In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

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.

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.

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.

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 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.

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.

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.

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/

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.

Collection of Sentinel-2 satellite scenes employed in the workshop "Introduction to Geospatial Raster and Vector Data with Python". Metadata is provided following the SpatioTemporal Asset Catalog (STAC) specification.

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).

Electronic maps (E-maps) provide people with convenience in real-world space. Although web map services can display maps on screens, a more important function is their ability to access geographical features. An E-map that is based on raster tiles is inferior to vector tiles in terms of interactive ability because vector maps provide a convenient and effective method to access and manipulate web map features. However, the critical issue regarding rendering tiled vector maps is that geographical features that are rendered in the form of map symbols via vector tiles may cause visual discontinuities, such as graphic conflicts and losses of data around the borders of tiles, which likely represent the main obstacles to exploring vector map tiles on the web. This paper proposes a tiled vector data model for geographical features in symbolized maps that considers the relationships among geographical features, symbol representations and map renderings. This model presents a method to tailor geographical features in terms of map symbols and ‘addition’ (join) operations on the following two levels: geographical features and map features. Thus, these maps can resolve the visual discontinuity problem based on the proposed model without weakening the interactivity of vector maps. The proposed model is validated by two map data sets, and the results demonstrate that the rendered (symbolized) web maps present smooth visual continuity.

This data set was processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files. The original polygon data were received from Linnet Graphics, the distributor of data for MNR. In the case of the species layer, the percentages of species composition were removed. This reduced the amount of information contained in the species layer of the gridded product, but it was necessary in order to make the gridded product easier to use. The original maps were produced from 1:15,840-scale aerial photography collected in 1988.

The global geospatial data market is poised for significant expansion, projected to reach $3,788 million and grow at a Compound Annual Growth Rate (CAGR) of 6.1% during the forecast period of 2025-2033. This robust growth is propelled by an increasing demand for location-based intelligence across diverse industries. Key drivers include the proliferation of IoT devices generating vast amounts of location data, advancements in satellite imagery and remote sensing technologies, and the growing adoption of AI and machine learning for analyzing complex geospatial datasets. The enterprise sector is emerging as a dominant application segment, leveraging geospatial data for enhanced decision-making in areas such as logistics, urban planning, real estate, and natural resource management. Furthermore, government agencies are increasingly utilizing this data for public safety, infrastructure development, and environmental monitoring. The market is characterized by a bifurcated segmentation between vector data, representing discrete geographic features, and raster data, depicting continuous phenomena like elevation or temperature. Both segments are experiencing healthy growth, driven by specialized applications and analytical needs. Emerging trends include the rise of real-time geospatial data streams, the increasing importance of high-resolution imagery, and the integration of AI-powered analytics to extract deeper insights. However, challenges such as data privacy concerns, high infrastructure costs for data acquisition and processing, and the need for skilled professionals to interpret and utilize the data effectively may pose some restraints. Despite these hurdles, the overwhelming benefits of actionable location intelligence are expected to drive sustained market expansion, with North America and Europe currently leading in adoption, followed closely by the rapidly growing Asia Pacific region. This in-depth report delves into the dynamic and rapidly evolving Geospatial Data Provider market, offering a comprehensive analysis from the historical period of 2019-2024 through to a robust forecast extending to 2033. With the Base Year and Estimated Year set at 2025, the report provides an up-to-the-minute snapshot and a forward-looking perspective on this critical industry. The market size, valued in the millions, is meticulously dissected across various segments, companies, and industry developments.

This data set was gridded by BORIS staff from a vector data set received from Canadian Soil Information System (CanSIS). The original data came in two parts that covered Saskatchewan and Manitoba. The data were gridded and merged into one data set of 84 files covering the BOReal Ecosystem-Atmosphere Study (BOREAS) region. The data were gridded into the Albers Equal-Area Conic (AEAC) projection. Because the mapping of the two provinces was done separately in the original vector data, there may be discontinuities in some of the soil layers because of different interpretations of certain soil properties.

Summary- These data products present vector files for different representations of land area from the moirai land data system. Vector files are generated at 3 main spatial levels, namely country, region, basin. In addition to this, files are generated for different intersections for the 3 main categories, intersections for country and basin boundaries (country_basin), region and basin boundaries (region_basin) and region and country boundaries (region_country). Since the land data system does not generate land area information for all cells within the above mentioned boundaries (for water bodies for example), the vectors are presented for 3 main classes for each spatial category, land cells, cells with no land and combined. With all of the above mentioned combinations, the data products contain 18 different vector files.

Methodology- In generating these vector files, we used the land outputs from moirai as inputs along with separate inputs for the boundaries for the main spatial levels (country, basin and region). Combining the spatial boundaries with land inputs we generated 3 raster outputs (land, no land and combined) for each of the main spatial levels along with all the intersections. A unique key is assigned for each unique spatial boundary. We then converted these rasters to vectors through a process of polygonization where polygons were dissolved using the key and finally added all metadata (basin names, region names, country names) to each of the vector files. We check and correct geometry errors in the polygons themselves. We also added various validation tests to the code to account for completeness and accuracy.

CONTENTS:

gcam_boundaries_moirai_3p1_0p5arcmin_wgs84 folder contains the following,

spatial_input_files contain the following,

valid_land_area.bil: raster file containing actual land area by grid cell globally.

crs: EPSG:4326 WGS84 - World Geodetic System 1984

resolution: 0.5 arc mins (Generated by the moirai LDS)

country_out.bil: raster file containing valid land cells matched to country codes.

crs: EPSG:4326 WGS84 - World Geodetic System 1984

resolution: 0.5 arc mins (Generated by the moirai LDS)

country_out_noland.bil: raster file containing non-land cells matched to country codes.

crs: EPSG:4326 WGS84 - World Geodetic System 1984

resolution: 0.5 arc mins (Generated by the moirai LDS)

glu_raster.bil: raster file containing valid land cells matched to GLU codes.

crs: EPSG:4326 WGS84 - World Geodetic System 1984

resolution: 0.5 arc mins (Generated by the moirai LDS)

glu_raster_noland.bil: raster file containing non-land cells matched to GLU codes.

crs: EPSG:4326 WGS84 - World Geodetic System 1984

resolution: 0.5 arc mins (Generated by the moirai LDS)

region_gcam_out.bil: raster file containing valid land cells matched to GCAM region codes.

crs: EPSG:4326 WGS84 - World Geodetic System 1984

resolution: 0.5 arc mins (Generated by the moirai LDS)

region_gcam_out_noland.bil: raster file containing non-land cells matched to GCAM region codes.

crs: EPSG:4326 WGS84 - World Geodetic System 1984

resolution: 0.5 arc mins (Generated by the moirai LDS)

Global235_CLM_5arcmin.bil: raster file containing basin boundaries for all cells output by moirai

crs: EPSG:4326 WGS84 - World Geodetic System 1984

resolution: 0.5 arc mins

GCAM_region_names.csv- Mapping file with details on GCAM region names (Used to fill in metadata)

iso_GCAM_regID.csv- Mapping file containing details on individual country names by iso code. (Used to fill in metadata)

basin_to_country_mapping.csv – Mapping file containing details on basin names by country and region. (Used to fill in metadata),

main_outputs contains the following,

 column names in outputs:

key: Unique identifier for feature

reg_id: Unique identifier for region (region number)

ctry_id: Unique identifier for country (country number)

glu_id: Unique identifier for basin (basin number)

reg_nm: Region name

ctry_nm: Country name

glu_nm: Basin name

See the README for more details on the data

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 sma ...

The data were created by transformation of vector cadastral component of SM 5 to raster file. In territories, where vector SM 5 has not been created yet, the cadastral and altimetry components were created by scanning of individual printing masters of planimetry and altimetry from the last issue of the State Map 1:5,000 - derived. The cadastral component does not contain parcel numbers.

The data were created by transformation of vector cadastral component of SM 5 to raster file. In territories, where vector SM 5 has not been created yet, the cadastral and altimetry components were created by scanning of individual printing masters of planimetry and altimetry from the last issue of the State Map 1:5,000 - derived. The cadastral component does not contain parcel numbers.