24 datasets found
  1. A

    Africa Land Surface Forms

    • data.amerigeoss.org
    • rcmrd.africageoportal.com
    • +2more
    Updated Jul 23, 2019
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    SERVIR (2019). Africa Land Surface Forms [Dataset]. https://data.amerigeoss.org/sl/dataset/africa-land-surface-forms
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    Dataset updated
    Jul 23, 2019
    Dataset provided by
    SERVIR
    Area covered
    Africa
    Description

    The land surface forms were identified using the method developed by the Missouri Resource Assessment Partnership (MoRAP). The MoRAP method is an automated land surface form classification based on Hammond's (1964a, 1964b) classification. MoRAP made modifications to Hammond's classification, which allowed finer-resolution elevation data to be used as input data and analyses to be made using 1 km2 moving window (True, 2002; True et al., 2000). While Hammond's methodology was based on three variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief (True, 2002). Slope is classified as gently sloping or not gently sloping using a threshold value of 8%. Local relief, the difference between the maximum and minimum elevation in a 1km2 neighborhood for analysis, is classified into five classes (0-15m, 16-30m, 31-90m, 91-150m, and >150m). Slope classes and relief classes were subsequently combined to produce eight land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains). In the implementation for the contiguous United States, Sayre et al. (2009) further refined the MoRAP methodology to identify a new land surface form class, "high mountains/deep canyons", by using an additional local relief class (>400 m). This method was implemented for Africa using a void-filled 90m SRTM elevation dataset which was created from the 30m SRTM elevation data provided by the National Geospatial-Intelligence Agency. In the preliminary output, which had nine land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains, and high mountains/deep canyons), artifacts were identified over flat desert areas affecting the classification between the two lowest relief classes, "flat plains" and "smooth plains." Since this problem was especially pronounced in areas where the input SRTM elevation data originally had data-voids, the problem could have been caused by anomalies or artifacts in the input data, which resulted from the void-filling processes. Instead of further investigating causes of the problem, the two land surface form classes were combined. In addition, the "low hills" class which had a very low occurrence was combined with the "hills" class. As a result, seven land surface form classes were identified in the final dataset (smooth plains, irregular plains, escarpments, hills, breaks/foothills, low mountains, and high mountains/deep canyons). References: Hammond, E.H., 1964a. Analysis of Properties in Land Form Geography - An Application to Broad-Scale Land Form Mapping. Annals of the Association of American Geographers, v. 54, no. 1, p. 11-19. Hammond, E.H. 1964b. Classes of land surface form in the forty-eight states, U.S.A. Annals of the Association of American Geographers. 54(1): map supplement no. 4, 1: 5,000,000. Sayre, R., P. Comer, H. Warner, and J. Cress. 2009. A new map of standardized terrestrial ecosystems of the conterminous United States: U. S. Geological Survey professional Paper 1768, 17 p. True, D. 2002. Landforms of the Lower Mid-West. Missouri Resource Assessment Partnership. MoRAP Map Series MS-2003-001, scale 1:1,500,000. http://www.cerc.usgs.gov/morap/Assets/maps/Landforms_of_the_Lower_Mid-West_MS-2002-01.pdf. True, D., T. Gordon, and D. Diamond. 2000. How the size of a sliding window impacts the generation of landforms. Missouri Resources Assessment Partnership. http://www.cerc.cr.usgs.gov/morap/projects/landform_model/landforms2001_files/frame.htm.

  2. s

    Africa (Raster Image)

    • searchworks.stanford.edu
    zip
    Updated Oct 10, 2019
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    (2019). Africa (Raster Image) [Dataset]. https://searchworks.stanford.edu/view/ts198sr7769
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    zipAvailable download formats
    Dataset updated
    Oct 10, 2019
    License

    Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
    License information was derived automatically

    Description

    This layer is a georeferenced image of an historic continental map of Africa created around 1836. This map contains an accurate outline of the continent. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, or other information associated with the principal map. This map was georeferenced by the Stanford University Geospatial Center using an Azimuthal Equidistant Auxiliary Sphere projection. This map is part of a selection of digitally scanned and georeferenced historic maps of Africa held at Stanford University Libraries.

  3. Central African Republic: High Resolution Population Density Maps +...

    • data.amerigeoss.org
    • cloud.csiss.gmu.edu
    json, zip
    Updated Oct 25, 2022
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    UN Humanitarian Data Exchange (2022). Central African Republic: High Resolution Population Density Maps + Demographic Estimates [Dataset]. https://data.amerigeoss.org/tr/dataset/highresolutionpopulationdensitymaps-caf
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    zip(12625355), zip(24560281), json(5693935), zip(12646690), zip(12633195), zip(24563018), zip(24540485), zip(12631650), zip(12616928), zip(12598326), zip(24557931), zip(24560032), zip(12620827), zip(24545166), zip(24557472)Available download formats
    Dataset updated
    Oct 25, 2022
    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
    Central African Republic
    Description

    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in the Central African Republic: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

    There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

  4. a

    Digital Earth Africa's Cropland extents for Africa

    • africageoportal.com
    • deafrica.africageoportal.com
    • +4more
    Updated Jan 13, 2022
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    Africa GeoPortal (2022). Digital Earth Africa's Cropland extents for Africa [Dataset]. https://www.africageoportal.com/datasets/bc6a9440f3cb41d6904b2c8831745903
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    Dataset updated
    Jan 13, 2022
    Dataset authored and provided by
    Africa GeoPortal
    License

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

    Area covered
    Description

    A central focus for governing bodies in Africa is the need to secure the necessary food sources to support their populations. It has been estimated that the current production of crops will need to double by 2050 to meet future needs for food production. Higher level crop-based products that can assist with managing food insecurity, such as cropping watering intensities, crop types, or crop productivity, require as a starting point precise and accurate cropland extent maps indicating where cropland occurs. Current cropland extent maps are either inaccurate, have coarse spatial resolutions, or are not updated regularly. An accurate, high-resolution, and regularly updated cropland area map for the African continent is therefore recognised as a gap in the current crop monitoring services. Key PropertiesGeographic Coverage: Continental Africa - approximately 37° North to 35 SouthTemporal Coverage: 2019Spatial Resolution: 10 x 10 meterUpdate Frequency: TBDNumber of Bands: 3 BandsParent Dataset: Digital Earth Africa's Sentinel-2 Semiannual GeoMADSource Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)

    Digital Earth Africa’s cropland extent maps for Eastern, Western, and Northern Africa show the estimated location of croplands in these countries for the period of January to December 2019:

    Eastern: Tanzania, Kenya, Uganda, Ethiopia, Rwanda and BurundiWestern: Nigeria, Benin, Togo, Ghana, Cote d'Ivoire, Liberia, Sierra Leone, Guinea and Guinea-BissauNorthern: Morocco, Algeria, Tunisia, Libya and EgyptSahel: Mauritania, Senegal, Gambia, Mali, Burkina Faso, Niger, Chad, Sudan, South Sudan, Somalia and DjiboutiSouthern: South Africa, Namibia, Botswana, Lesotho and Eswatini

    Cropland is defined as:

    "a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvestable at least once within the 12 months after the sowing/planting date."

    This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation.

    The provisional cropland extent maps have a resolution of 10 metres and were built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent maps were built separately using extensive training data from Eastern, Western, and Northern Africa, coupled with a Random Forest machine learning model. A detailed exploration of the methods used to produce the cropland extent map can be found in the Jupyter Notebooks in DE Africa’s crop-mask GitHub repository.

    Independent validation datasets suggest the following accuracies:

    The Eastern Africa cropland extent map has an overall accuracy of 90.3 %, and an f-score of 0.85 The Western Africa cropland extent map has an overall accuracy of 83.6 %, and an f-score of 0.75 The Northern Africa cropland extent map has an overall accuracy of 94.0 %, and an f-score of 0.91The Sahel Africa cropland extent map has an overall accuracy of 87.9 %, and an f-score of 0.78The Southern Africa cropland extent map has an overall accuracy of 86.4 %, and an f-score of 0.75

    The algorithms for all regions tend to report more omission errors (labelling actual crops as non-crops) than commission errors (labelling non-crops as crops). Where commission errors occur, they tend to be focussed around wetlands and seasonal grasslands which spectrally resemble some kinds of cropping.

    Available BandsBand IDDescriptionValue rangeData typeNoData/Fill valuemaskcrop extent (pixel)0 - 1uint80probcrop probability (pixel)0 - 100uint80filteredcrop extent (object-based)0 - 1uint80

    mask: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is a pixel-based cropland extent map, meaning the map displays the raw output of the pixel-based Random Forest classification.

    prob: This band displays the prediction probabilities for the ‘crop’ class. As this service uses a random forest classifier, the prediction probabilities refer to the percentage of trees that voted for the random forest classification. For example, if the model had 200 decision trees in the random forest, and 150 of the trees voted ‘crop’, the prediction probability is 150 / 200 x 100 = 75 %. Thresholding this band at > 50 % will produce a map identical to mask.

    filtered: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has been filtered using an image segmentation algorithm (see this paper for details on the algorithm used). During this process, segments smaller than 1 Ha (100 10m x 10m pixels) are merged with neighbouring segments, resulting in a map where the smallest classified region is 1 Ha in size. The filtered dataset is provided as a complement to the mask band; small commission errors are removed by object-based filtering, and the ‘salt and pepper’ effect typical of classifying pixels is diminished.

    More details on this dataset can be found here.

  5. e

    Sub-Saharan Africa - Wind Speed and Wind Power Potential Maps - Dataset -...

    • energydata.info
    Updated Sep 27, 2020
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    (2020). Sub-Saharan Africa - Wind Speed and Wind Power Potential Maps - Dataset - ENERGYDATA.INFO [Dataset]. https://energydata.info/dataset/sub-saharan-africa-wind-speed-and-wind-power-potential-maps
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    Dataset updated
    Sep 27, 2020
    License

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

    Area covered
    Sub-Saharan Africa
    Description

    Maps with wind speed and wind power density potential for Sub-Saharan Africa. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).

  6. a

    Africa Traffic Map (Night)

    • africageoportal.com
    • rwanda.africageoportal.com
    • +2more
    Updated Dec 13, 2017
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    Africa GeoPortal (2017). Africa Traffic Map (Night) [Dataset]. https://www.africageoportal.com/maps/30588cbd4bb34fb396bd05de38257148
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    Dataset updated
    Dec 13, 2017
    Dataset authored and provided by
    Africa GeoPortal
    Area covered
    Description

    This map features near real-time traffic information for different countries in Africa, designed for a night time display. This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The color coded map leverages historical, real time and predictive traffic data. Historical traffic is based on the average of observed speeds over the past three years. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

  7. c

    Global Digital Map Market Report 2025 Edition, Market Size, Share, CAGR,...

    • cognitivemarketresearch.com
    pdf,excel,csv,ppt
    Updated Apr 21, 2025
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    Cognitive Market Research (2025). Global Digital Map Market Report 2025 Edition, Market Size, Share, CAGR, Forecast, Revenue [Dataset]. https://www.cognitivemarketresearch.com/digital-map-market-report
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    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Apr 21, 2025
    Dataset authored and provided by
    Cognitive Market Research
    License

    https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

    Time period covered
    2021 - 2033
    Area covered
    Global
    Description

    According to Cognitive Market Research, the global Digital Maps market size was USD XX million in 2023 and will expand at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.

    The global Digital Maps market will expand significantly by XX% CAGR between 2024 to 2031.
    
    
    North America held the major market of more than XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
    
    
    Europe accounted for a share of over XX% of the global market size of USD XX million.
    
    
    Asia Pacific held a market of around XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
    
    
    Latin America's market will have more than XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
    
    
    Middle East and Africa held the major market of around XX% of the global revenue with a market size of USD XX million in 2023 and will grow at a compound annual growth rate (CAGR) of XX% from 2024 to 2031.
    
    
    The Tracking and Telematics segment is set to rise GPS tracking enables fleet managers to monitor their cars around the clock, avoiding expensive problems like speeding and other careless driving behaviors like abrupt acceleration. 
    
    
    The digital maps market is driven by mobile computing devices that are increasingly used for navigation, and the increased usage of geographic data.
    
    
    The retail and real estate segment held the highest Digital Maps market revenue share in 2023.
    

    Market Dynamics of Digital Maps:

    Key drivers of the Digital Maps Market

    Mobile Computing Devices Are Increasingly Used for Navigation leading to market expansion-
    

    Since technology is changing rapidly, two categories of mobile computing devices—tablets and smartphones—are developing and becoming more diverse. One of the newest features accessible in this category is map software, which is now frequently preinstalled on smartphones. Meitrack Group launched the MD500S, a four-channel AI mobile DVR, for the first time in 2022. The MD500S is a 4-channel MDVR with excellent stability that supports DMS, GNSS tracking, video recording, and ADAS. Source- https://www.meitrack.com/ai-mobile-dvr/#:~:text=Mini%204CH%20AI%20Mobile%20DVR,surveillance%20solutions%20that%20uses%20H.

    It's no secret that people who own smartphones routinely use built-in mapping apps to find directions and other driving assistance. Furthermore, these individuals use georeferenced data from GPS and GIS apps to find nearby establishments such as cafes, movie theatres, and other sites of interest. Mobile computing devices are now commonly used to acquire accurate 3D spatial information. A personal digital assistant (PDA) is a software agent that uses information from the user's computer, location, and various web sources to accomplish tasks or offer services. Thus, mobile computing devices are increasingly used for navigation leading to market expansion.

    The usage of geographic data has increased leading to market expansion-
    

    Since it is used in so many different industries and businesses—including risk and emergency management, infrastructure management, marketing, urban planning, resource management (oil, gas, mining, and other resources), business planning, logistics, and more—geospatial information has seen a boom in recent years. Since location is one of the essential components of context, geo-information also serves as a basis for applications in the future. For example, Atos SE provides services to companies in supply chain management, data centers, infrastructure development, urban planning, risk and emergency management, navigation, and healthcare by utilizing geographic information system (GIS) platforms with location-based services (LBS).

    Furthermore, augmented reality-based technologies make use of 3D platforms and GIS data to offer virtual information about people and their environment. Businesses can offer users customized ads by using this information to better understand their needs.Thus, the usage of geographic data has increased leading to market expansion.

    Restraints of the Digital Maps Market

    Lack of knowledgeable and skilled technicia...
    
  8. Z

    Comparison of Cropland Maps Derived from Land Cover Maps in Sub-Saharan...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jul 7, 2024
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    Zvonkov, Ivan (2024). Comparison of Cropland Maps Derived from Land Cover Maps in Sub-Saharan Africa [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_8048966
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    Dataset updated
    Jul 7, 2024
    Dataset provided by
    Kerner, Hannah
    Nakalembe, Catherine
    Becker-Reshef, Inbal
    McWeeny, Ryan
    Yang, Adam
    Zvonkov, Ivan
    Tseng, Gabriel
    License

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

    Area covered
    Africa, Sub-Saharan Africa
    Description

    This data repository provides the datasets associated with analysis conducted in Kerner et al. (2024), citation below.

    The CSV file intercomparison-results.csv gives a table metrics for each of 11 land cover maps (plus a majority vote ensemble of all maps) evaluated using the reference dataset for each of 8 countries (Kenya, Rwanda, Uganda, Tanzania, Mali, Malawi, Togo, and Zambia). The reference datasets are provided as zip files. To ensure these datasets can be used for independent evaluation and comparison between maps in the future, the reference datasets should ONLY be used for final, independent evaluation of data products/model outputs; they should NOT be used for training models, tuning hyperparameters, or any other decisions during model/map development.

    The provided tif files contain the consensus maps (sum of all 11 maps) used to compute consensus statistics in Kerner et al. (2024).

    Kerner, H., Nakalembe, C., Yang, A., Zvonkov, I., McWeeny, R., Tseng, G., and Becker-Reshef, I. (2024). How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? Under review.

  9. t

    Mapping canopy cover in African dry forests from combined use of Sentinel-1...

    • service.tib.eu
    • doi.pangaea.de
    Updated Nov 30, 2024
    + more versions
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    (2024). Mapping canopy cover in African dry forests from combined use of Sentinel-1 and Sentinel-2 data: application to Tanzania for year 2018 [Dataset]. https://service.tib.eu/ldmservice/dataset/png-doi-10-1594-pangaea-940264
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    Dataset updated
    Nov 30, 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 monitoring of tropical forests has benefited from the increased availability of high-resolution earth observation data. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. The availability of time series of remote sensing images at 10-meters is changing this paradigm. In the context of REDD+ national reporting requirements, we investigate a methodology that is reproducible and adaptable in order to ensure user appropriation. The overall methodology consists of three main steps: (i) the generation of Sentinel-1 (S1) and Sentinel-2 (S2) layers, (ii) the collection of an ad-hoc training/validation dataset and (iii) the classification of the satellite data. Three different classification workflows are compared in terms of their capability to capture the canopy cover of forests in East Africa. The method is tested at scale, over Tanzania. Two big data computing platforms are combined to exploit the important volume of satellite data available over a yearly period. The study also explores the accuracy of two products derived from these mapping approaches: i) binary tree cover/no tree cover (TC/NTC) map, and ii) map of canopy cover classes. We demonstrate the potential of the combination of S1 SAR and S2 optical sensors to derive an accurate map of forest cover in East Africa at a spatial resolution of 10 meters for the year 2018. Our approach uses the high temporal resolution of S2 that allows to produce bimonthly cloud-free composites that reflect the seasonality of the vegetation. An overall accuracy (OA) of about 95% is reached for the TC/NTC map. When mapping different categories of forest and canopy cover, we obtain an OA over 85% both with a per pixel accuracy approach and when considering the neighbouring pixels in the classification training. The potential of S1 and S2 data for single trees discrimination is also assessed. The reference dataset (training and validation), the three best maps and the codes to produce the S1 and S2 composites on Google Earth Engine are shared here.

  10. i

    Afrobarometer Survey 2005-2006 - Africa

    • dev.ihsn.org
    Updated Apr 25, 2019
    + more versions
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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
    Institute for Democracy in South Africa (IDASA)
    Michigan State University (MSU)
    Ghana Centre for Democratic Development (CDD-Ghana)
    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

  11. High resolution native forest map of Eastern Arc Mountains

    • doi.pangaea.de
    zip
    Updated Apr 3, 2020
    + more versions
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    Joni Koskikala; Niina Käyhkö; Markus Kukkonen (2020). High resolution native forest map of Eastern Arc Mountains [Dataset]. http://doi.org/10.1594/PANGAEA.914420
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    zipAvailable download formats
    Dataset updated
    Apr 3, 2020
    Dataset provided by
    PANGAEA
    Authors
    Joni Koskikala; Niina Käyhkö; Markus Kukkonen
    License

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

    Area covered
    Description

    This study aims to develop more accurate method for mapping closed canopy evergreen natural forest (CCEF) of the Eastern Arc Mountains (EAM) ecoregion in Tanzania and Kenya, to update the knowledge on its spatial extent, level of fragmentation and conservation status. We tested 1023 model possibilities stemming from combination of Sentinel-1(S1) and Sentinel-2(S2) satellite imagery, spatial texture of S1 and S2, seasonality derived from Landsat-8 time series and topographic information, using random forest modelling approach. The CCEF model has moderate accuracy measured in True Skill Statistic (0.57), and it clearly outperforms other similar products from the region. Based on this model, there are about 296000 ha of Eastern Arc Forests (EAF) left, which is 16-27% less than estimated by previous products. Furthermore, acknowledging small forest fragments (1-10 ha) implies that the EAFs are more fragmented than previously considered. The generated CCEF model should be used to design updates and more informed and detailed conservation allocation plans, to balance this situation

  12. n

    Daily Global Area Coverage (GAC) Images for Africa from the NOAA AVHRR...

    • access.earthdata.nasa.gov
    • cmr.earthdata.nasa.gov
    Updated Apr 20, 2017
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    (2017). Daily Global Area Coverage (GAC) Images for Africa from the NOAA AVHRR Radiometers, 1982-1992, from SAI/JRC [Dataset]. https://access.earthdata.nasa.gov/collections/C1214155443-SCIOPS
    Explore at:
    Dataset updated
    Apr 20, 2017
    Time period covered
    Jan 1, 1982 - Dec 31, 1992
    Area covered
    Description

    General information about NOAA-AVHRR can be queried by interested users in the category 'Sensor' and 'Source'. Some basic information is given hereafter.

     The Advanced Very High Resolution Radiometer (AVHRR) onboard NOAA 6,
     8, 10 and TIROS-N measured in four spectral bands, while the NOAA 7, 9
     and 11 are measured in 5 bands. The primary objective of the AVHRR
     instrument is to provide cloud top and sea surface temperatures
     through passively measured visible, near infra-red and infra-red
     spectral radiation bands. Nevertheless these data are widely used for
     terrestrial applications, such as land cover mapping and vegetation
     monitoring.
    
     The available data provide a long term AVHRR Global Area Coverage
     (GAC) data set with particular emphasis placed on the continent of
     Africa. Normalized Difference Vegetation Index (NDVI), channel 2
     reflectance, channel 3 and 4 brightness temperatures, an approximate
     surface temperature and a cloud probability image are available on a
     daily basis from January 1982 to December 1992 for the whole African
     Continent at a resolution of 5 km.
    
     The remaining data from the entire GAC time series (July 1981 to the
     present) will be processed by the end of 1995.
    
     Channels 1 and 2 are converted to radiance using the August desert
     calibration coefficients published by Holben et al. (1990).
     Radiances in channels 1 and 2 are converted to 'top of atmosphere'
     reflectances (ToA). Brightness temperatures are calculated for
     channels 3, 4 and 5 using the inverse Planck function (Kidwell 1991).
    
     Channels 4 and 5 are corrected for non-linearity of sensor response to
     give true brightness temperatures using pre-launch correction
     coefficients (Planet 1988). The brightness temperature and
     reflectance images are scaled to 8 bit integers.
    
     The ToA reflectances and brightness temperatures are used to identify
     cloudy pixels. The channel 1 and 2 reflectances are then used to
     compute the NDVI, and channels 4 and 5 true brightness temperatures
     used to compute an approximate surface temperature using a 'split
     window' technique (Price 1984). This does not take variations in
     emissivity into account, and so is only of limited accuracy.
    
     The NDVI, channel 2 reflectance, channels 3 and 4 brightness
     temperature, surface temperature and the cloud probability channel are
     then geometrically corrected. Geometric correction involves three
     steps; navigation using the ELPs from the raw data, correlation with a
     reference image data base to provide ground control points for fine
     correction, and resampling into a daily continental scale mosaic.
    
     For Africa a Mercator map projection is used. The Africa GAC mosaic
     map center coordinates are 0#161#, 17.25#161# E, pixel size at the
     equator is 5 by 5 km, giving an image of 1800 lines by 1600 columns,
     with top left co-ordinates 37.59#161# N, 18.64#161# W, bottom right
     37.59#161# S, 53.64#161# E. As a function of the Mercator projection
     the resolution degrades by approximately 20% at the northern and
     southern limits; for example the ground area represented by each pixel
     is 30 km2 at 35#161# N or S, (4 km, x axis by 7.5 km, y axis) compared
     with 25 km2 at the equator.
    
     The processed data sets are stored as ERDAS 7.4 format files on a
     gigatek optical disk juke box. Data are currently made available
     through formal collaborative research agreements between outside
     laboratories and the Joint Research Centre. In such instances data
     costs are for marginal cost of reproduction only.
    
     These data will become available to the international research
     community through the EC and European Space Agency initiative, the
     Centre for Earth Observation (CEO).
    
     The data sets have already provided new information concerning inter
     and intra annual variations in vegetation fire dynamics for Africa and
     have been used to derive forest seasonality information through the
     JRC`s thematic projects such as TRopical Ecosystem and Environment
     observations by Satellite (TREES).
    
     See separate entry for TREES.
    
     Example data can be found on the CEO World Wide Web home page:
     "http://www.ceo.org/".
    
  13. w

    Global High Precision Real-Time Map Market Research Report: By Application...

    • wiseguyreports.com
    Updated Dec 4, 2024
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    wWiseguy Research Consultants Pvt Ltd (2024). Global High Precision Real-Time Map Market Research Report: By Application (Autonomous Vehicles, Augmented Reality, Geographic Information Systems, Delivery Drones, Urban Planning), By Technology (Satellite Mapping, LiDAR Mapping, Photogrammetry, Real-Time Data Processing, 3D Mapping), By End Use (Transportation, Logistics, Construction, Smart Cities, Tourism), By Deployment Mode (Cloud-based, On-premise, Hybrid) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2032. [Dataset]. https://www.wiseguyreports.com/reports/high-precision-real-time-map-market
    Explore at:
    Dataset updated
    Dec 4, 2024
    Dataset authored and provided by
    wWiseguy Research Consultants Pvt Ltd
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2024
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 20232.47(USD Billion)
    MARKET SIZE 20242.67(USD Billion)
    MARKET SIZE 20325.0(USD Billion)
    SEGMENTS COVEREDApplication, Technology, End Use, Deployment Mode, Regional
    COUNTRIES COVEREDNorth America, Europe, APAC, South America, MEA
    KEY MARKET DYNAMICSIncreasing demand for autonomous vehicles, Advancements in satellite technology, Growth in urban planning applications, Rising need for accurate geolocation, Expansion of drone delivery services
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDPitney Bowes, HERE Technologies, Google, Microsoft, Autodesk, NavVis, Hexagon, TomTom, Mapbox, Apple, DigitalGlobe, Oracle, Leica Geosystems, Samsung Electronics, Esri
    MARKET FORECAST PERIOD2025 - 2032
    KEY MARKET OPPORTUNITIESIncreased demand for autonomous vehicles, Expansion in smart city projects, Growth in logistics and supply chain, Advancements in drone technology, Rising adoption of AR/VR applications
    COMPOUND ANNUAL GROWTH RATE (CAGR) 8.18% (2025 - 2032)
  14. T

    South Africa - Land Area (sq. Km)

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Sep 30, 2013
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    TRADING ECONOMICS (2013). South Africa - Land Area (sq. Km) [Dataset]. https://tradingeconomics.com/south-africa/land-area-sq-km-wb-data.html
    Explore at:
    json, xml, excel, csvAvailable download formats
    Dataset updated
    Sep 30, 2013
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    South Africa
    Description

    Land area (sq. km) in South Africa was reported at 1213090 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Land area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

  15. Comoros: High Resolution Population Density Maps + Demographic Estimates -...

    • ckan.africadatahub.org
    Updated Sep 30, 2022
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    africadatahub.org (2022). Comoros: High Resolution Population Density Maps + Demographic Estimates - Dataset - ADH Data Portal [Dataset]. https://ckan.africadatahub.org/dataset/comoros-high-resolution-population-density-maps-demographic-estimates
    Explore at:
    Dataset updated
    Sep 30, 2022
    Dataset provided by
    Africa Data Hub
    CKANhttps://ckan.org/
    License

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

    Area covered
    Comoros
    Description

    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in South Africa: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click here. For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found here

  16. d

    Soil and Terrain Database for Northeastern Africa and Crop Production System...

    • search.dataone.org
    Updated Nov 17, 2014
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    Food and Agriculture Organization of the United Nations (FAO); International Soil Reference and Information Centre (ISRIC) (2014). Soil and Terrain Database for Northeastern Africa and Crop Production System Zones of the IGAD Subregion [Dataset]. https://search.dataone.org/view/Soil_and_Terrain_Database_for_Northeastern_Africa_and_Crop_Production_System_Zones_of_the_IGAD_Subregion.xml
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    Dataset updated
    Nov 17, 2014
    Dataset provided by
    Regional and Global Biogeochemical Dynamics Data (RGD)
    Authors
    Food and Agriculture Organization of the United Nations (FAO); International Soil Reference and Information Centre (ISRIC)
    Time period covered
    Jan 1, 1987 - Dec 31, 1988
    Area covered
    Description

    The Soil and Terrain Database for Northeastern Africa contains land resource information on soils, physiography, geology and vegetation for the following ten countries: Burundi, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Rwanda, Somalia, Sudan and Uganda. The information is accessible with an easy-to-use viewer program and is also stored in vector Arc/Info export format. Information on individual soil properties with class values is also given. A land suitability assessment for irrigated and upland crops for each unit is included. The scale ofthe source material is variable and ranges between 1:1 million and 1:2 million. A user manual for the viewer program and background information on the collected and correlated land resource materials are contained in filed documents.

    Soils are classified in the Revised Legend; physiographic and lithology information was collected using an earlier draft version of the SOTER manual.

    The Inter-Governmental Authority on Development (IGAD) -- Sudan, Kenya, Djibouti, Somalia, Uganda, Eritrea, Ethiopia -- Crop Production System Zones (CPSZ) software is a detailed database that provides background information about actual farming in the region. It comes with a program (CVIEW, a CPSZ viewer) that displays maps, zooms in and out, and provides export facilities for the maps in image format and for the actual data in text format. The elementary mapping unit is a compromise between administrative units and agro-ecological zones: whenever steep agro-ecological gradients exist, administrative units are subdivided, thus resulting in 1200 mapping units that are homogeneous from an agro-ecological point of view, while retaining the compatibility with the administrative units used for most socio-economic variables in agricultural planning.

    The just over 500 mappable variables are subdivided into several categories covering the spectrum from agronomy and livestock to the physical environment. For each mapping unit, detailed information is also presented on the crop calendar, typical yields and main pests and diseases.

    This CD-ROM contains a collection of land and natural resource information for Northeastern Africa, in particular for the IGAD countries bordering the Nile basin. It includes data on administrative boundaries, rivers and lakes, soil and terrain, climatology, land use, physiography, geology and natural vegetation in easily accessible format.

    Soil and Terrain Database for Northeasterm Africa (1:1 Million Scale) and Crop Production System Zones of the IGAD Subregion is provided on CD-ROM by the FAO, Land and Water Digital Media Series (Number 2). The CD-ROM can be purchased (Price: US$40) from FAO, Sales and Marketing Group, Viale delle Terme di Caracalla 0100 Rome, Italy (Fax: +39-06-5705-3360 E-mail: publications-sales@fao.org).

  17. Urban Land Cover Maps for Mekelle, Ethiopia and Polokwane, South Africa,...

    • open.nasa.gov
    • daac.ornl.gov
    • +3more
    Updated Jul 2, 2025
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    nasa.gov (2025). Urban Land Cover Maps for Mekelle, Ethiopia and Polokwane, South Africa, 2020 [Dataset]. https://open.nasa.gov/dataset/urban-land-cover-maps-for-mekelle-ethiopia-and-polokwane-south-africa-2020
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    Dataset updated
    Jul 2, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Area covered
    Ethiopia, Polokwane, Mekele, South Africa
    Description

    This dataset consists of very high resolution urban land cover maps for two African cities, Mekelle, Ethiopia and Polokwane, South Africa for 2020. Maps were generated from Planet SuperDove satellite imagery at 3.125-m spatial resolution, and Worldview-3 satellite imagery (Maxar Techologies) at two spatial resolutions, 2 m for multispectral imagery and 0.5-m spatial resolution for pansharpened imagery. An object-based image classification approach was used to produce a multi-class land cover product for each image source. The aim of this work was to support fine scale urban land cover analyses and comparative assessments between different high resolution satellite imagery sources. The data are provided in shapefile format.

  18. South Africa: High Resolution Population Density Maps + Demographic...

    • data.amerigeoss.org
    zip
    Updated Oct 23, 2024
    + more versions
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    UN Humanitarian Data Exchange (2024). South Africa: High Resolution Population Density Maps + Demographic Estimates [Dataset]. https://data.amerigeoss.org/dataset/highresolutionpopulationdensitymaps-zaf
    Explore at:
    zip(17295446), zip(43627370), zip(17234519), zip(43625180), zip(17460009), zip(17384423), zip(17245581), zip(43618197), zip(17273684), zip(43618459), zip(43614372), zip(43626293), zip(17272479), zip(43627600)Available download formats
    Dataset updated
    Oct 23, 2024
    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
    South Africa
    Description

    The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in South Africa: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

  19. Dataset according to "A Wind Turbines Dataset for South Africa: Open Street...

    • zenodo.org
    bin
    Updated May 7, 2025
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    Maximilian Kleebauer; Maximilian Kleebauer (2025). Dataset according to "A Wind Turbines Dataset for South Africa: Open Street Map Data, Deep Learning Based Geo Coordinate Correction and Capacity Analysis" [Dataset]. http://doi.org/10.5281/zenodo.15221465
    Explore at:
    binAvailable download formats
    Dataset updated
    May 7, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Maximilian Kleebauer; Maximilian Kleebauer
    License

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

    Description

    Dataset Summary
    This dataset provides the most accurate and comprehensive geospatial information on wind turbines in South Africa as of 2025. It includes precise turbine coordinates, detailed technical attributes, and spatially harmonized metadata across 42 wind farms. The dataset contains 1,487 individual turbine entries with validated information on turbine type, rated capacity, rotor diameter, commissioning year, and administrative regions. It was compiled by integrating OpenStreetMap (OSM) data, satellite imagery from Google and Bing, a RetinaNet-based deep learning model for coordinate correction, and manual verification.

    Data Structure

    • Format: GeoJSON

    • Coordinate Reference System (CRS): WGS 84 (EPSG:4326)

    • Number of features: 1,487

    • Geometry type: Point (turbine locations)

    • Key attributes:

      • id: Unique internal identifier

      • osm_id: Reference ID from OpenStreetMap

      • gid, country, type1, name1, type2, name2: Administrative region (based on GADM)

      • farm_name: Name of the wind farm

      • commissioning_year: Year the turbine was commissioned

      • number_of_turbines: Total number of turbines at the wind farm

      • total_farm_capacity: Total installed capacity of the wind farm (MW)

      • capacity_per_turbine: Rated power per turbine (MW)

      • turbine_type: Manufacturer and model of the turbine

      • geometry: Point geometry (longitude, latitude)

    Publication Abstract
    Accurate and detailed spatial data on wind energy infrastructure is essential for renewable energy planning, grid integration, and system analysis. However, publicly available datasets often suffer from limited spatial accuracy, missing attributes, and inconsistent metadata. To address these challenges, this study presents a harmonized and spatially refined dataset of wind turbines in South Africa, combining OpenStreetMap data with high-resolution satellite imagery, deep learning-based coordinate correction, and manual curation. The dataset includes 1,487 turbines across 42 wind farms, representing over 3.9 GW of installed capacity as of 2025. The Geo-Coordinates were validated and corrected using a RetinaNet-based object detection model applied to both Google and Bing satellite imagery. Instead of relying solely on spatial precision, the curation process emphasized attribute completeness and consistency. Through systematic verification and cross-referencing with multiple public sources, the final dataset achieves a high level of attribute completeness and internal consistency across all turbines, including turbine type, rated capacity, and commissioning year. The resulting dataset is the most accurate and comprehensive publicly available dataset on wind turbines in South Africa to date. It provides a robust foundation for spatial analysis, energy modeling, and policy assessment related to wind energy development.

    Citation Notification

    If you use this dataset, please cite the following publication (currently in the process of publication):

    Kleebauer, M. et. al (2025). A Wind Turbines Dataset for South Africa: OpenStreetMap Data, Deep Learning Based Geo-Coordinate Correction and Capacity Analysis.

  20. a

    CARTOGRAPHIE DES ZONES BASSES

    • geoprcm-prcm.hub.arcgis.com
    Updated Jun 19, 2023
    + more versions
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    PRCM (2023). CARTOGRAPHIE DES ZONES BASSES [Dataset]. https://geoprcm-prcm.hub.arcgis.com/maps/cartographie-des-zones-basses-2
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    Dataset updated
    Jun 19, 2023
    Dataset authored and provided by
    PRCM
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    We mapped Low Elevation Coastal Zones at or below 10m in elevation and adjacent to the coastline for West Africa, from Senegal to Nigeria. This analysis was conducted using MERIT DEM data, which was created by removing multiple error types from SRTM3 v2.1 and AW3D-30m v1 to reduce vertical height bias (Yamakzai et al. 2018). Given this increased vertical accuracy, MERIT DEM can map 10-meter LECZs with an 89% accuracy (Gesch 2018).

    To determine the 10-meter LECZ, we identified pixels that had a value less than 10 and were adjacent to the coast or a coastal water body. We also masked permanent water bodies from the zone to better visually represent the surrounding land areas most at risk.

    Limitations

    LIDAR derived Digital Elevation Models (DEMs), along with current, bathymetric and storm surge data, is widely acknowledged to be the most accurate way of modeling fine-scale SLR (Luger and Gunduz 2015, Gesch 2018, Kulp and Strauss 2015). Although this is largely recognized as the most accurate approach, LIDAR data is expensive to obtain, often unavailable in many parts of the world, and would require a large amount of processing power to analyze at the scale of the West African Coastline. Remotely sensed, globally available DEMs are also commonly used to map SLR vulnerability, although it has been shown that global DEMs are not suitable for mapping fine scale sea-level rise over relatively short time horizons with any acceptable amount of accuracy (Leon et al. 2014, Gesch 2018). Given these accuracy and data availability issues, we were not able to model seal level rise itself, but rather were able to identify 10-meter Low Elevation Coastal Zones (LECZs) for the entire west coast of Africa (Senegal to Nigeria). We also highlighted other key areas within the LECZ that are particularly vulnerable to the impacts of sea level rise for information and planning purposes.

    Map projection : It is currently Africa Albers Equal Area Conic (WGS84).

    Data links

    · MERIT DEM : https://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/

    · https://www.wabicc.org/mdocs-posts/mapping-west-africas-low-elevation-coastal-zones/

    · file:///C:/Users/ProDesk%20400/Downloads/Mapping%20West%20Africa&%23039%3Bs%20Low%20Elevation%20Coastal%20Zones.pdf

    Data source

    This data layer was developed using MERIT DEM data, which is created by removing multiple error types from SRTM3 v2.1 and AW3D-30m v1 to reduce vertical height bias. This dataset was produced by Yamakzai et al. 2018.

    Citation (s)

    Cori G., 2019. Mapping weest Africa’s low elevation costal zones. USAID, WA BiCC, Tetra Tech.

    Gesch, D., 2018. Best Practices for Elevation-Based Assessments of Sea-Level Rise and Coastal Flooding Exposure. Frontiers in Earth Science, 6.

    Gunduz, Orhan & Tulger Kara, Gülşah. (2015). ‘Influence of DEM Resolution on GIS-Based Inundation Analysis’. 9th World Congress of the European Water Resources Association (EWRA). İstanbul, Turkey.

    Kulp, S. and Strauss, B., 2015. ‘The Effect Of DEM Quality On Sea Level Rise Exposure Analysis’. AGU Fall Meeting. 2015.

    Leon, J., Heuvelink, G. and Phinn, S., 2014. Incorporating DEM Uncertainty in Coastal Inundation Mapping. PLoS ONE, 9(9), p.e108727.

    Yamazaki D., D. Ikeshima, R. Tawatari, T. Yamaguchi, F. O'Loughlin, J.C. Neal, C.C. Sampson, S. Kanae & P.D. Bates. A high accuracy map of global terrain elevations. Geophysical Research Letters, vol.44, pp.5844-5853, 2017 doi: 10.1002/2017GL072874.

    Geographic coverageSenegal to Nigeria.

    Layer creation date : 7/31/20.

    Contacts : Cori Grainger (cori.grainger@tetratech.com), Vaneska Litz (vaneska.litz@tetratech.com), Stephen Kelleher (Stephen.Kelleher@wabicc.org).

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SERVIR (2019). Africa Land Surface Forms [Dataset]. https://data.amerigeoss.org/sl/dataset/africa-land-surface-forms

Africa Land Surface Forms

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6 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jul 23, 2019
Dataset provided by
SERVIR
Area covered
Africa
Description

The land surface forms were identified using the method developed by the Missouri Resource Assessment Partnership (MoRAP). The MoRAP method is an automated land surface form classification based on Hammond's (1964a, 1964b) classification. MoRAP made modifications to Hammond's classification, which allowed finer-resolution elevation data to be used as input data and analyses to be made using 1 km2 moving window (True, 2002; True et al., 2000). While Hammond's methodology was based on three variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief (True, 2002). Slope is classified as gently sloping or not gently sloping using a threshold value of 8%. Local relief, the difference between the maximum and minimum elevation in a 1km2 neighborhood for analysis, is classified into five classes (0-15m, 16-30m, 31-90m, 91-150m, and >150m). Slope classes and relief classes were subsequently combined to produce eight land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains). In the implementation for the contiguous United States, Sayre et al. (2009) further refined the MoRAP methodology to identify a new land surface form class, "high mountains/deep canyons", by using an additional local relief class (>400 m). This method was implemented for Africa using a void-filled 90m SRTM elevation dataset which was created from the 30m SRTM elevation data provided by the National Geospatial-Intelligence Agency. In the preliminary output, which had nine land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains, and high mountains/deep canyons), artifacts were identified over flat desert areas affecting the classification between the two lowest relief classes, "flat plains" and "smooth plains." Since this problem was especially pronounced in areas where the input SRTM elevation data originally had data-voids, the problem could have been caused by anomalies or artifacts in the input data, which resulted from the void-filling processes. Instead of further investigating causes of the problem, the two land surface form classes were combined. In addition, the "low hills" class which had a very low occurrence was combined with the "hills" class. As a result, seven land surface form classes were identified in the final dataset (smooth plains, irregular plains, escarpments, hills, breaks/foothills, low mountains, and high mountains/deep canyons). References: Hammond, E.H., 1964a. Analysis of Properties in Land Form Geography - An Application to Broad-Scale Land Form Mapping. Annals of the Association of American Geographers, v. 54, no. 1, p. 11-19. Hammond, E.H. 1964b. Classes of land surface form in the forty-eight states, U.S.A. Annals of the Association of American Geographers. 54(1): map supplement no. 4, 1: 5,000,000. Sayre, R., P. Comer, H. Warner, and J. Cress. 2009. A new map of standardized terrestrial ecosystems of the conterminous United States: U. S. Geological Survey professional Paper 1768, 17 p. True, D. 2002. Landforms of the Lower Mid-West. Missouri Resource Assessment Partnership. MoRAP Map Series MS-2003-001, scale 1:1,500,000. http://www.cerc.usgs.gov/morap/Assets/maps/Landforms_of_the_Lower_Mid-West_MS-2002-01.pdf. True, D., T. Gordon, and D. Diamond. 2000. How the size of a sliding window impacts the generation of landforms. Missouri Resources Assessment Partnership. http://www.cerc.cr.usgs.gov/morap/projects/landform_model/landforms2001_files/frame.htm.

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