This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

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.

High resolution land cover data set for New York City. This is the 3ft version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

6 inch resolution raster image of New York City, classified by landcover type.

High resolution land cover data set for New York City. This is the 6 inch version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

Students learn about the importance of good data management and begin to explore QGIS and RStudio for spatial analysis purposes. Students will explore National Land Cover Database raster data and made-up vector point data on both platforms.

Cadaster data from PDOK used to illustrate the use of geopandas and shapely, geospatial python packages for manipulating vector data. The brpgewaspercelen_definitief_2020.gpkg file has been subsetted in order to make the download manageable for workshops. Other datasets are copies of those available from PDOK.

The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat. Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

Monroe County in southeastern West Virginia hosts world-class karst within carbonate units of Mississippian and Ordovician age. Lidar-derived elevation data acquired in late December of 2016 were used to create a 3-meter resolution working digital elevation model (DEM), from which surface depressions were identified using a semi-automated workflow in ArcGIS®. Depressions in the automated inventory were systematically checked by a geologist within a grid of 1.5 square kilometer tiles using aerial imagery, lidar-derived imagery, and 3D viewing of the lidar imagery. Distinguishing features such as modification by human activities or hydrological significance (stream sink, ephemerally ponded, etc.) were noted wherever relevant to a particular depression. Relative confidence in depression identification was provided and determined by whether the depression was visible in the lidar imagery, aerial imagery, or both. Statistics on the geometric morphometry of each depression were calculated including perimeter, area, depth, length of major and minor elliptical axes, and azimuth of the major axis. Center points were created for each surface depression and were used to create a point density raster. The density raster displays the number of closed depression points per square kilometer.

As one of the plain wetland systems in northern China, Baiyangdian Wetland plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of Baiyangdian Wetland ecosystem is also of great significance for the construction of the New Area and future scientific planning. Based on the 10-meter spatial resolution sentinel-2B image provided by ESA in September 2017, combined with Google Earth high resolution satellite image (resolution 0.23m), the wetland ecosystem network distribution map and river network distribution map of in Baiyangdian basin in 2017 were drawn by artificial visual interpretation and machine automatic classification, which can provide reference for the wetland connectivity (including hydrological connectivity and landscape connectivity) in Baiyangdian basin. The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (.shp); Baiyangdian basin river channel data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of Baiyangdian basin and the river 3 km buffer) (.tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif). The boundary of Baiyangdian basin in this dataset comes from the basic geographic information map of Baiyangdian basin provided by Zhou Wei and others. The DEM is the GDEM digital elevation data with 30m resolution. The original image data of wetland remote sensing classification comes from the sentinel-2B remote sensing image on September 20, 2017 provided by ESA. This data set uses the second, third, fourth and eighth bands of the 10m resolution in the image. The preprocessing operations such as radiometric calibration, mosaic and mosaic are carried out in SNAP and ArcGIS 10.2 software, and the supervised classification is carried out in ENVI software. The data used for river channel extraction is based on Google Earth high resolution satellite images. The research and development steps of this dataset include: preprocessing sentinel-2B image, establishing wetland classification system and selecting samples, drawing the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; based on Google Earth high-resolution satellite image (resolution 0.23m), this paper uses LocaSpaceViewer software to identify and extract river channels by manual visual interpretation. For the river channels with embankment, identify and draw along the embankment; for the river channels without embankment, distinguish according to the spectral difference between the river channels and the surrounding land use types and empirical knowledge, mark the uncertain areas, and conduct field investigation in the later stage, which can ensure that the identified river channels have been extracted. The identified river channels include the main river channel, each classified river channel, abandoned river channel, etc., and all rivers are continuous. It can effectively identify the channel and ensure the accuracy of extraction. According to the river network map of Baiyangdian basin obtained by manual visual interpretation, the total length of the river in Baiyangdian basin is about 2440 km, and the total area is 514 km2. Among them, there are 177 km2 river channels in mountainous area, with a length of 866 km, distributed in northeast-southwest direction, mostly at the junction of forest land and cultivated land; there are 337 km2 river channels in plain area, with a length of 1574 km. The Baiyangdian basin is divided into eight land use / cover types: river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land. The remote sensing monitoring results show that the wetland area of Baiyangdian basin accounted for 13.90% in 2017. Among all the wetland types, the area of marsh is the largest, followed by the area of flood plain, ditch accounts for about 1%, and the proportion of lake and river is less than 0.5%. Combined with the land use / cover classification map and the distribution of slope and elevation, it can be seen that nearly 60% of the area of forest land is distributed in 10 ° to 30 ° mountain area, and the rest of the land use / cover types are mainly distributed in 0 ° to 2 ° area. The elevation statistics show that nearly 80% of the lakes and large reservoirs are distributed in the height of 100 m to 300 m, the distribution of marsh is relatively uniform, mainly in the higher altitude area of 20 m to 300 m, the types of construction land, flood area and cultivated land are mainly concentrated in the area of 20 m to 100 m, and rivers and ditches are mainly concentrated in the area of 0 m to 100 m. Based on the classification results of land use / cover within the river, it can be found that the main land use type is wetland. Specifically, the types of marsh, flood area and lake are the most, while the types of ditch and river are less. With the increase of the buffer area, the proportion of non-wetland type gradually increased, while the proportion of wetland type gradually decreased. The main wetland types in 1-3km buffer zone on both sides of the river are marsh and flood zone. It is worth noting that nearly one third of the River belongs to cultivated land, that is, the river occupation is serious. In terms of area, about 1 / 3 rivers and 3 / 4 lakes are distributed in the river course. Most of the water bodies in the river course are controlled by human beings, but the marsh area in the river course only accounts for about 3% of the marsh area in the whole river course. In this study, 8 types of land features including river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land were selected. The total number of samples was 5199, of which 67% was used for supervised classification and 33% for accuracy verification of confusion matrix. The overall accuracy of support vector machine (SVM) classification results in Baiyangdian basin is 84.25%, and kappa coefficient is 0.82. River occupation will not only directly reduce the connectivity of wetlands in the basin, but also cause some environmental and economic problems such as water pollution. However, if the connectivity of wetlands is reduced, the ecological and environmental functions of wetlands will be destroyed, which will pose a great threat to the water security of the basin. Taking Baiyangdian basin as a whole, improving the connectivity of wetlands and enhancing the ecological and environmental functions of wetlands in the basin will help to improve the water ecological and environmental security of Xiong'an New Area and Baiyangdian basin.

The map of potential natural vegetation of eastern Africa (V4A) gives the distribution of potential natural vegetation in Ethiopia, Kenya, Tanzania, Uganda, Rwanda, Burundi, Malawi and Zambia.

The map is based on national and local vegetation maps constructed from botanical field surveys - mainly carried out in the two decades after 1950 - in combination with input from national botanical experts. Potential natural vegetation (PNV) is defined as “vegetation that would persist under the current conditions without human interventions”. As such, it can be considered a baseline or null model to assess the vegetation that could be present in a landscape under the current climate and edaphic conditions and used as an input to model vegetation distribution under changing climate.

Vegetation types are defined by their tree species composition, and the documentation of the maps thus includes the potential distribution for more than a thousand tree and shrub species, see the documentation (https://vegetationmap4africa.org/species.html)

The map distinguishes 48 vegetation types, divided in four main vegetation groups: 16 forest types, 15 woodland and wooded grassland types, 5 bushland and thicket types and 12 other types. The map is available in various formats. The online version (https://vegetationmap4africa.org/vegetation_map.html) and for PDF versions of the map, see the documentation (https://vegetationmap4africa.org/documentation.html). Version 2.0 of the potential natural vegetation map and the woody species selection tool was published in 2015 (https://vegetationmap4africa.org/docs/versionhistory/). The original data layers include country-specific vegetation types to maintain the maximum level of information available. This map might be most suitable when carrying out analysis at the national or sub-national level.

When using V4A in your work, cite the publication: Lillesø, J-P.B., van Breugel, P., Kindt, R., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H., Mulumba, J., Namaganda, M., Ndangalasi, H., Ruffo, C., Jamnadass, R. & Graudal, L. 2011, Potential Natural Vegetation of Eastern Africa (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia). Volume 1: The Atlas. 61 ed. Forest & Landscape, University of Copenhagen. 155 p. (Forest & Landscape Working Papers; 61 - as well as this repository using the DOI .

The development of V4A was mainly funded by the Rockefeller Foundation and supported by University of Copenhagen

If you want to use the potential natural vegetation map of eastern Africa for your analysis, you can download the spatial data layers in raster format as well as in vector format from this repository

A simplified version of the map can be found on Figshare . That version aggregates country specific vegetation types into regional types. This might be the better option when doing regional-level assessments.

The GDAL/OGR libraries are open-source, geo-spatial libraries that work with a wide range of raster and vector data sources. One of many impressive features of the GDAL/OGR libraries is the ViRTual (VRT) format. It is an XML format description of how to transform raster or vector data sources on the fly into a new dataset. The transformations include: mosaicking, re-projection, look-up table (raster), change data type (raster), and SQL SELECT command (vector). VRTs can be used by GDAL/OGR functions and utilities as if they were an original source, even allowing for chaining of functionality, for example: have a VRT mosaic hundreds of VRTs that use look-up tables to transform original GeoTiff files. We used the VRT format for the presentation of hydrologic model results, allowing for thousands of small VRT files representing all components of the monthly water balance to be transformations of a single land cover GeoTiff file.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/

Support dataset for the workshop: "Introduction to Geospatial Raster and Vector Data with Python", from the Carpentries Incubator. The focus will be the wildfires that affected Rhodes in July 2023.

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format.

The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data.

The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria.

The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

This dataset is the product of a geospatial interpolation using groundwater-level data obtained from a U.S. Geological Survey (USGS) synoptic survey of 129 groundwater wells in Fauquier County, VA from October 29 through November 2, 2018 and selected points from the National Hydrography Dataset (NHD). Methodology is detailed in USGS SIR 2022-5014 "Groundwater-level contour map of Fauquier County, VA, October - November 2018." Files include a continuous raster surface of groundwater-level altitudes at a horizontal resolution of 30 meters and vector lines of discrete groundwater-level altitude contours.

VCGI Training Data: Vector, raster, gpx, and tabular data referred to in VCGI's training manuals

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks) For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub. To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 30 meter cell size. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

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.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

This dataset contains the processing unit for Greenland from the Hydrological Derivatives for Modeling and Analysis (HDMA) database. The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.