America's private forests provide a vast array of public goods and services, including abundant, clean surface water. Forest loss and development can affect water quality and quantity when forests are removed and impervious surfaces, such as paved roads, spread across the landscape. We rank watersheds across the conterminous United States according to the contributions of private forest land to surface drinking water and by threats to surface water from increased housing density. Private forest land contributions to drinking water are greatest in the East but are also important in Western watersheds. Development pressures on these contributions are concentrated in the Eastern United States but are also found in the North-Central region, parts of the West and Southwest, and the Pacific Northwest; nationwide, more than 55 million acres of rural private forest land are projected to experience a substantial increase in housing density from 2000 to 2030. Planners, communities, and private landowners can use a range of strategies to maintain freshwater ecosystems, including designing housing and roads to minimize impacts on water quality, managing home sites to protect water resources, and using payment schemes and management partnerships to invest in forest stewardship on public and private lands.This data is based on the digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the continental United States. To focus this analysis on watersheds with private forests, only watersheds with at least 10% forested land and more than 50 acres of private forest were analyzed. All other watersheds were labeled ?Insufficient private forest for this analysis"and coded -99999 in the data table. This dataset updates forest and development statistics reported in the the 2011 Forests to Faucet analysis using 2006 National Land Cover Database for the Conterminous United States, Grid Values=41,42,43,95. and Theobald, Dr. David M. 10 March 2008. bhc2000 and bhc2030 (Housing density for the coterminous US in 2000 and 2030, respectively.) Field Descriptions:HUC_12: Twelve Digit Hydrologic Unit Code: This field provides a unique 12-digit code for each subwatershed.HU_12_DS: Sixth Level Downstream Hydrologic Unit Code: This field was populated with the 12-digit code of the 6th level hydrologic unit that is receiving the majority of the flow from the subwatershed.IMP1: Index of surface drinking water importance (Appendix Map). This field is from the 2011 Forests to Faucet analysis and has not been updated for this analysis.HDCHG_AC: Acres of housing density change on private forest in the subwatershed. HDCHG_PER: Percent of the watershed to experience housing density change on private forest. IMP_HD_PFOR: Index Private Forest importance to Surface Drinking Water with Development Pressure - identifies private forested areas important for surface drinking water that are likely to be affected by future increases in housing density, Ptle_IMP_HD: Private Forest importance to Surface Drinking Water with Development Pressure (Figure 7), percentile. Ptle_HDCHG: Percentage of each subwatershed to Experience an increase in House Density in Private Forest (Figure 6), percentile. FOR_AC: Acres forest (2006) in the subwatershed. PFOR_AC: Acres private forest (2006) in the subwatershed. PFOR_PER: Percent of the subwatershed that is private forest. HU12_AC: Acreage of the subwatershedFOR_PER: Percent of the subwatershed that is forest. PFOR_IMP: Index of Private Forest Importance to Surface Drinking Water. .Ptle_PFIMP: Private forest importance to surface drinking water(Figure 4), percentile. TOP100: Top 100 subwatersheds. 50 from the East, 50 from the west (using the Mississippi River as the divide.) Metadata

This article examines the significance of the world map in video games for the interpretation of spatial situations. An example is the popular role-playing game The Witcher 3: Wild Hunt. Nowadays, most video games are characterized by the presence of a spatial aspect. The game world map is the most important navigational element of the game that the gamer can use. To this end, the authors decided to test the importance of the game world map in the context of analyzing different examples of spatial situations that appear in The Witcher 3: Wild Hunt by the respondents. Eye movement tracking was chosen as the research method. The analysis was conducted using statistical tests. Both gamers and non-gamers of The Witcher 3: Wild Hunt, gamers and non-gamers in general, and people who identified themselves as women or men participated in the survey. Each subject was shown 5 movies (1 introductory movie, 4 movies in the main part of the study) from the gameplay of the game, in which the game world map was opened. After each video, a question was asked about both the gameplay and the game world map. It was found that familiarity with The Witcher 3: Wild Hunt, frequency of playing video games and gender influenced the correctness and time of answering the questions asked. In addition, it was found that the game world map and gameplay segments do not cognitively burden the users. Differences in visual strategy were observed between the groups of test subjects. The authors emphasized the importance of conducting further research on video games in relation to the analysis of spatial situations.

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.

Digital Map Service Market Outlook

The global digital map service market size is projected to grow significantly, from approximately $18.9 billion in 2023 to an estimated $53.1 billion by 2032, reflecting a compelling Compound Annual Growth Rate (CAGR) of 12.5%. This robust growth is driven by the increasing adoption of digital mapping technologies across diverse industries and the rising demand for real-time geographic and navigation data in both consumer and enterprise applications.

One of the primary growth factors for the digital map service market is the expanding use of digital maps in the automotive sector, particularly in the development of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. These technologies rely heavily on precise and up-to-date mapping data for navigation, obstacle detection, and other functionalities, making digital maps indispensable. Additionally, the proliferation of mobile devices and the integration of mapping services in applications such as ride-sharing, logistics, and local search have significantly contributed to market expansion.

Another significant driver is the increasing reliance on Geographic Information Systems (GIS) across various industries. GIS technology enables organizations to analyze spatial information, improve decision-making processes, and enhance operational efficiencies. Industries such as government, defense, agriculture, and urban planning utilize GIS for land use planning, disaster management, and resource allocation, among other applications. The continuous advancements in GIS technology and the integration of artificial intelligence (AI) and machine learning (ML) are expected to further propel market growth.

The rising demand for real-time location data is also a crucial factor fueling the growth of the digital map service market. Real-time location data is essential for applications such as fleet management, asset tracking, and public safety. Businesses leverage this data to optimize routes, monitor assets, and enhance customer service. The increasing implementation of Internet of Things (IoT) devices and the growing importance of location-based services are likely to sustain the demand for real-time mapping solutions in the coming years.

Regionally, North America leads the digital map service market, driven by the high adoption rate of advanced technologies and the presence of major players in the region. However, the Asia Pacific region is expected to witness the fastest growth, attributed to rapid urbanization, increasing smartphone penetration, and government initiatives to develop smart cities. Europe, Latin America, and the Middle East & Africa are also anticipated to experience substantial growth, fueled by the rising demand for digital mapping solutions across various sectors.

Service Type Analysis

In the digital map service market, the service type segment includes mapping and navigation, geographic information systems (GIS), real-time location data, and others. Mapping and navigation services hold a significant share in the market, primarily due to their extensive use in personal and commercial navigation systems. These services provide detailed road maps, traffic updates, and route planning, which are essential for everyday commuting and logistics operations. The continuous advancements in navigation technologies, such as integration with AI and ML for predictive analytics, are expected to enhance the accuracy and functionality of these services.

Geographic Information Systems (GIS) represent another critical segment within the digital map service market. GIS technology is widely used in various applications, including urban planning, environmental management, and disaster response. The ability to analyze and visualize spatial data in multiple layers allows organizations to make informed decisions and optimize resource allocation. The integration of GIS with other emerging technologies, such as drones and remote sensing, is further expanding its application scope and driving market growth.

Real-time location data services are gaining traction due to their importance in applications like fleet management, asset tracking, and location-based services. These services provide up-to-the-minute information on the geographical position of assets, vehicles, or individuals, enabling businesses to improve operational efficiency and customer satisfaction. The growing adoption of IoT devices and the increasing need for real-time visibility in supply chain operations are expected to bolster the demand for real-time location data services.</p&

The electronic map market is experiencing robust growth, driven by increasing adoption of location-based services (LBS), the proliferation of smartphones and connected devices, and the expanding use of GPS technology across various sectors. The market's value, estimated at $15 billion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. Key drivers include the rising demand for precise navigation systems in the automotive industry, the surge in e-commerce and delivery services relying on efficient route optimization, and the growing importance of location intelligence for urban planning and resource management. Furthermore, advancements in mapping technologies, such as 3D mapping and augmented reality (AR) integration, are further fueling market expansion. While data security and privacy concerns represent a potential restraint, the overall outlook remains positive, fueled by continuous technological advancements and increasing reliance on location data across numerous applications. The market is segmented by various factors, including map type (2D, 3D, etc.), application (navigation, GIS, etc.), and end-user (automotive, government, etc.). Leading companies like ESRI, Google, TomTom, and HERE Technologies are actively shaping the market landscape through innovation and strategic partnerships. Regional variations in market penetration exist, with North America and Europe currently holding a significant share. However, Asia-Pacific is expected to witness the fastest growth due to rapid urbanization and increasing smartphone penetration. The competitive landscape is characterized by both established players and emerging technology companies vying for market share through technological advancements, improved data accuracy, and enhanced user experience. The forecast period of 2025-2033 promises significant opportunities for growth, driven by the continuous integration of electronic maps into various aspects of daily life and the emerging importance of location data in diverse industries.

NCED is currently involved in researching the effectiveness of anaglyph maps in the classroom and are working with educators and scientists to interpret various Earth-surface processes. Based on the findings of the research, various activities and interpretive information will be developed and available for educators to use in their classrooms. Keep checking back with this website because activities and maps are always being updated. We believe that anaglyph maps are an important tool in helping students see the world and are working to further develop materials and activities to support educators in their use of the maps.

This website has various 3-D maps and supporting materials that are available for download. Maps can be printed, viewed on computer monitors, or projected on to screens for larger audiences. Keep an eye on our website for more maps, activities and new information. Let us know how you use anaglyph maps in your classroom. Email any ideas or activities you have to ncedmaps@umn.edu

Anaglyph paper maps are a cost effective offshoot of the GeoWall Project. Geowall is a high end visualization tool developed for use in the University of Minnesota's Geology and Geophysics Department. Because of its effectiveness it has been expanded to 300 institutions across the United States. GeoWall projects 3-D images and allows students to see 3-D representations but is limited because of the technology. Paper maps are a cost effective solution that allows anaglyph technology to be used in classroom and field-based applications.

Maps are best when viewed with RED/CYAN anaglyph glasses!

A note on downloading: "viewable" maps are .jpg files; "high-quality downloads" are .tif files. While it is possible to view the latter in a web-browser in most cases, the download may be slow. As an alternative, try right-clicking on the link to the high-quality download and choosing "save" from the pop-up menu that results. Save the file to your own machine, then try opening the saved copy. This may be faster than clicking directly on the link to open it in the browser.

World Map: 3-D map that highlights oceanic bathymetry and plate boundaries.

Continental United States: 3-D grayscale map of the Lower 48.

Western United States: 3-D grayscale map of the Western United States with state boundaries.

Regional Map: 3-D greyscale map stretching from Hudson Bay to the Central Great Plains. This map includes the Western Great Lakes and the Canadian Shield.

Minnesota Map: 3-D greyscale map of Minnesota with county and state boundaries.

Twin Cities: 3-D map extending beyond Minneapolis and St. Paul.

Twin Cities Confluence Map: 3-D map highlighting the confluence of the Mississippi and Minnesota Rivers. This map includes most of Minneapolis and St. Paul.

Minneapolis, MN: 3-D topographical map of South Minneapolis.

Bassets Creek, Minneapolis: 3-D topographical map of the Bassets Creek watershed.

North Minneapolis: 3-D topographical map highlighting North Minneapolis and the Mississippi River.

St. Paul, MN: 3-D topographical map of St. Paul.

Western Suburbs, Twin Cities: 3-D topographical map of St. Louis Park, Hopkins and Minnetonka area.

Minnesota River Valley Suburbs, Twin Cities: 3-D topographical map of Bloomington, Eden Prairie and Edina area.

Southern Suburbs, Twin Cities: 3-D topographical map of Burnsville, Lakeville and Prior Lake area.

Southeast Suburbs, Twin Cities: 3-D topographical map of South St. Paul, Mendota Heights, Apple Valley and Eagan area.

Northeast Suburbs, Twin Cities: 3-D topographical map of White Bear Lake, Maplewood and Roseville area.

Northwest Suburbs, Mississippi River, Twin Cities: 3-D topographical map of North Minneapolis, Brooklyn Center and Maple Grove area.

Blaine, MN: 3-D map of Blaine and the Mississippi River.

White Bear Lake, MN: 3-D topographical map of White Bear Lake and the surrounding area.

Maple Grove, MN: 3-D topographical mmap of the NW suburbs of the Twin Cities.

This dataset may be a mix of two years and is updated as the data is released for each county. For example, one county may have data from 2014 while a neighboring county may have had a more recent release of 2016 data. For specific years, please check the service that specifies the year, i.e. California Important Farmland: 2016.Established in 1982, Government Code Section 65570 mandates FMMP to biennially report on the conversion of farmland and grazing land, and to provide maps and data to local government and the public.The Farmland Mapping and Monitoring Program (FMMP) provides data to decision makers for use in planning for the present and future use of California's agricultural land resources. The data is a current inventory of agricultural resources. This data is for general planning purposes and has a minimum mapping unit of ten acres.

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 58.03 percent, this approach shows strong potential for generating crop type maps of current year in September.

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively.With overall mapping accuracy for the map reaching 69.88 percent, this approach shows strong potential for generating crop type maps of current year in September.

This statistic displays the benefits for consumers supported by Google Maps in Australia in 2015, by mode of transport. In total, Australian consumers derived *** billion Australian dollars worth of benefits from Google Maps. Most of the estimated consumer benefit that year, namely *** billion Australian dollars, was derived from the use of Google Maps for driving.

Web Map constructed using "U.S. States and Territories" shapefile layer from National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS): https://www.weather.gov/gis/USStatesMetadata Link: https://www.weather.gov/gis/StateMetadataWeb map supports the attachment viewer app here: https://noaa.maps.arcgis.com/home/item.html?id=83a6220b94c141d2993997dfc5f5bc01Web map/attachment viewer included in the 3D Nation Elevation and Requirements Benefits Study Hub Site here: https://3d-nation-elevation-requirements-and-benefits-study-noaa.hub.arcgis.com/

Established in 1982, Government Code Section 65570 mandates FMMP to biennially report on the conversion of farmland and grazing land, and to provide maps and data to local government and the public.

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. America's private forests provide a vast array of public goods and services, including abundant, clean surface water. Forest loss and development can affect water quality and quantity when forests are removed and impervious surfaces, such as paved roads, spread across the landscape. We rank watersheds across the conterminous United States according to the contributions of private forest land to surface drinking water and by threats to surface water from increased housing density. Private forest land contributions to drinking water are greatest in the East but are also important in Western watersheds. Development pressures on these contributions are concentrated in the Eastern United States but are also found in the North-Central region, parts of the West and Southwest, and the Pacific Northwest; nationwide, more than 55 million acres of rural private forest land are projected to experience a substantial increase in housing density from 2000 to 2030. Planners, communities, and private landowners can use a range of strategies to maintain freshwater ecosystems, including designing housing and roads to minimize impacts on water quality, managing home sites to protect water resources, and using payment schemes and management partnerships to invest in forest stewardship on public and private lands.This data is based on the digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the continental United States. To focus this analysis on watersheds with private forests, only watersheds with at least 10% forested land and more than 50 acres of private forest were analyzed. All other watersheds were labeled ?Insufficient private forest for this analysis"and coded -99999 in the data table. This dataset updates forest and development statistics reported in the the 2011 Forests to Faucet analysis using 2006 National Land Cover Database for the Conterminous United States, Grid Values=41,42,43,95. and Theobald, Dr. David M. 10 March 2008. bhc2000 and bhc2030 (Housing density for the coterminous US in 2000 and 2030, respectively.) Field Descriptions:HUC_12: Twelve Digit Hydrologic Unit Code: This field provides a unique 12-digit code for each subwatershed.HU_12_DS: Sixth Level Downstream Hydrologic Unit Code: This field was populated with the 12-digit code of the 6th level hydrologic unit that is receiving the majority of the flow from the subwatershed.IMP1: Index of surface drinking water importance (Appendix Map). This field is from the 2011 Forests to Faucet analysis and has not been updated for this analysis.HDCHG_AC: Acres of housing density change on private forest in the subwatershed. HDCHG_PER: Percent of the watershed to experience housing density change on private forest. IMP_HD_PFOR: Index Private Forest importance to Surface Drinking Water with Development Pressure - identifies private forested areas important for surface drinking water that are likely to be affected by future increases in housing density, Ptle_IMP_HD: Private Forest importance to Surface Drinking Water with Development Pressure (Figure 7), percentile. Ptle_HDCHG: Percentage of each subwatershed to Experience an increase in House Density in Private Forest (Figure 6), percentile. FOR_AC: Acres forest (2006) in the subwatershed. PFOR_AC: Acres private forest (2006) in the subwatershed. PFOR_PER: Percent of the subwatershed that is private forest. HU12_AC: Acreage of the subwatershedFOR_PER: Percent of the subwatershed that is forest. PFOR_IMP: Index of Private Forest Importance to Surface Drinking Water. .Ptle_PFIMP: Private forest importance to surface drinking water(Figure 4), percentile. TOP100: Top 100 subwatersheds. 50 from the East, 50 from the west (using the Mississippi River as the divide.) Metadata

The source codes that support the paper 'Automatic road network selection method considering functional semantic features of roads with graph convolutional networks' published in the International Journal of Geographical Information Science.Abstract: Road network selection plays a key role in map generalization for creating multi-scale road network maps. Existing methods usually determine road importance based on road geometric and topological features, few evaluate road importance from the perspective of road utilization based on human travel data, ignoring the functional values of roads, which leads to a mismatch between the generated results and people’s needs. This paper develops two functional semantic features (i.e., travel path selection probability and regional attractiveness) to measure the functional importance of roads and proposes an automatic road network selection method based on graph convolutional networks (GCN), which models road network selection as a binary classification. Firstly, we create a dual graph representing the source road network and extract road features including six graphical and two functional semantic features. Then, we develop an extended GCN model with connectivity loss for generating multi-scale road networks and propose a refinement strategy based on the road continuity principle to ensure road topology. Experiments demonstrate the proposed model with functional features improves the quality of selection results, particularly for large and medium scale maps. The proposed method outperforms state-of-the-art methods and provides a meaningful attempt for artificial intelligence models empowering cartography.Keywords: road network selection; graph convolutional network; functional features; map generalization; POI data

Risk maps of points of particular importance corresponding to a scenario of average probability of flooding (return period of 100 years), taking into account information related to IPPC facilities, WWTP, Cultural Heritage and elements of special importance for Civil Protection.

Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

Natural Earth was built through a collaboration of many volunteers and is supported by NACIS (North American Cartographic Information Society).

Natural Earth Vector comes in ESRI shapefile format, the de facto standard for vector geodata. Character encoding is Windows-1252.

Natural Earth Vector includes features corresponding to the following:

Cultural Vector Data Thremes:

• Countries: matched boundary lines and polygons with names attributes for countries and sovereign states. Includes dependencies (French Polynesia), map units (U.S. Pacific Island Territories) and sub-national map subunits (Corsica versus mainland Metropolitan France).
• Disputed areas and breakaway regions - From Kashmir to the Elemi Triangle, Northern Cyprus to Western Sahara.
• First order admin (provinces, departments, states, etc.): internal boundaries and polygons for all but a few tiny island nations. Includes names attributes and some statistical groupings of the same for smaller countries.
• Populated places: point symbols with name attributes. Includes capitals, major cities and towns, plus significant smaller towns in sparsely inhabited regions. We favor regional significance over population census in determining rankings.
• Urban polygons: derived from 2002-2003 MODIS satellite data.
• Parks and protected areas: US National Park Service units.
• Pacific nation groupings: boxes for keeping these far-flung islands tidy.
• Water boundary indicators: partial selection of key 200-mile nautical limits, plus some disputed, treaty, and median lines.

Physical Vector Data Themes:

• Coastline: ocean coastline, including major islands. Coastline is matched to land and water polygons.
• Land: Land polygons including major islands
• Ocean: Ocean polygon split into contiguous pieces.
• Minor Islands: additional small ocean islands ranked to two levels of relative importance.
• Reefs: major coral reefs from WDB2.
• Physical region features: polygon and point labels of major physical features.
• Rivers and Lake Centerlines: ranked by relative importance. Includes name and line width attributes. Don’t want minor lakes? Turn on their centerlines to avoid unseemly data gaps.
• Lakes: ranked by relative importance, coordinating with river ranking. Includes name attributes.
• Glaciated areas: polygons derived from DCW, except for Antarctica derived from MOA. Includes name attributes for major polar glaciers.
• Antarctic ice shelves: derived from 2003-2004 MOA. Reflects recent ice shelf collapses.
• Bathymetry: nested polygons at 0, -200, -1,000, -2,000, -3,000, -4,000, -5,000, -6,000, -7,000, -8,000, -9,000,and -10,000 meters. Created from SRTM Plus.
• Geographic lines: Polar circles, tropical circles, equator, and International Date Line.
• Graticules: 1-, 5-, 10-, 15-, 20-, and 30-degree increments. Includes WGS84 bounding box.

Strategic noise map for road traffic with more than 3 million vehicle passes per year according to RL 2002/49/EC, together with impact of additional roads. The reference year for these data is 2016. The noise map indicates how much noise the environment is exposed to. The noise load is expressed in the parameter Lden. The Lden level is a weighted annual average sound pressure level over a 24-hour period, with the evening and night levels having relatively greater weight, which corresponds to the finding that noise pollution is generally experienced as more annoying in the evening and at night. European research shows that an Lden is a relatively good predictor of the extent to which local residents may experience nuisance. These noise maps are updated every 5 years. The strategic noise maps with reference years 2006, 2011 and 2016 were calculated using an old calculation method. From the strategic noise maps with reference year 2021, a new calculation method was used. This is a new joint European calculation method that is mandatory for all Member States from the mapping round with reference year 2021. Because this calculation method differs from that used in previous mapping rounds, it is not appropriate to compare the results of reference year 2021 with previous editions (2006, 2011 and 2016). After all, it cannot be ruled out that differences in calculated exposure are purely due to the application of this new calculation method and are not a consequence of increased or decreased exposure.

This map (raster dataset, single layer) uses existing datasets to map globally “How important point x is likely to be for meeting the demand of a reliable & useable source of water on a scale of 0 to 1?” This relatively simple approach uses estimated water demand in a given basin as weight to identify pressure for flow regulation and water provisioning services. Precipitation and land cover estimates are then combined with it to give some insight into the hydrologic attributes of “location” and “timing” of flow that the ecosystems may influence. The underlying assumption here is that undisturbed ecosystems everywhere are performing the ecohydrological functions leading to freshwater services. The question is more (at the global scale): how dependent are the populations in the basin on the continued functioning of these services.

Input datasets:

1. Annual surface & groundwater (“blue”) water consumption estimates. URL: http://waterfootprint.org/en/resources/water-footprint-statistics/
2. HydroBasins watershed outline.
3. European Space Agency (ESA) global land cover 2015.
4. WorldClim annual average precipitation (Version 2.0).

Process:

Step 1: Calculate average annual water consumption estimates over HydroBasin outlines. This step spreads the demand laterally (in case of small basins) and upstream to the headwaters from (typically) downstream consumer concentration.

Step 2: Normalize the demand globally and map the normalized values on to “natural” land cover classes from the land cover dataset [forests, grasslands, etc].

Step 3: Normalize annual precipitation layer within basins on the scale 0-1 where 1 is the maximum annual precipitation in that basin. This is also mapped on the “natural” land cover. Precipitation is thus acting as ‘weight’ for importance within the basin. Example, upland headwaters will typically receive more rainfall and can be argued to be important for the flow regulation in the basin.

Step 4: Combine the layers from 2 and 3.

Caveats:

1. Identification of what constitutes a “natural” land cover is not trivial, especially from global land cover maps. Example: Forests and plantations are hard to distinguish from these products.
2. Improvement of quality of water is assumed to be implicit for functioning ecosystems.

This project consists of 11 files: 1) a zipped folder with a geodatabase containing seven raster files and two shapefiles, 2) a zipped folder containing the same layers found in the geodatabase, but as standalone files, 3) 9 .xml files containing the metadata for the spatial datasets in the zipped folders. These datasets were generated in ArcPro 3.0.3. (ESRI). Six raster files (drainaged, geology, nlcd, precipitation, slope, solitexture) present spatially distributed information, ranked according to the relative importance of each class for groundwater recharge. The scale used for these datasets is 1-9, where low scale values are assigned to datasets with low relative importance for groundwater recharge, while high scale values are assigned to datasets with high relative importance for groundwater recharge. The seventh raster file contains the groundwater recharge potential map for the Anchor River Watershed. This map was calculated using the six raster datasets mentioned previously. Here, the values assigned represent Very Low to Very High groundwater recharge potential (scale 1 - 5, 1 being Very Low and 5 being Very High). Finally, the two shapefiles represent the groundwater wells and the polygons used for model validation. This data is part of the manuscript titled: Mapping Groundwater Recharge Potential in High Latitude Landscapes using Public Data, Remote Sensing, and Analytic Hierarchy Process, published in the journal remote sensing.