages in turn.

    STAGE ONE: Selection of Primary Sampling Units (PSUs)

    The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

    We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

    Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

    Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

    Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

    Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

    The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

    These PSUs should then be allocated

  12. Data from: Geospatial based model for malaria risk prediction in Kilombero...

    • data.niaid.nih.gov
    • search.dataone.org
    • +2more
    zip
    Updated Jul 7, 2023
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    Stephen Mwangungulu; Emmanuel Kaindoa; Dorothea Deus; Zakaria Ngereja (2023). Geospatial based model for malaria risk prediction in Kilombero Valley, south-eastern Tanzania [Dataset]. http://doi.org/10.5061/dryad.d51c5b081
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    zipAvailable download formats
    Dataset updated
    Jul 7, 2023
    Dataset provided by
    Ifakara Health Institutehttp://www.ihi.or.tz/
    Ardhi University
    Authors
    Stephen Mwangungulu; Emmanuel Kaindoa; Dorothea Deus; Zakaria Ngereja
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Area covered
    Tanzania
    Description

    Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

    Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
    Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

  13. Population in Africa 2025, by selected country

    • statista.com
    Updated Jun 24, 2025
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    Statista (2025). Population in Africa 2025, by selected country [Dataset]. https://www.statista.com/statistics/1121246/population-in-africa-by-country/
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    Dataset updated
    Jun 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2025
    Area covered
    Africa
    Description

    Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.

  14. Measuring Statelessness: A Study of the Pemba - 2016 - Kenya

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated May 27, 2021
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    Norway Refugee Council (2021). Measuring Statelessness: A Study of the Pemba - 2016 - Kenya [Dataset]. https://microdata.worldbank.org/index.php/catalog/4005
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    Dataset updated
    May 27, 2021
    Dataset provided by
    United Nations High Commissioner for Refugeeshttp://www.unhcr.org/
    Norway Refugee Council
    Time period covered
    2016
    Area covered
    Kenya
    Description

    Abstract

    The survey of the Pemba was an attempt to reach all households in Kenya with links to Pemba in Tanzania. It was conducted in the two counties of Kilifi and Kwale on the coast, north and south of Mombasa, respectively. According to information from village elders familiar with the Pemba community in Kenya, most of the Pemba population resides in these two counties. While there are some Pemba residents in Lamu, the security situation prevented data collection there. Further, a few Pemba are believed to live in the city of Mombasa and elsewhere in the country. But due to lack of further information, no data were collected in Mombasa or elsewhere.

    The objectives of the full survey, conducted in August 2016, were: 1. To establish the number and characteristics of the Pemba living in Kenya, including their arrival period in Kenya, nationality and their problems; 2. To make recommendations for the issuance of the documentation that is required for those who apply for citizenshiop by registration

    Geographic coverage

    Kwale and Kilifi counties, Kenya.

    Analysis unit

    Households, individuals

    Universe

    The total number of households with links to Pemba in Tanzania, in Kilifi and Kwale counties.

    Kind of data

    Census/enumeration data [cen]

    Sampling procedure

    A household mapping exercise was conducted in Kilifi and Kwale to identify Pemba households and to make it easier to locate them on the ground. The mapping was done from 4 to 12 August 2016 by a team from UNHCR Kenya office and KNBS.

    The mapping in each village commenced with a visit to the chief's office, who put the team in touch with the village chair. The team explained the purpose of its visit to the village chair and began the mapping exercise. The importance of involving the chiefs and village chairpersons is that they are well connected, recognised and trusted by residents in their communities. The same procedure is followed by KNBS when they are mapping for sample surveys and censuses.

    The team established physical boundaries of the area to be mapped, located the boundaries on the map and then identified and listed the Pemba households within the enumeration boundary. A Pemba household, in this context, is one identified by the informants as having at least one person with origins or links to Pemba. The links may include a person's spouse, parents or grandparents, who migrated to Kenya from Pemba or where a person has migrated from Pemba to Kenya.

    The mapping team was followed by the village chair to the Pemba households, where the UNHCR and Haki Centre staff listed number of persons in each, while the KNBS staff marked the location of the household on the map. The entrances of identified Pemba households were marked in chalk with the letters HCR and a number starting at 001 to make it easier to find the houses during the enumeration. Since it seems to be generally well known where the Pemba live it was not considered stigmatising to mark their doors. During the feedback forums with the Pemba after the survey, there was no mention of stigmatization due to marking the door with chalk.

    The maps were from the 2009 national housing and population census, purchased from KNBS. The team made lists with information about the location, number and size of each household. The mapping team visited 17 villages in Kilifi and Kwale (see Table 1 in Section 2.7). All villages visited were identified before the mapping exercise by key informants as locations being home to the Pemba of Kenya. The key informants were Pemba elders in different sub-counties previously identified for providing background information on the Pemba arrival and history in Kenya. In each sub-country, the chief, the assistant chief or the village chair also accompanied the team. In Kwale, 358 households were identified with 2,220 persons, and in Kilifi, 86 households with 558 persons.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The questionnaire was developed before the pilot survey and revised during and after the pilot survey, based on the experience gained. The pilot survey was used to test the questions and to check for inconsistences and misinterpretations due to unclear concepts and definitions. The testing process also revealed some important themes that had been left out.

    The structure of the questionnaire was altered, including the order of the questions and the introductory pages, to facilitate administration of the questionnaire.

    Finally, the questionnaire was translated into Swahili. Both the English and Swahili versions were used in the survey, even though the English version was preferred by almost all interviewers. The two versions of the questionnaire are attached in Annex 4 and 5. Enumerators used the English questionnaire to frame the questions in the local and less academic version of Swahili.

    Cleaning operations

    The data were imported into a Statistics Analysis Software (SAS) file and validated. Several errors were identified during the validation process, both on how the data had been recorded by the interviewers in the field and how the data had been entered by the clerks. There were particularly many errors in the entry of the variable “Relation to the household head” (Q.2). There were also many errors in the entry of the age of the household head, which was mostly due to errors in recording the right codes. A substantial amount of time was spent cleaning the data after the data had been entered, which included consulting many paper questionnaires. The quality of the survey data was significantly improved after the data entry revision. The data were analysed using both SAS software and Excel spreadsheets.

    Response rate

    The rate of non-response was low. Of the 452 households visited, visits to only 23 households can be categorised as non-response. A lot of effort was made to revisit non-responding households, using interviewers living nearby. Out of the 23 non-responsive households, 12 were not at home or there was no adult at home. There were 2 interrupted interviews, 7 refusals and 2 with no links to Pemba. In one household the respondent was not mentally stable enough to be interviewed, according to the enumerator.

  15. Purchasing Power per Capita in Tanzania

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • africageoportal.com
    • +4more
    Updated Jul 6, 2013
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    Esri (2013). Purchasing Power per Capita in Tanzania [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/maps/esri::purchasing-power-per-capita-in-tanzania/explore?location=-6.742980%2C39.108000%2C7.21
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    Dataset updated
    Jul 6, 2013
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    This map shows the purchasing power per capita in Tanzania in 2023, in a multiscale map (Country and Region). Nationally, the purchasing power per capita is 1,557,856 Tanzanian shilling. Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Tanzanian shilling (TZS) per capita.The pop-up is configured to show the following information at each geography level:Purchasing power per capitaThe source of this data is Michael Bauer Research. The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.Additional Esri Resources:Esri DemographicsThis item is for visualization purposes only and cannot be exported or used in analysis.We would love to hear from you. If you have any feedback regarding this item or Esri Demographics, please let us know.Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

  16. W

    United Republic of Tanzania - Age and sex structures

    • cloud.csiss.gmu.edu
    geotiff
    Updated Jun 18, 2019
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    UN Humanitarian Data Exchange (2019). United Republic of Tanzania - Age and sex structures [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/worldpop-united-republic-of-tanzania-age-and-sex-structures
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    geotiffAvailable download formats
    Dataset updated
    Jun 18, 2019
    Dataset provided by
    UN Humanitarian Data Exchange
    Area covered
    Tanzania
    Description

    Age and sex structures: WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Tatem et al and Pezzulo et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020 structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map population age and sex counts for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).

  17. f

    Crop Storage Location Score: Cassava (Tanzania - ~ 1Km)

    • data.apps.fao.org
    Updated Jun 27, 2024
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    (2024). Crop Storage Location Score: Cassava (Tanzania - ~ 1Km) [Dataset]. https://data.apps.fao.org/map/catalog/srv/resources/datasets/7f0099f3-5f09-4c8a-a7a5-76da165c3f75
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    Dataset updated
    Jun 27, 2024
    Area covered
    Tanzania
    Description

    The raster dataset consists of a 1km score grid for cassava storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Port Accessibility" * 0.2) + (“Major Cities Weighted Accessibility” * 0.1) + (”Regional Cities Weighted Accessibility” * 0.1) This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

  18. f

    Tanzania medicinal plants

    • data.apps.fao.org
    Updated Nov 13, 2020
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    (2020). Tanzania medicinal plants [Dataset]. https://data.apps.fao.org/map/catalog/srv/search?keyword=Plants
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    Dataset updated
    Nov 13, 2020
    Area covered
    Tanzania
    Description

    Source: National Forest Resources Monitoring and Assessment of Tanzania (NAFORMA). 2015. Ministry of Natural Resources and Tourism, Tanzania. Methodology: The map shows the locations of NAFORMA clusters where medicinal plants were observed. Background: Non-timber forest products are important benefits that the forest provide to the population of Tanzania. REDD+ activities could be designed to assist communities to sustainably extract subsistence or income sources from the forest, where possible. Edible and medicinal plants were observed in most parts of the country. The NAFORMA inventory found that around 15% collected plant-based medicines.

  19. Extreme poverty as share of global population in Africa 2025, by country

    • statista.com
    Updated Feb 3, 2025
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    Statista (2025). Extreme poverty as share of global population in Africa 2025, by country [Dataset]. https://www.statista.com/statistics/1228553/extreme-poverty-as-share-of-global-population-in-africa-by-country/
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    Dataset updated
    Feb 3, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2025
    Area covered
    Africa
    Description

    In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

  20. f

    Crop Storage Location Score: Rice (Tanzania - ~ 1Km)

    • data.apps.fao.org
    Updated Nov 1, 2024
    + more versions
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    (2024). Crop Storage Location Score: Rice (Tanzania - ~ 1Km) [Dataset]. https://data.apps.fao.org/map/catalog/srv/resources/datasets/bbdd2924-2f27-4789-9db8-8f9c119532c4
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    Dataset updated
    Nov 1, 2024
    Area covered
    Tanzania
    Description

    The raster dataset consists of a 1km score grid for rice storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Port Accessibility" * 0.2) + (“Major Cities Weighted Accessibility” * 0.1) + (”Regional Cities Weighted Accessibility” * 0.1) This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

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(2022). Tanzania - Population density - Dataset - ENERGYDATA.INFO [Dataset]. https://energydata.info/dataset/tanzania-population-density-2015

Tanzania - Population density - Dataset - ENERGYDATA.INFO

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Dataset updated
Sep 20, 2022
License

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

Area covered
Tanzania
Description

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Tanzania data available from WorldPop here. Data and Resources TIFF Tanzania - Population density (2015)

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