The Human Geography Map (World Edition) web map provides a detailed vector basemap with a monochromatic style and content adjusted to support Human Geography information. Where possible, the map content has been adjusted so that it observes WCAG contrast criteria.This basemap, included in the ArcGIS Living Atlas of the World, uses 3 vector tile layers:Human Geography Label, a label reference layer including cities and communities, countries, administrative units, and at larger scales street names.Human Geography Detail, a detail reference layer including administrative boundaries, roads and highways, and larger bodies of water. This layer is designed to be used with a high degree of transparency so that the detail does not compete with your information. It is set at approximately 50% in this web map, but can be adjusted.Human Geography Base, a simple basemap consisting of land areas in a very light gray only.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Learn more about this basemap from the cartographic designer in Introducing a Human Geography Basemap.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map.

This is a subset of World Biomass Image Layer to focus on Central Asia and Caucasus Region. Use this web map to visualize and understand the Biomass for that region. Use image layer for your analysis. Plants play a central role in the carbon cycle by absorbing carbon dioxide from the atmosphere and incorporating it in the structure of the plant. Globally living plants contain 500 billion metric tons of carbon, more than 60 times the amount of carbon released to the atmosphere by humans each year. Understanding the distribution of the carbon stored in living plants, known as biomass, is key to estimating the effects of land use change on the climate.Dataset SummaryThis layer provides access to a 1-km cell-sized raster with data on the density of carbon stored in living plants in metric tons per hectare for the year 2000. It was published by the Oak Ridge National Laboratory Carbon Dioxide Information Analysis Center in 2008.The authors of these data request that they be cited as:Ruesch, Aaron, and Holly K. Gibbs. 2008. New IPCC Tier-1 Global Biomass Carbon Map For the Year 2000. Available online from the Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

The Human Geography Dark Map (World Edition) web map provides a detailed world basemap with a dark monochromatic style and content adjusted to support human geography information. Where possible, the map content has been adjusted so that it observes WCAG contrast criteria.This basemap, included in the ArcGIS Living Atlas of the World, uses 3 vector tile layers:Human Geography Dark Label, a label reference layer including cities and communities, countries, administrative units, and at larger scales street names.Human Geography Dark Detail, a detail reference layer including administrative boundaries, roads and highways, and larger bodies of water. This layer is designed to be used with a high degree of transparency so that the detail does not compete with your information. It is set at approximately 50% in this web map, but can be adjusted.Human Geography Dark Base, a simple basemap consisting of land areas in a very dark gray only.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Learn more about this basemap from the cartographic designer in A Dark Version of the Human Geography Basemap.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

Author: ANN WURST, NGS TEACHER CONSULTANTGrade/Audience: grade 6, grade 7, grade 8, high school, ap human geography, post secondary, professional developmentResource type: activitySubject topic(s): cartography, maps, regional geographyRegion: worldStandards: TEXAS TEKS (19) Social studies skills. The student applies critical-thinking skills to organize and use information acquired through established research methodologies from a variety of valid sources, including technology. The student is expected to: (A) analyze information by sequencing, categorizing, identifying cause-and-effect relationships, comparing, contrasting, finding the main idea, summarizing, making generalizations and predictions, and drawing inferences and conclusions; (B) create a product on a contemporary government issue or topic using critical methods of inquiry; (D) analyze and evaluate the validity of information, arguments, and counterarguments from primary and secondary sources for bias, propaganda, point of view, and frame of reference; Objectives: Students will keep a list of the toolkit 'helpers' in their notebook and use the elements to process/apply information in various formats such as short answers responses, tickets out the door, setting up writing samples for world geo, AP Human Geo and other courses involving the study of geographic concepts. Summary: Students can use these 'hooks' in their study of cartography/map making , can be applied in every unit where map skills are needed. Helps further critical thinking skills.

The Human Geography Map (US Edition) web map provides a detailed vector basemap with a monochromatic style and content adjusted to support Human Geography information. Where possible, the map content has been adjusted so that it observes WCAG contrast criteria.This basemap is available in the United States Vector Basemaps gallery and consists of 3 vector tile layers:Human Geography Label (US Edition), a label reference layer including cities and communities, countries, administrative units, and at larger scales street names.Human Geography Detail (US Edition), a detail reference layer including administrative boundaries, roads and highways, and larger bodies of water. This layer is designed to be used with a high degree of transparency so that the detail does not compete with your information. It is set at approximately 50% in this web map, but can be adjusted.Human Geography Base, a simple basemap consisting of land areas in a very light gray only.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

Abstract The amount of researchers and scientific papers rapidly grows, annually. The metrics to analyze the quality and quantity of these publications have consolidated in the academic world. A bibliometric mapping of scientific papers on Geographic Information Systems (GIS) published between 2007 and 2016 was carried out. The sample analyzed 2,053 papers, extracted from twenty journals of the Web of Science Core Collection platform. The following were evaluated: total number of publications, production by area of knowledge and by country, authors, periodicals and the most cited words. The results shows that 2012 and 2013 were the most productive periods, and that the annual growth rate of publication was 1.8%. The most significant academic areas were Geography, Computer Science, Physical Geography, and Environmental Sciences/Ecology. The three major publishing clusters were North America, Western Europe, and Eastern Asia. The International Journal of Geographic Information Science was considered the most important journal. The most relevant topics were cellular automata, relationship between GIS and users, integration of GIS with remote sensing, different land use classification methods, and critical reflections on technologies and GIS.

Author: Joseph Kerski, post_secondary_educator, Esri and University of DenverGrade/Audience: high school, ap human geography, post secondary, professional developmentResource type: lessonSubject topic(s): population, maps, citiesRegion: africa, asia, australia oceania, europe, north america, south america, united states, worldStandards: All APHG population tenets. Geography for Life cultural and population geography standards. Objectives: 1. Understand how population change and demographic characteristics are evident at a variety of scales in a variety of places around the world. 2. Understand the whys of where through analysis of change over space and time. 3. Develop skills using spatial data and interactive maps. 4. Understand how population data is communicated using 2D and 3D maps, visualizations, and symbology. Summary: Teaching and learning about demographics and population change in an effective, engaging manner is enriched and enlivened through the use of web mapping tools and spatial data. These tools, enabled by the advent of cloud-based geographic information systems (GIS) technology, bring problem solving, critical thinking, and spatial analysis to every classroom instructor and student (Kerski 2003; Jo, Hong, and Verma 2016).

This code creates a geographic map and a corresponding population cartogram side by side. They have the same colour coding to facilitate comparison. Users can modify this code to map their own data.

To understand the relationship between place and politics, we must measure both political attitudes and the ways in which place is represented in the minds of individuals. In this paper, we assess a new measure of mental-representation of geography, in which survey respondents draw their own local communities on maps and describe them. This mapping measure has been used in Canada, the UK, Denmark, and the U.S. so far. We use a panel study in Canada to present evidence that these maps are both valid and reliable measures of a personally relevant geographic area, laying the measurement groundwork for the growing number of studies using this technology. We hope to set efforts to measure ‘place’ for the study of context and politics on firmer footing. Our validity assessments show that individuals are thinking about people and places with which they have regular contact when asked to draw their communities. Our reliability assessments show that people can draw more or less the same map twice, even when the exercise is repeated months later. Finally, we provide evidence that the concept of community is a tangible consideration in the minds of ordinary citizens and is not simply a normative aspiration or motivation.

The Geometrically Valuable Areas Signaling Map is a map showing a total overview of larger and smaller geologically interesting areas and elements in Zeeland. These areas are interesting because of landscape shape/history, soil type, current formation processes or special geology. The Earthly Valuable Areas Signaling Map forms the basis of provincial selection on the Earthly Valuable Area Map. However, the Signalering Map also contains areas that are not included in the provincial selection of geographically valuable areas but have a clear geographical and landscape significance.

This 1m Digital Terrain Model (DTM) is derived from bare-ground Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. This dataset is better suited for derived layers such as slope angle, aspect, and contours. The DTM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DTM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DTM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. A shaded relief model was also generated. A similar layer, the Digital Surface Model (DSM), is a first-stop elevation layer. A processing report and readme file are included with this data release. The DTM is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

This viewer contains data directly from the U.S. Census Bureau. Use this map viewer to identify 2020 Census tract, block group, or block at a location. Map is centered on the City of Long Beach and shows the City boundary as recorded in the Census incorporated places layer. Data source: https://www.census.gov/data/developers/data-sets/TIGERweb-map-service.htmlAbout Census Tracts: https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_13About Census Block Groups: https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_4About Census Blocks: https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_5

This 1m Digital Surface Model (DSM) is derived from first-stop Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. The DSM was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DSM has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DSM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. cm RMSE at 1 sigma. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. A shaded relief model was also generated. A similar layer, the Digital Terrain Model (DTM), is a ground-surface elevation dataset better suited for derived layers such as slope angle, aspect, and contours. A processing report and readme file are included with this data release. The DSM is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

This 1m Digital Terrain Model (DTM) shaded relief is derived from first-stop Light Detection and Ranging (LiDAR) point cloud data from September 2005 for the Green Lakes Valley, near Boulder Colorado. The DTM shaded relief was created from LiDAR point cloud tiles subsampled to 1-meter postings, acquired by the National Center for Airborne Laser Mapping (NCALM) project. This data was collected in collaboration between the University of Colorado, Institute of Arctic and Alpine Research (INSTAAR) and NCALM, which is funded by the National Science Foundation (NSF). The DTM shaded relief has the functionality of a map layer for use in Geographic Information Systems (GIS) or remote sensing software. Total area imaged is 35 km^2. The LiDAR point cloud data was acquired with an Optech 1233 Airborne Laser Terrain Mapper (ALTM) and mounted in a twin engine Piper Chieftain (N931SA) with Inertial Measurement Unit (IMU) at a flying height of 600 m. Data from two GPS (Global Positioning System) ground stations were used for aircraft trajectory determination. The continuous DTM surface was created by mosaicing and then kriging 1 km2 LiDAR point cloud LAS-formated tiles using Golden Software's Surfer 8 Kriging algorithm. Horizontal accuracy and vertical accuracy is unknown. cm RMSE at 1 sigma. The layer is available in GEOTIF format approx. 265 MB of data. It has a UTM zone 13 projection, with a NAD83 horizonal datum and a NAVD88 vertical datum computed using NGS GEOID03 model, with FGDC-compliant metadata. This shaded relief model was also generated. A similar layer, the Digital Surface Model (DSM), is a first-stop elevation layer. A processing report and readme file are included with this data release. The DTM dataset is available through an unrestricted public license. The LiDAR DEMs will be of interest to land managers, scientists, and others for study of topography, ecosystems, and environmental change. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

Context

I created this dataset to train a network to generate realistic looking terrain maps based on simple color region codings. You can see some samples of the result here: https://www.reddit.com/r/MachineLearning/comments/7dwj1q/p_fun_project_mspaint_to_terrain_map_with_gan/

Content

This is a dataset of 1360 image pairs. The ground truth image is a random 512x512 pixel crop of terrain from a global map of the Earth. The second image is a color quantized and mode filtered version of the base image to create a very simple terrain region mapping composed of five colors. The five colors correspond to terrain types as follows: blue - water, grey - mountains, green - forest/jungle/marshland, yellow - desert/grassland/glacier, brown - hills/badlands.

Context

Global Map is a set of basic geospatial information at the scale of 1:1 million, which was developed and verified by National Geospatial Information Authorities (NGIAs) in the world so that it is considered as “authoritative data.” Global Mapping Project is a collaborative international project of developing Global Map for sustainable development, environmental protection and disaster mitigation.

The International Steering Committee for Global Mapping (ISCGM) was established to implement the Project. The Geospatial Information Authority of Japan (GSI) served as the Secretariat of ISCGM for the whole duration of the Committee from February 1996 to March 2017, and supported the Project activities.

Recognizing that the objective of Global Mapping Project was mostly achieved by the collective efforts of ISCGM and the participating NGIAs, the 23rd ISCGM meeting held in August, 2016 adopted the resolution of dissolving ISCGM and transferring the Global Map data to the Geospatial Information Section of the United Nations. Thus, Global Mapping Project came to end.

Content

This dataset contains geospatial vector and raster data across the map of Japan. Each zip file contains a portion (or all) of the data layers for the specific map version.

Filename breakdown:
'gm-jpn-ve_u_1_0.zip'
'GlobalMap - Japan - Layer _ Version _ Version_Num .zip'

Acknowledgements

This data is pulled directly from the Geospatial Information Authority of Japan website (http://www.gsi.go.jp/kankyochiri/gm_japan_e.html). To see more information on licensing, please visit the website's Terms of Use.

From Terms of Use:
Information made available on this website (hereinafter referred to as “Content”) may be freely used, copied, publicly transmitted, translated or otherwise modified on condition that the user complies with provisions 1) to 7) below. Commercial use of Content is also permitted.

Cover photo by David Edelstein on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This is replication data for "Mapping News Geography: A Computational Framework for Classifying Local Media Through Geographic Coverage Patterns". Instructions for usage and code can be found at the following GitHub repository: https://github.com/simonabisiani/geographic-local-media-classifier

NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, North America, South America, Europe, and Oceania are available. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. The GLanCE data product provides seven layers: the land cover class, the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule.Known Issues Version 1.0 of the data set does not include Quality Assurance, Leaf Type or Leaf Phenology. These layers are populated with fill values. These layers will be included in future releases of the data product. * Science Data Set (SDS) values may be missing, or of lower quality, at years when land cover change occurs. This issue is a by-product of the fact that Continuous Change Detection and Classification (CCDC) does not fit models or provide synthetic reflectance values during short periods of time between time segments. * The accuracy of mapping results varies by land cover class and geography. Specifically, distinguishing between shrubs and herbaceous cover is challenging at high latitudes and in arid and semi-arid regions. Hence, the accuracy of shrub cover, herbaceous cover, and to some degree bare cover, is lower than for other classes. * Due to the combined effects of large solar zenith angles, short growing seasons, lower availability of high-resolution imagery to support training data, the representation of land cover at land high latitudes in the GLanCE product is lower than in mid latitudes. * Shadows and large variation in local zenith angles decrease the accuracy of the GLanCE product in regions with complex topography, especially at high latitudes. * Mapping results may include artifacts from variation in data density in overlap zones between Landsat scenes relative to mapping results in non-overlap zones. * Regions with low observation density due to cloud cover, especially in the tropics, and/or poor data density (e.g. Alaska, Siberia, West Africa) have lower map quality. * Artifacts from the Landsat 7 Scan Line Corrector failure are occasionally evident in the GLanCE map product. High proportions of missing data in regions with snow and ice at high elevations result in missing data in the GLanCE SDSs.* The GlanCE data product tends to modestly overpredict developed land cover in arid regions.

The Human Geography Dark Map (US Edition) provides a detailed world basemap with a dark monochromatic style and content adjusted to support human geography information.This basemap is available in the United States Vector Basemaps gallery and consists of 3 vector tile layers:Human Geography Dark Label (US Edition), a label reference layer including cities and communities, countries, administrative units, and at larger scales street names.Human Geography Dark Detail (US Edition), a detail reference layer including administrative boundaries, roads and highways, and larger bodies of water. This layer is designed to be used with a high degree of transparency so that the detail does not compete with your information. It is set at approximately 50% in this web map, but can be adjusted.Human Geography Dark Base, a simple basemap consisting of land areas in a very dark gray only.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

This vector tile layer presents the Human Geography Label style (World Edition) and provides a detailed vector basemap for world labels designed to draw attention to your thematic content. This is similar in content and style to the popular Light Gray Canvas map. The map includes labels for highways, major roads, minor roads, water features, cities, landmarks, and administrative boundaries. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.This layer is used in the Human Geography Map web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.