Distribution map (raster format: geotiff) of Fagus sylvatica, computed using the NFIs - EFDAC EForest European dataset of species presence/absence. The distribution is estimated by means of statistical interpolation (constrained spatial multi-frequency analysis, C-SMFA) Available years: 2000. The maps are available in the European Forest Data Center (EFDAC). The specific goal of EFDAC is to become a focal point for policy relevant forest data and information by hosting and pointing to relevant forest information as well as providing web-based tools for accessing information located in EFDAC.

Here we provide two ArcGIS map packages with georeferenced files on the spatial distribution of seals in the wider Weddell Sea (Antarctica), which were created in the context of the development of a marine protected area in the Weddell Sea.Spatial distribution of seals based on aerial surveys: The map of the spatial distribution of crabeater seals is based on modelled seal abundances from Flores et al. (2008) and Forcada et al. (2012). These modelled abundances were supplemented by abundance data derived from Bester et al. (1995, 2002) and by point data from Plötz et al. (2011a-e), which were translated into abundance values by the count method for line transect data. The calculated data on seal abundances from Plötz et al. (2011a-e) and Bester et al. (1995, 2002) were interpolated using the inverse distance weighted method. The combined data set of modelled and interpolated abundances showed highest absolute seal abundances offshore the Riiser-Larsen Ice Shelf and Quarisen Ice Shelf.Spatial distribution of seals based on tracking data: The map of probability of seal occurrence is based on all tracking data publicly available for the wider Weddell Sea from the MEOP data portal "Marine Mammals Exploring the Oceans Pole to Pole" (data request: 14-11-2016). In addition, we have used MEOP data (UK data: ct27, ct70; German data: ct113, wd06, wd07) for which unconditional sharing is not yet accepted. These data were provided by Lars Boehme (University of St. Andrews) and Horst Bornemann (AWI), respectively. Furthermore, the data from the MEOP data portal were complemented by tracking data sets on southern elephant seals (Tosh et al. 2009, James et al. 2012), Weddell seals (McIntyre et al. 2013) and crabeater seals (Nachtsheim et al. 2016). All tracking data united were processed with a state-space model described by Johnson et al. (2008) and were implemented in the R package crawl (Johnson 2011). The tracking data analysis indicated frequent occurrence of seals in a larger area off the Brunt and Filchner Ice Shelf (approx. 25°W-40°W), and in smaller patches along the eastern Weddell Sea ice shelfs as well as in the region around the tip of the Antarctic Peninsula.More information on the spatial analysis is given in working paper WG-EMM-16/03 and WG-SAM-17/30 submitted to the CCAMLR Working Group on Ecosystem Monitoring and Management (EMM) and the CCAMLR Working Group on Statistics, Assessments and Modelling (SAM), respectively (available at https://www.ccamlr.org/en/wg-emm-16 and https://www.ccamlr.org/en/wg-sam-17).

Node of the Institute of Statistics and Cartography of Andalusia. Regional Government of Andalusia. WFS Population Mesh Service. Integrated in the Spatial Data Infrastructure of Andalusia following the guidelines of the Statistical and Cartographic System of Andalusia. WFS map service of spatial distribution of the population of Andalusia in cells of 250m x 250m. The information represented in these maps has been georeferenced from the location of the postal address where each of the inhabitants of Andalusia resides. To facilitate the representation of the information and to preserve statistical confidentiality, a regular mesh has been drawn with cells of 250 meters on the side, where all the information that corresponds in each case has been added. Information that could not be georeferenced, has been estimated using spatial analysis techniques. On December 23, 2019, the data of members, pensioners, job seekers referred to the population of Andalusia corresponding to January 1, 2018 were updated. In addition, this year the information is incorporated relating to the time spent in the municipality of residence. The website of the Institute of Statistics and Cartography of Andalusia offers a visualization service: "Spatial distribution of the population of Andalusia" for interactive consultation https://www.juntadeandalucia.es/institutodeestadisticaycartografia/distributionpob/index.htm

This paper examines the spatial distribution pattern and influencing factors of Martial Arts Schools (MASs) based on Baidu map data and Geographic Information System (GIS) in China. Using python to obtain the latitude and longitude data of the MASs through Baidu Map API, and with the help of ArcGIS (10.7) to coordinate information presented on the map of China. By harnessing the geographic latitude and longitude data for 492 MASs across 31 Provinces in China mainland as of May 2024, this study employs a suite of analytical tools including nearest neighbor analysis, kernel density estimation, the disequilibrium index, spatial autocorrelation, and geographically weighted regression analysis within the ArcGIS environment, to graphically delineate the spatial distribution nuances of MASs. The investigation draws upon variables such as martial arts boxings, Wushu hometowns, intangible cultural heritage boxings of Wushu, population education level, Per capita disposable income, and population density to elucidate the spatial distribution idiosyncrasies of MASs. (1) The spatial analytical endeavor unveiled a Moran’s I value of 0.172, accompanied by a Z-score of 1.75 and a P-value of 0.079, signifying an uneven and clustered distribution pattern predominantly concentrated in provinces such as Shandong, Henan, Hebei, Hunan, and Sichuan. (2) The delineation of MASs exhibited a prominent high-density core centered around Shandong, flanked by secondary high-density clusters with Hunan and Sichuan at their heart. (3) Amongst the array of variables dissected to explain the spatial distribution traits, the explicative potency of ‘martial arts boxings’, ‘Wushu hometowns’, ‘intangible cultural heritage boxings of Wushu’, ‘population education level’, ‘Per capita disposable income’, and ‘population density’ exhibited a descending trajectory, whilst ‘educational level of the populace’ inversely correlated with the geographical dispersion of MASs. (4) The entrenched regional cultural ethos significantly impacts the spatial layout of martial arts institutions, endowing them with distinct regional characteristics.

Suitability maps (raster format: geotiff) of Acer pseudoplatnus, computed using the ForestFocus European dataset of species presence/absence. The adopted suitability model estimates the optimal environmental conditions for European tree species under present and future climates.

Available years: 2000, 2020, 2050, 2080.

For year 2000 the observed (WorldClim) climate conditions have been used.

For years 2020, 2050, 2080 the climate conditions simulated for the climate change scenarios A2 and B2 have been used (by means of the climate models CCCMA, CSIRO, HANDCM3 and of an ensemble model of them).

The maps are available in the European Forest Data Center (EFDAC). The specific goal of EFDAC is to become a focal point for policy relevant forest data and information by hosting and pointing to relevant forest information as well as providing web-based tools for accessing information located in EFDAC.

Node of the Institute of Statistics and Cartography of Andalusia. Regional Government of Andalusia. WFS Service of the Mesh of Pensioners. Integrated in the Spatial Data Infrastructure of Andalusia following the guidelines of the Statistical and Cartographic System of Andalusia. WFS map service of spatial distribution of the population of Andalusia in cells of 250m x 250m. The information represented in these maps has been georeferenced from the location of the postal address where each of the inhabitants of Andalusia resides. To facilitate the representation of the information and to preserve statistical confidentiality, a regular mesh has been drawn with cells of 250 meters on the side, where all the information that corresponds in each case has been added. Information that could not be georeferenced has been estimated using spatial analysis techniques. On December 23, 2019, the statistical information on contributory pension data, corresponding to January 1, 2018, is presented, according to the type of Social Security pensioner and their income. The website of the Institute of Statistics and Cartography of Andalusia offers a visualization service: "Spatial distribution of the population of Andalusia" for interactive consultation https://www.juntadeandalucia.es/institutodeestadisticaycartografia/distributionpob/index.htm

This dataset contains data to reproduce the results and figures published in Clawson et al. 2022, Mapping the spatial distribution of global mariculture production. article doi: https://doi.org/10.1016/j.aquaculture.2022.738066 Publication abstract: Mariculture (marine and brackish water aquaculture) has grown rapidly over the past 20 years, yet publicly available information on the location of mariculture production is sparse. Identifying where mariculture production occurs remains a major challenge for understanding its environmental impacts and the sustainability of individual farms and the sector as a whole. We compiled known mariculture locations and applied a simple production-allocation approach to map remaining global mariculture locations across 73 countries using the key determinants of distance to shore and ports, and average productivity (tonnage) of known farms. Our map represents 96% of reported fish and invertebrate mariculture production for 2017, but excludes algae which constitutes half of global mariculture production. We provide, for the first time, a publicly available spatial database of known and estimated mariculture locations. We discuss the utility and limitations of the existing data and our modeling approach, and highlight the key data gaps and future challenges for mapping aquaculture. Our results provide a vital resource for mariculture and environmental researchers, but we emphasize the need for a standardized, ground-truthed global spatial database of aquaculture locations and farm-level attributes (e.g., species, production type) to better understand the distribution of production and adequately plan for future growth.

Suitability maps (raster format: geotiff) of Betula pubescens, computed using the ForestFocus European dataset of species presence/absence. The adopted suitability model estimates the optimal environmental conditions for European tree species under present and future climates.

Available years: 2000, 2020, 2050, 2080.

For year 2000 the observed (WorldClim) climate conditions have been used.

For years 2020, 2050, 2080 the climate conditions simulated for the climate change scenarios A2 and B2 have been used (by means of the climate models CCCMA, CSIRO, HANDCM3 and of an ensemble model of them).

The maps are available in the European Forest Data Center (EFDAC). The specific goal of EFDAC is to become a focal point for policy relevant forest data and information by hosting and pointing to relevant forest information as well as providing web-based tools for accessing information located in EFDAC.

The map shows the spatial distribution of population density in 2020 from WorldPop. The data is based on country totals adjusted to match the corresponding UNPD estimates. Tabular population per county estimates (2019) are from the Kenya National Bureau of Statistics.

This map shows the distribution of the iceberg data extracted from ERS SAR images.

Icebergs are identified in Synthetic Aperture Radar [SAR] images by image analysis using the texture and intensity of the microwave backscatter observations. The images are segmented using an edge detecting algorithm, and segments identified as iceberg or background, which may be sea ice, open water, or a mixture of both. Dimensions of the icebergs are derived by spatial analysis of the corresponding image segments. Location of the iceberg is derived from its position within the image and the navigation data that gives the location and orientation of the image.

More than 20,000 individual observations have been extracted from SAR images acquired by the European Space Agency's ERS-1 and 2 satellites and the Canadian Space Agency's Radarsat satellite. Because images can overlap, some proportion of the observations represent multiple observations of the same set of icebergs.

Most observations relate to the sector between longitudes 70E and 135E. The data set includes observations from several other discrete areas around the Antarctic coast. In general observations are within 200 km of the coast but in limited areas extend to about 500 km from the coast.

This metadata record has been derived from work performed under the auspices of ASAC project 2187 (ASAC_2187).

The map in the pdf file shows the extent of the coverage of individual SAR scenes used in the analysis and the abundance and size characteristics (by a limited colour palette) of the identified icebergs.

Node of the Institute of Statistics and Cartography of Andalusia. Regional Government of Andalusia. WMS Population Mesh Service. Integrated in the Spatial Data Infrastructure of Andalusia following the guidelines of the Statistical and Cartographic System of Andalusia. WMS map service of spatial distribution of the population of Andalusia in cells of 250m x 250m. The information represented in these maps has been georeferenced from the location of the postal address where each of the inhabitants of Andalusia resides. To facilitate the representation of the information and to preserve statistical confidentiality, a regular mesh has been drawn with cells of 250 meters on the side, where all the information that corresponds in each case has been added. Information that could not be georeferenced has been estimated using spatial analysis techniques. On December 23, 2019, the demographic statistical information of the population data, corresponding to January 1, 2018, is presented. The website of the Institute of Statistics and Cartography of Andalusia offers a visualization service: "Spatial distribution of the population of Andalusia" for interactive consultation https://www.juntadeandalucia.es/institutodeestadisticaycartografia/distributionpob/index.htm

This dataset visualises the spatial distribution of the rental value in Amsterdam between 1647 and 1652. The source of rental value comes from the Verponding registration in Amsterdam. The verponding or the ‘Verpondings-quohieren van den 8sten penning’ was a tax in the Netherlands on the 8th penny of the rental value of immovable property that had to be paid annually. In Amsterdam, the citywide verponding registration started in 1647 and continued into the early 19th century. With the introduction of the cadastre system in 1810, the verponding came to an end.

The original tax registration is kept in the Amsterdam City Archives (Archief nr. 5044) and the four registration books transcribed in this dataset are Archief 5044, inventory 255, 273, 281, 284. The verponding was collected by districts (wijken). The tax collectors documented their collecting route by writing down the street or street-section names as they proceed. For each property, the collector wrote down the names of the owner and, if applicable, the renter (after ‘per’), and the estimated rental value of the property (in guilders). Next to the rental value was the tax charged (in guilders and stuivers). Below the owner/renter names and rental value were the records of tax payments by year.

This dataset digitises four registration books of the verponding between 1647 and 1652 in two ways. First, it transcribes the rental value of all real estate properties listed in the registrations. The names of the owners/renters are transcribed only selectively, focusing on the properties that exceeded an annual rental value of 300 guilders. These transcriptions can be found in Verponding1647-1652.csv. For a detailed introduction to the data, see Verponding1647-1652_data_introduction.txt.

Second, it geo-references the registrations based on the street names and the reconstruction of tax collectors’ travel routes in the verponding. The tax records are then plotted on the historical map of Amsterdam using the first cadaster of 1832 as a reference. Since the geo-reference is based on the street or street sections, the location of each record/house may not be the exact location but rather a close proximation of the possible locations based on the street names and the sequence of the records on the same street or street section. Therefore, this geo-referenced verponding can be used to visualise the rental value distribution in Amsterdam between 1647 and 1652. The preview below shows an extrapolation of rental values in Amsterdam. And for the geo-referenced GIS files, see Verponding_wijken.shp.

GIS specifications:

Coordination Reference System (CRS): Amersfoort/RD New (ESPG:28992)

Historical map tiles URL (From Amsterdam Time Machine)

NB: This verponding dataset is a provisional version. The georeferenced points and the name transcriptions might contain errors and need to be treated with caution.

Contributors

Historical and archival research: Weixuan Li, Bart Reuvekamp

Plotting of geo-referenced points: Bart Reuvekamp

Spatial analysis: Weixuan Li

Mapping software: QGIS

Acknowledgements: Virtual Interiors project, Daan de Groot

Overview map of the spatial distribution of dissolved iron concentrations in the groundwater of Lower Saxony. The map shows the area-based evaluation of 1180 groundwater analyses from depths from 50 m below the terrain surface. The colour-graded overview map represents only the spatial distribution of the measured iron concentrations and does not take into account any influences on the geological structures and properties of the substrate. The interpolation method of inverse distance weighting was used to create the map.

In the Mediterranean region, land systems have been shaped gradually through centuries. They provide services to a large and growing population in a region that is among the most vulnerable to future global change. The spatial extent and distribution of Mediterranean land systems was, until now, unknown. We present a new, expert-based classification of Mediterranean land systems, representing landscapes as integrated social-ecological systems. We combined data on land cover, management intensity and livestock available on the European and global scale in a geographic information system based approach. We put special emphasis on agro-silvo-pastoral mosaic systems: multifunctional Mediterranean landscapes hosting different human activities that are not represented in common land cover maps.The resulting land systems typology can be used to prioritize and protect landscapes of high cultural and environmental significance. The map 'medi_LS' presents the spatial distribution of Mediterranean land system, and can be imported in a GIS. The map is created in a Lambert Azimuthal Equal Area projection with a resolution of 2 x 2 km (custom projection with more details in the file "readme_projection").

The data from the Digital Mountain Map of China depicts the spatial pattern and complex morphological characteristics of mountains in China from a macro scale, including the mountains’ spatial distribution, classification, morphological elements and area ratio. It is a set of basic data that can be used for mountain zoning, mountain genetic classification and resource environment correlation analysis. Mountains carry great natural resource supply, provide ecological service and regulation functions, and play an important part in eco-civilization construction and socioeconomic development in China. Lately，Prof. Li Ainong of the Institute of Mountain Hazards and Environment, CAS, developed this data set based on the spatial definition of mountains, an a topography adaptive slide window method for the relief amplitude. The data include: (1) Spatial distribution of mountains in China; (2) Mountain classification; (3) Main mountain ranges (with range alignment, relief grade and ridge morphology); (4)Main mountain peaks; (5)Mountain proportion table of the provinces/autonomous regions/municipalities of China; (6) Contour zoning data; (7) General situation of mountain formation; (8)Mountain division and zoning data; (9) List of main mountain peaks. The spatial resolution of the original DEM source is about 90m. And the boundaries of mountains have been revised with multisource remote sensing data, which has good spatial consistency with the relief shading map. The cartographic generalization accuracy of mountain ranges and relevant features is 1:1 000 000. Mountain features in this data set have higher spatial resolution and pertinence, which are available for the zonality of mountain environment and mountain hazards, and the spatial analysis for ecological, production and living spaces in mountain areas, surpporting macro decision-making on mountain areas' development in China. p

The Terrestrial 30x30 Conserved Areas map layer was developed by the CA Nature working group, providing a statewide perspective on areas managed for the protection or enhancement of biodiversity. Understanding the spatial distribution and extent of these durably protected and managed areas is a vital aspect of tracking and achieving the “30x30” goal of conserving 30% of California's lands and waters by 2030.Terrestrial and Freshwater Data• The California Protected Areas Database (CPAD), developed and managed by GreenInfo Network, is the most comprehensive collection of data on open space in California. CPAD data consists of Holdings, a single parcel or small group of parcels which comprise the spatial features of CPAD, generally corresponding to ownership boundaries. • The California Conservation Easement Database (CCED), managed by GreenInfo Network, aggregates data on lands with easements. Conservation Easements are legally recorded interests in land in which a landholder sells or relinquishes certain development rights to their land in perpetuity. Easements are often used to ensure that lands remain as open space, either as working farm or ranch lands, or areas for biodiversity protection. Easement restrictions typically remain with the land through changes in ownership. •The Protected Areas Database of the United States (PAD-US), hosted by the United States Geological Survey (USGS), is developed in coordination with multiple federal, state, and non-governmental organization (NGO) partners. PAD-US, through the Gap Analysis Project (GAP), uses a numerical coding system in which GAP codes 1 and 2 correspond to management strategies with explicit emphasis on protection and enhancement of biodiversity. PAD-US is not specifically aligned to parcel boundaries and as such, boundaries represented within it may not align with other data sources. • Numerous datasets representing designated boundaries for entities such as National Parks and Monuments, Wild and Scenic Rivers, Wilderness Areas, and others, were downloaded from publicly available sources, typically hosted by the managing agency.Methodology1.CPAD and CCED represent the most accurate location and ownership information for parcels in California which contribute to the preservation of open space and cultural and biological resources.2. Superunits are collections of parcels (Holdings) within CPAD which share a name, manager, and access policy. Most Superunits are also managed with a generally consistent strategy for biodiversity conservation. Examples of Superunits include Yosemite National Park, Giant Sequoia National Monument, and Anza-Borrego Desert State Park. 3. Some Superunits, such as those owned and managed by the Bureau of Land Management, U.S. Forest Service, or National Park Service , are intersected by one or more designations, each of which may have a distinct management emphasis with regards to biodiversity. Examples of such designations are Wilderness Areas, Wild and Scenic Rivers, or National Monuments.4. CPAD Superunits and CCED easements were intersected with all designation boundary files to create the operative spatial units for conservation analysis, henceforth 'Conservation Units,' which make up the Terrestrial 30x30 Conserved Areas map layer. Each easement was functionally considered to be a Superunit. 5. Each Conservation Unit was intersected with the PAD-US dataset in order to determine the management emphasis with respect to biodiversity, i.e., the GAP code. Because PAD-US is national in scope and not specifically parcel aligned with California assessors' surveys, a direct spatial extraction of GAP codes from PAD-US would leave tens of thousands of GAP code data slivers within the 30x30 Conserved Areas map. Consequently, a generalizing approach was adopted, such that any Conservation Unit with greater than 80% areal overlap with a single GAP code was uniformly assigned that code. Additionally, the total area of GAP codes 1 and 2 were summed for the remaining uncoded Conservation Units. If this sum was greater than 80% of the unit area, the Conservation Unit was coded as GAP 2. 6.Subsequent to this stage of analysis, certain Conservation Units remained uncoded, either due to the lack of a single GAP code (or combined GAP codes 1&2) overlapping 80% of the area, or because the area was not sufficiently represented in the PAD-US dataset. 7.These uncoded Conservation Units were then broken down into their constituent, finer resolution Holdings, which were then analyzed according to the above workflow. 8. Areas remaining uncoded following the two-step process of coding at the Superunit and then Holding levels were assigned a GAP code of 4. This is consistent with the definition of GAP Code 4: areas unknown to have a biodiversity management focus. 9. Greater than 90% of all areas in the Terrestrial 30x30 Conserved Areas map layer were GAP coded at the level of CPAD Superunits intersected by designation boundaries, the coarsest land units of analysis. By adopting these coarser analytical units, the Terrestrial 30X30 Conserved Areas map layer avoids hundreds of thousands of spatial slivers that result from intersecting designations with smaller, more numerous parcel records. In most cases, individual parcels reflect the management scenario and GAP status of the umbrella Superunit and other spatially coincident designations.10. PAD-US is a principal data source for understanding the spatial distribution of GAP coded lands, but it is national in scope, and may not always be the most current source of data with respect to California holdings. GreenInfo Network, which develops and maintains the CPAD and CCED datasets, has taken a lead role in establishing communication with land stewards across California in order to make GAP attribution of these lands as current and accurate as possible. The tabular attribution of these datasets is analyzed in addition to PAD-US in order to understand whether a holding may be considered conserved. Tracking Conserved Areas The total acreage of conserved areas will increase as California works towards its 30x30 goal. Some changes will be due to shifts in legal protection designations or management status of specific lands and waters. However, shifts may also result from new data representing improvements in our understanding of existing biodiversity conservation efforts. The California Nature Project is expected to generate a great deal of excitement regarding the state's trajectory towards achieving the 30x30 goal. We also expect it to spark discussion about how to shape that trajectory, and how to strategize and optimize outcomes. We encourage landowners, managers, and stakeholders to investigate how their lands are represented in the Terrestrial 30X30 Conserved Areas Map Layer. This can be accomplished by using the Conserved Areas Explorer web application, developed by the CA Nature working group. Users can zoom into the locations they understand best and share their expertise with us to improve the data representing the status of conservation efforts at these sites. The Conserved Areas Explorer presents a tremendous opportunity to strengthen our existing data infrastructure and the channels of communication between land stewards and data curators, encouraging the transfer of knowledge and improving the quality of data. CPAD, CCED, and PAD-US are built from the ground up. Data is derived from available parcel information and submissions from those who own and manage the land. So better data starts with you. Do boundary lines require updating? Is the GAP code inconsistent with a Holding’s conservation status? If land under your care can be better represented in the Terrestrial 30X30 Conserved Areas map layer, please use this link to initiate a review.The results of these reviews will inform updates to the California Protected Areas Database, California Conservation Easement Database, and PAD-US as appropriate for incorporation into future updates to CA Nature and tracking progress to 30x30.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

We present a map of arable land (both utilized and abandoned) together with a validation data set for eight countries of the former Soviet Union (fSU), namely Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine. The map has a spatial resolution of 10 arc-seconds and represents the year 2010. The map is based on the integration of a number of existing maps and a training data set collected using visual interpretation of very high resolution (VHR) imagery. The map can be used for carbon modelling, assessment of land use, land use change and evaluation of agriculture potential. An additional validation data set was collected through visual interpretation of VHR imagery by trained experts, and can be used for validation of other maps from this region. The overall accuracies of the map by region and country, i.e. Caucasus, Belarus, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine, are 90±2%, 84±2%, 92±1%, 78±3%, 95±1%, 83±2%, respectively.

Suitability maps (raster format: geotiff) of Quercus petraea, computed using the ForestFocus European dataset of species presence/absence. The adopted suitability model estimates the optimal environmental conditions for European tree species under present and future climates.

Available years: 2000, 2020, 2050, 2080.

For year 2000 the observed (WorldClim) climate conditions have been used.

For years 2020, 2050, 2080 the climate conditions simulated for the climate change scenarios A2 and B2 have been used (by means of the climate models CCCMA, CSIRO, HANDCM3 and of an ensemble model of them).

The maps are available in the European Forest Data Center (EFDAC). The specific goal of EFDAC is to become a focal point for policy relevant forest data and information by hosting and pointing to relevant forest information as well as providing web-based tools for accessing information located in EFDAC.

Ecological carrying capacity refers to the maximum population scale with a certain level of social and economic development that can be sustainably carried by the ecosystem without damaging the production capacity and functional integrity of the ecosystem, per person/square kilometer. Spatial distribution data of ecological carrying capacity were calculated based on NPP data simulated by VPM model and FAO production and trade data of agriculture, forestry and animal husbandry. Based on NPP data and combined with the land use data of cci-ci and biomass ratio parameters of various ecosystems, ANPP data was obtained to serve as ecological supply quantity. Based on agricultural, forestry and animal husbandry production and trade data and combined with population data, per capita ecological consumption standards of countries along the One Belt And One Road line were obtained, and then national scale data space was rasterized. The spatial rasterized ecological bearing data are obtained by dividing the ecological supply data with the per capita ecological consumption standard.