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.

Catholic Carbon Footprint Story Map Map:DataBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Map Development: Molly BurhansMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

The UNESCO/AETFAT/UNSO (White's) Vegetation Map of Africa was published in 1983 after more than 15 years of collaboration between UNESCO and AETFAT (Association pour l'Etude Taxonomique de la Flore De l'Afrique Tropicale). The original product comprises three map sheets at a scale of 1:5,000,000. These maps were digitized by GRID in their original Miller Oblated Stereographic Projection, and later rasterized at a cell (pixel) size of 30 seconds latitude/longitude resolution (approximately one-kilometer square). The digital product distributed by GRID covers the entire continent of Africa and Madagascar in a single data file.

The Vegetation Map of Africa is a compendium of various existing map sources for different regions/countries, which were integrated and synthesized by the AETFAT committeee responsible for creating the map (headed by Dr. F. White of Oxford University, UK). The first draft of the map was checked by extensive fieldwork and discussions with local experts. The vegetation classification used is the UNESCO standard based on physiognomy and floristic composition (not climate), and it includes a total of 80 major vegetation types and mosaics. Water is added as category 81 in the GRID legend for the digital map.

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.

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.

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

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

The dataset consists of a map of above ground forest biomass (AGB, unit: tons/ha i.e., Mg/ha) of the African continent for the year 2010 (raster dataset) with a pixel size of 25 m x 25 m. AGB is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots. Per-pixel estimates of above-ground biomass uncertainty expressed as standard error in Mg/ha (raster dataset) are also provided.

The AGB estimates were obtained from spaceborne SAR (ALOS PALSAR, Envisat ASAR), optical (Landsat-7), LiDAR (ICESAT), auxiliary datasets with multiple estimation procedures (Santoro et al., ESSD, 2021).

In this repository, the AGB data are available in form of tiles of 2° x 2° for the African continent (bounding box: longitude -26°E/+56°E latitude: -36°N/+38°N).

This dataset is the basis for the official GlobBiomass dataset consisting of global estimates of forest biomass with 1 ha pixels (https://doi.pangaea.de/10.1594/PANGAEA.894711). The dataset in this repository represents the original GlobBiomass dataset of AGB for Africa from which the official dataset was obtained after averaging from 25 m to 100 m. Given the lower accuracy of the 25 m pixel-based estimates, it is recommended to use the official GlobBiomass dataset unless detailed spatial resolution is a fundamental asset.

Technical specifications are provided in the file README_GLOBBIOMASS_Africa_20210428.pdf

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).

Snapshot img

3D Mapping And Modeling Market Size 2025-2029

The 3d mapping and modeling market size is forecast to increase by USD 35.78 billion, at a CAGR of 21.5% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing adoption in smart cities and urban planning projects. This trend is attributed to the ability of 3D mapping and modeling technologies to provide accurate and detailed visualizations of complex urban environments, enabling more efficient planning and management. Another key driver is the emergence of digital twin technology, which allows for real-time monitoring and simulation of physical assets in a digital environment. However, the market also faces challenges, most notably privacy and security concerns. With the increasing use of 3D mapping and modeling in various applications, there is a growing risk of data breaches and unauthorized access to sensitive information. As such, companies must prioritize robust security measures to protect customer data and maintain trust. Additionally, the high cost of implementing and maintaining these technologies remains a barrier to entry for some organizations. Despite these challenges, the market's potential for innovation and growth is substantial, with opportunities for companies to capitalize on emerging trends and navigate challenges effectively.

What will be the Size of the 3D Mapping And Modeling Market during the forecast period?

Request Free SampleThe market continues to evolve, driven by advancements in spatial data acquisition, project management, and navigation systems. BIM software, artificial intelligence (AI), and 3D visualization services are increasingly integrated into infrastructure management, real estate development, and cultural heritage preservation. Image recognition and 3D scanning are revolutionizing asset management and virtual reality (VR) applications. In precision agriculture, AI development and machine learning enable object detection and scene understanding, while data analysis and processing facilitate more efficient crop management. Autonomous vehicles and remote sensing rely on 3D modeling software and point cloud processing for accurate environmental monitoring. Additive manufacturing and 3D printing services are transforming industries, from construction to healthcare, with advancements in 3D modeling software, materials, and processing techniques. Urban planning benefits from AI-driven data analytics and 3D model optimization for smarter city design. Deep learning and computer vision are enhancing object tracking and data visualization services, while software development and spatial analysis are improving facility management and location-based services. The ongoing integration of these technologies is shaping a dynamic and innovative market landscape.

How is this 3D Mapping And Modeling Industry segmented?

The 3d mapping and modeling industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Product Type3D modeling3D mappingComponentSoftwareServicesGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By Product Type Insights

The 3d modeling segment is estimated to witness significant growth during the forecast period.The 3D modeling market encompasses the creation of three-dimensional digital representations of objects, environments, and surfaces, finding extensive applications in industries such as architecture, gaming, film production, product design, and medical imaging. This technology's integration has revolutionized these sectors, fostering more precise and efficient design, planning, and analysis. Key technologies driving this market include computer-aided design (CAD) software, 3D scanning and rendering, simulation and animation tools, and geospatial data. CAD is a cornerstone technology in architecture, engineering, and manufacturing, enabling professionals to create intricate and accurate designs. 3D scanning and rendering technologies convert physical objects into digital models, crucial for industries where exact replicas are required. Artificial intelligence (AI) and machine learning algorithms are increasingly integrated into 3D modeling, enhancing object detection, computer vision, and data analysis capabilities. Virtual reality (VR) and augmented reality (AR) technologies are transforming 3D modeling by providing immersive experiences for design visualization, enabling better scene understanding and spatial analysis. Precision agriculture employs 3D modeling for terrain modeling and crop monitoring, while infrastructure management uses it for asset management and urban planning. 3D modeling is also instrumental in heritage preservation, environmental monitoring, and ad

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/".

Snapshot img

GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019

This soil organic carbon dataset contains the following maps: soil organic carbon concentration (%) for the 0-10 cm, 10-20 cm, 20-30 cm and 0-30 cm soil layers, and bulk density (kg/m3) and soil organic carbon stock (kg/m2) for the 0-30 cm layer. These maps were produced with (geostatistical) regression-kriging models that combined soil data from the NAFORMA survey, the Tanzania National Soil Survey and the African Soil Profiles Database Version 1.1 with a suite of environmental GIS data layers including a land cover map, SOTER soil class map, maps of topographic attributes derived from the SRTM-DEM, maps of surface reflectance and vegetation indices derived from satellite imagery. The regression-kriging models were used to predict carbon concentrations, stocks and bulk density at the nodes of a regular grid with 250 meter cell size covering the Tanzania. Prediction uncertainty was quantified and is available with the data as the lower and upper boundary of the 90% prediction interval. Further details about the input data, modelling framework, and cross validation results are provided in a peer-reviewed, scientific journal article. The project was funded by the UN-REDD Programme Output 2.4 “National Maps inform delivery of the REDD+ Framework” and conducted through a letter of Agreement between Food and Agricultural Organization of the United Nations (FAO) and ISRIC-World Soil Information. The maps were produced by ISRIC - World Soil Information in a collaborative effort with the National Soil Survey, Ministry of Natural Resources and Tourism, Tanzania Forest Services, Sokoine University, and the AfSIS project.

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Use this country model layer when performing analysis within a single country. This layer displays a single global land cover map that is modeled by country for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

This map features a multi-resolution terrain layer for elevation displayed using the function for tinted hillshade. Terrain represents ground surface elevation and is based on a digital terrain model (DTM) where the lowest measured elevation values have been favored, and features such as structures and vegetation have been eliminated, leaving the best estimate of where the ground surface would be. Ground surface elevation is also known as bare earth elevation.Dataset SummaryThis layer is primarily intended for visualization and exploration tasks scripts. There are multiple datasets available within this layer, and depending on the scale being viewed, data from one of these datasets will be shown:GMTED2010 – Global Multi-res Terrain Elevation Data 2010, from USGS – 30, 15, and 7.5 ArcSecond (approximate resolution 1 km, 500 m, and 250 m per pixel)SRTM – From 60 Degrees N to 58 S. From USGS. – 3 ArcSecond (92.766242 m)NED – Covering Alaska. From USGS. – 2 ArcSecond (61.844162 m)NED – Covering Continental United States, Hawaii, and Mexico. From USGS. – 1 ArcSecond (30.922081 m)NED – Covering Continental United States, Hawaii, and parts of Alaska. From USGS. – 1/3 ArcSecond (10.30736 m)NED – Covering some portions of the Eastern United States. From USGS. – 1/9 ArcSecond (3.435787 m)This layer provides numeric values representing ground surface heights, based on a digital terrain model (DTM). Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights. In order to most effectively work with this service in ArcGIS Desktop, you will need to use the exact resolutions given above as the cell size geoprocessing environment setting.What can you do with this layer?This layer has server functions defined for following elevation derivatives:Slope DegreesSlope PercentageAspectHillshadePre-symbolized Elevation Tinted Hillshade (shown by default in this map)Pre-symbolized Slope DegreesPre-symbolized AspectThis layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit. This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.Important Note: This layer is available for users with an ArcGIS Organizational subscription. To access this layer, you'll need to sign in with an account that is a member of an organizational subscription. If you don't have an organizational subscription, you can create a new account and then sign up for a 30 day trial of ArcGIS Online This service is currently in beta. For more information, see the Landscape Layers group on ArcGIS Online.

This data set consists of 83 digital maps that were produced by the Food and Agriculture Organization of the United Nations (FAO) for the World Bank as part of a Global Farming Systems Study. The maps are distributed through the FAO-UN GeoNetwork Portal to Spatial Data and Information.

As part of the World Bank's review of its rural development strategy, the Bank sought the assistance of FAO in evaluating how farming systems might change and adapt over the next thirty years. Amongst other objectives, the World Bank asked FAO to provide guidance on priorities for investment in food security, poverty reduction, and economic growth, and in particular to identify promising approaches and technologies that will contribute to these goals. The results of the study are summarized in a set of seven documents, comprising six regional reports and a global overview. The global overview, which synthesizes the results of the six regional analyses as well as discussing global trends, cross-cutting issues and possible implementation modalities, presents an overview of the complete study. The global document is supplemented by two case study reports of development issues of importance to farming systems globally.

The six regions studied include:

East Asia Pacific East Europe and Central Asia Latin America and Caribbean Middle East and North Africa South Asia Sub-Saharan Africa

Map coverages for each region include the following:

Average precipitation Average temperature Elevation Irrigation intensity Land cover Length of growing period Livestock stocking density Major environmental constraints Major farming systems NOAA Satellite imagery (shaded relief imagery and ocean floor bathymetry) Permanent crop and arable land Rural population Slope Total population

The map coverages were prepared by FAO based on the following data sources:

Doll, P. and Siebert, S. 1999. A Digital Global Map of Irrigated Areas, Report No A9901, Centre for Environmental Systems Research, University of Kassel, Kassel, Germany.

Environmental Systems Research Institute (ESRI) Data and Maps 1999, Volume 1. World Worldsat Color Shaded Relief Image. Based on 1996 NOAA weather satellite images, with enhanced shaded relief imagery and ocean floor relief data (bathymetry) to provide a land and undersea topographic view. ESRI, Redlands, California, USA.

Food and Agriculture Organization of the United Nations (FAO), Land and Water Development Division (AGL) with the collaboration of the International Institute for Applied Systems Analysis (IIASA). 2000. Global Agro-Ecological Zones Study. FAO, Rome, Italy.

Gomes, R. 1999. Major Environmental Constraints for Agricultural Production Project. Based on FAOCLIM database, ARTEMIS NDVI imagery, and soil and terrain data provided by Soil Resources Management and Conservation Service. FAO-GIS. Food and Agriculture Organization of the United Nations (FAO), Environment and Natural Resources Service, Rome, Italy.

Leemans, R. and Cramer, W. 1991. The IIASA Database for Mean Monthly Values of Temperature, Precipitation and Cloudiness on a Global Terrestrial Grid. Research Report RR-91-18. November 1991. International Institute of Applied Systems Analyses, Laxenburg, pp. 61.

Oak Ridge National Laboratory, LandScan Global Population 1998 Database. Oak Ridge National Laboratory (ORNL), Oak Ridge, Tennessee, USA.

Slingenbergh, J. Livestock Distribution, Production and Diseases: Towards a Global Livestock Atlas. Food and Agriculture Organization of the United Nations (FAO), AGAH, Rome, Italy. (aka Global Livestock Production and Health Atlas (GLiPHA))

U.S. Geological Survey, EROS Data Center. 1996. GTOPO30 Digital Data Set. EDC, Sioux Falls, South Dakota, USA.

Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

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.

Le lien vers les métadonnées : https://drive.google.com/file/d/1cVJAEFIDsuXBfKXFYMTp7Ts0To7WfF5Q/view?usp=sharing

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 (WGS 1984). 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

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 Date of Creation of the layer : 7/31/20 Contacts : Cori Grainger (cori.grainger@tetratech.com), Vaneska Litz (vaneska.litz@tetratech.com), Stephen Kelleher (Stephen.Kelleher@wabicc.org)

Use this country model layer when performing analysis within a single country. This layer displays predictions within each country of relative vulnerability to modification by humans by the year 2050. ESA CCI land cover maps from the years 2010 and 2018 were used to create these predictions.

Variable mapped: Vulnerability of land cover to anthropogenic change by 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer can be used in analysis, to estimate and compare vulnerability to land cover change globally due to expansion of human activity, by 2050. This layer is useful in ecological planning, helping to prioritize areas for conservation. Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and global) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between proximate countries, use the country level. If mapping larger patterns or vastly separated countries, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasContinentCountryRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil Texture