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.

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.

This packaged data collection contains all of the outputs from our primary model, including the following data layers: Habitat Cores (vector polygons) Least-cost Paths (vector lines) Least-cost Corridors (raster) Least-cost Corridors (vector polygon interpretation) Modeling Extent (vector polygon) Please refer to the embedded spatial metadata and the information in our full report for details on the development of these data layers. Packaged data are available in two formats: Geodatabase (.gdb): A related set of file geodatabase rasters and feature classes, packaged in an ESRI file geodatabase. ArcGIS Pro Map Package (.mpkx): The same data included in the geodatabase, presented as fully-symbolized layers in a map. Note that you must have ArcGIS Pro version 2.0 or greater to view. See Cross-References for links to individual datasets, which can be downloaded in shapefile (.shp) or raster GeoTIFF (.tif) formats.

These data (vector and raster) were compiled for spatial modeling of salinity yield sources in the Upper Colorado River Basin (UCRB) and describe different scales of watersheds in the Upper Colorado River Basin (UCRB) for use in salinity yield modeling. Salinity yield refers to how much dissolved salts are picked up in surface waters that could be expected to be measured at the watershed outlet point annually. The vector polygons are small catchments developed originally for use in SPARROW modeling that break up the UCRB into 10,789 catchments linked together through a synthetic stream network. The catchments were used for a machine learning based salinity model and attributed with the new results in these vector GIS datasets. Although all of these feature classes include the same polygons, the attribute tables for each include differing outputs from new salinity models and a comparison with SPARROW model results from previous research. The new model presented in these datasets utilizes new predictive soil maps and a more flexible random forest function to improve on previous UCRB salinity spatial models. The raster data layers represent aspects of soils, topography, climate, and runoff characteristics that have hypothesized influences on salinity yields.

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.

The development and the generation of the datasets that are published through this data release, were based on the results and findings of the report mentioned here: Kim, M.H., 2018, Flood-inundation maps for the Wabash River at Lafayette, Indiana: U.S. Geological Survey Scientific Investigations Report 2018–5017, 10 p., https://doi.org/10.3133/sir20185017. The geospatial dataset contain final versions of the raster and vector geospatial data and its related metadata, and the model archive dataset contains all relevant files to document and re-run the surface-water (SW) hydraulic model that are discussed in the report.

The development and the generation of the dataset that is published through this data release, is based on the results and findings of the report mentioned here: Prokopec, J.G., 2018, Hydraulic modeling and flood-inundation mapping for the Huron River and Ore Lake Tributary, Livingston County, Michigan: U.S. Geological Survey Scientific Investigations Report 2018-5048, https://doi.org/10.3133/sir20185048. The geospatial dataset contains final versions of the raster and vector geospatial data and its related metadata that are discussed in the report.

The development and generation of the datasets that are published in this data release, were based on the methods and findings of the report: Kohn, M.S. and Patton, T.T., 2018, Flood-Inundation Maps for the South Platte River at Fort Morgan, Colorado, 2018: U.S. Geological Survey Scientific Investigations Report 2018-5114, 14 p., https://doi.org/10.3133/sir20185114. The geospatial datasets contain final versions of the raster and vector geospatial data and related metadata, and the model archive dataset contains all relevant files to document and re-run the surface-water hydraulic model that are discussed in the report. Digital flood-inundation maps for a 4.5-mile reach of the South Platte River at Fort Morgan, Colorado from Morgan County Road 16 to Morgan County 20.5, were created by the U.S. Geological Survey (USGS) in cooperation with the Colorado Water Conservation Board. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web sit ...

Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer’s accuracies, class-level precisions or user’s accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development.

Spatial layers are provided in GDA2020 Map Grid of Australia (MGA) 56 coordinate reference system, which include an orthomosaic RGB tiff in cloud optimised geotiff (COG) format; a paddock fence line vector in geojson format; a digital elevation model (DEM) generated from Lidar, a 1m contour vector and a digitised soil map generated from a paper map. All data have been generated under the Strategic Investment Process (SIP): Leveraging the Research Farms. Lineage: Orthomosaic cloud optimised geotiff (COG) was produced by an external provider with full rights and ownership by CSIRO. Data was captured in GDA2020 MGA 56 via a DJI Matrice 300 with a P1 camera at 45 megapixels. A digital elevation model (DEM) tiff image was generated from drone mounted Lidar source. A field spatial survey utilising AusCORS corrections via a rover survey tool was carried out to mark fence strainer post in order to create a highly accurate fence line spatial layer. The digitised soil map was created by georeferencing the paper map produced for the report, 'Schafer, B. M. A description of the soils on the CSIRO pastoral research laboratory property, Chiswick, Armidale, N.S.W. (Animal Research Laboratories technical paper; no. 8).

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset is a complete state-wide digital land use map of Queensland. The dataset is a product of the Queensland Land Use Mapping Program (QLUMP) and was produced by the Queensland Government. It presents the most current mapping of land use features for Queensland, including the land use mapping products from 1999, 2006 and 2009, in a single feature layer. This dataset was last updated July 2012. See additional information also.

Purpose

Indicates the current primary use or management objective of the land.

Dataset History

Source DataQueensland Government - Land use mapping (1999); Landsat TM and ETM imagery; Spot5 imagery; High resolution ortho photography through the Spatial Imagery Subscription Plan (SISP); Queensland Digital Cadastral Database (DCDB) (2009), Queensland Valuation and Sales Database (QVAS) (2009); Queensland Nature Refuges (2009); Queensland Estates (2009); Queensland Herbarium's Regional Ecosystem, Water Body and Wetlands datasets (2009); Statewide Landcover & Trees Study (SLATS) Queensland Dams and Waterbodies (2009) and land cover change data; scanned aerial photography (1999-2009).Additional verbal & written information on land uses & their locations was obtained from regional Queensland Government officers, Local Government Authorities, land owners & managers, private industry as well as from field observations & checking.Data captureA range of existing digital datasets containing land use information was collated from the Queensland Government spatial data inventory and prepared for use in a GIS using ArcGIS and ERDAS Imagine software.Processing steps To compile the 1999 baseline mapping, datasets containing baseline land cover (supplied by SLATS), Protected Areas, State Forest and Timber Reserves, plantations, coastal wetlands, reserves (from DCDB) and logged forests were interpreted in a spatial model to produce a preliminary land use raster image.The model incorporated a decision matrix which assigned each pixel a specific land use class according to a set of pre-determined rules.Individual catchments were clipped from the model output and enhanced with additional land use information interpreted primarily from Landsat TM and ETM imagery as well as scanned and hardcopy aerial photography (where available). The DCDB and other datasets containing land use information were used to help identify property and land use type boundaries. This process produced a draft land use raster.Verification of the draft land use dataset, particularly those with significant areas of intensive land uses, was undertaken by comparing mapped land use classes with observed land use classes in the field where possible. The final raster image was converted to a vector coverage in ARC/Info and GIS editing performed.The existing 1999 baseline (or later where available) land use dataset (vector) formed the basis for the 2006 and 2009 land use mapping. The 2006 & 2009 datasets were then updated primarily by interpretation of SPOT5 imagery, high-res orthophotography, scanned aerial photography and inclusion of expert local knowledge. This was performed in an ESRI ArcSDE geodatabase replication infrastructure, across some nine regional offices. The DCDB, QVAS, Estates, Queensland Herbarium wetlands and SLATS land cover change and waterbody datasets were used to assist in identification and delineation of property and land use type boundaries. Digitised areas of uniform land use type were assigned to land use classes according to ALUMC Version 7 (May 2010).This "current" land use mapping product presents a complete state-wide land use map of Queensland, after collating the most current land use datasets within a single mapping layer.An independent validation was undertaken to assess thematic (attribute) accuracy under the ALUM classification. Please refer to the orignal source data for the validation results.

Dataset Citation

Queensland Department of Science, Information Technology, Innovation and the Arts (2013) Bioregional_Assessment_Programme_Land use mapping - Queensland current. Bioregional Assessment Source Dataset. Viewed 21 December 2017, http://data.bioregionalassessments.gov.au/dataset/740d257f-b622-49c2-9745-be283239add3.

The development and generation of the datasets that are published through this data release, were based on the results and findings of the report: Kohn, M.S. and Patton, T.T., 2018, Flood-Inundation Maps for the South Platte River at Fort Morgan, Colorado, 2018: U.S. Geological Survey Scientific Investigations Report 2018-5114, 14 p., https://doi.org/10.3133/sir20185114. The geospatial dataset contain final versions of the raster and vector geospatial data and related metadata. The geospatial data include inundation extents, corresponding inundation depths, and the study area boundaries. Digital flood-inundation maps for a 4.5-mile reach of the South Platte River at Fort Morgan, Colorado from Morgan County Road 16 to Morgan County 20.5, were created by the U.S. Geological Survey (USGS) in cooperation with the Colorado Water Conservation Board. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site (https://water.usgs.gov/osw/flood_inundation/), depict estimates of the areal extent and depth of flooding corresponding to select water levels (stages) at USGS streamgage 06759500, South Platte River at Fort Morgan. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information are available through the National Water Information System web interface or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (http:/water.weather.gov/ahps/). Water-profiles were computed for the stream reach by means of a one-dimensional, step-backwater model. The September 15, 2013 and May 20, 2017 floods were used to calibrate the model, and the June 15, 2015 and May 29, 2017 floods were used to independently validate the model. Nine pressure transducers were deployed to record the stage at nine different locations along the reach and to document the floods of May 20 and 29, 2017 at the South Platte River at Fort Morgan streamgage. The calibrated hydraulic model was then used to determine 16 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from 12 ft (3.66 m) or below bankfull to 27 ft (8.23 m), which is 1 ft (0.3 m) greater than the highest recorded water level (25.73 ft [7.84 m] on September 15, 2013) at the South Platte River at Fort Morgan streamgage during its period of record and the 2013 flood exceeds the major flood stage of 21.5 ft (6.55 m) by more than 4 ft (1.2 m) as defined by the National Weather Service. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging) to delineate the area flooded at stages ranging from 12-ft to 27-ft. The availability of these inundation maps, along with internet information regarding the current stage from the USGS streamgage 06759500, South Platte River at Fort Morgan, Colorado, and forecast river stages from the NWS Advanced Hydrologic Prediction Service, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019

This paper performs, describes, and evaluates a comparison of seven software tools (ArcGIS Pro, GRASS GIS, SAGA GIS, CitySim, Ladybug, SimStadt and UMEP) to calculate solar irradiation. The analysis focuses on data requirements, software usability, and accuracy simulation output. The use case for the comparison is solar irradiation on building surfaces, in particular on roofs. The research involves collecting and preparing spatial and weather data. Two test areas - the Santana district in S ̃ao Paulo, Brazil, and the Heino rural area in Raalte, the Netherlands - were selected. In both cases, the study area encompasses the vicinity of a weather station. Therefore, the meteorological data from these stations serve as ground truth for the validation of the simulation results. We create several models (raster and vector) to meet the diverse input requirements. We present our findings and discuss the output from the software tools from both quantitative and qualitative points of view. Vector-based simulation models offer better results than raster-based ones. However, they have more complex data requirements. Future research will focus on evaluating the quality of the simulation results on vertical and tilted surfaces as well as the calculation of direct and diffuse solar irradiation values for vector-based methods.

Soil surface clay percent is one of 19 attributes of soils chosen to underpin the land suitability assessment of the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project through the digital soil mapping process (DSM). This raster data (in GeoTIFF format) represents a modelled surface of clay in the soil surface measured as a percent and is derived from laboratory, MIR and environmental covariates. The data is used in assessment of soil physical factors eg water infiltration, seedling establishment and machinery workability. The attribute data file is named "ClayPredictions.tif". Also included are data reflecting confidence of the main dataset. This file is named "Clay_SD.tif". "SD" represents "standard deviation". The DSM process is described in the technical report: Bartley R, Thomas MF, Clifford D, Phillip S, Brough D, Harms D, Willis R, Gregory L, Glover M, Moodie K, Sugars M, Eyre L, Smith DJ, Hicks W and Petheram C (2013) Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project, CSIRO. This raster data provides improved soil information to identify opportunities and promote detailed investigation for a range of sustainable development options and was created within the “Land Suitability” component of FGARA projects. Lineage: This data has been created from a range of inputs and processing steps. Below is an overview. Broadly, the steps were to: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc.). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Soil Surface Clay Percenct Digital Soil Mapping (DSM) key attribute output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. Quality assessment of the attribute data is mapped spatially as a function of the model output by evaluating the rigour of the DSM attribute data using non-parametric bootstrapping of the DSM modelling. For more information refer to “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

The Geospatial Data Extraction Guide can be found here. The Geospatial Data Extraction Tool allows for the dynamic extraction of data from the Government of Canadas Open Data Portal. There is a selection of base layers including: Landsat mosaic Canadian Digital Surface Model Canadian Digital Elevation Model National Forest Inventory National Tiling System Grid Coverage National Parks Boundaries National Marine Conservation Areas Automatic Extraction Building Projects Limits The User can select the data to be extracted, including: CanVec Elevation Automatic Extraction Data CanVec CanVec contains more than 60 topographic features organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features.

This multiscale product originates from the best available geospatial data sources covering Canadian Territory. It offers quality topographic information in vector format complying with international geomatics standards. The document CanVec_Code in the Data Resourced section shows the list of entities and the scales at which they are available.The maximum extraction area is 150000km. Users are able to extract the following data:Lakes and rivers - Hydrographic featuresTransport networks - Transport featuresConstructions and land use - Manmade featuresMines, energy and communication networks - Resources Management FeaturesWooded areas, saturated soils and landscape - Land featuresElevation featuresMap Labels - Toponymic features (50K only)Output Options: OGC GeoPackage, ESRI file Geodatabase, ESRI ShapefileCoordinate System Options: NAD83 CSRS (EPSG:4617), WGS 84 / Pseudo-Mercator (EPSG:3857), NAD83 / Canada Atlas Lambert (EPSG:3979)Option to clip the data: Yes / NoScale Options: 1 / 50,000, 1 / 250,00ElevationElevation data consists of the Canadian Digital Elevation Model (CDDEM) and the Canadian Digital Surface Model (CDSM). These products are available for extraction along with their derived products (Shaded Relief, Color Shaded Relief, Color Relief, Slope Map*, Aspect Map* and Point Data). *Only available for CDEM.The maximum extraction area is 50000km. Users are able to extract the following data:Digital Elevation Model (DEM)Shaded ReliefColor ReliefColor Shaded ReliefSlope mapAspect mapPoint DataPick an azimuth between 0 and 360 Degrees: Direction of light source, between 0 and 360, measured in degrees, clockwise from the north.Pick an altitude between 0 and 90 degrees: Vertical direction of light source, from 0 (horizon) to 90 degrees (zenith).Enter a vertical exaggeration factor: Vertical exaggeration factor.Select the slope's measuring unit: Choice of degrees or percent slope.Coordinate System Options: NAD83 CSRS (EPSG:4617), WGS 84 / Pseudo-Mercator (EPSG:3857), NAD83 / Canada Atlas Lambert (EPSG:3979). Data is stored in geographic coordinates (longitude and latitude). However, it can also be offered in a plane coordinate projection (X and Y) at the time of extraction. Definition for the coordinate system can be found in the metadata.Select the DEM output formats: OGC GeoPackage, ESRI file Geodatabase, ESRI Shapefile. The source data (DEM or DSM) available formats are GeoTIFF and Esri ASCII Grid. The GeoTIFF format specification can be obtained from: https://www.pubdoc.org/fileformat/rasterimage/tiff/geotiff.pdf and https://geotiff.maptools.org/spec/geotiffhome.html.The Esri ASCII Grid format specification can be obtained from:https://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/esri-ascii-raster-format.htmSelect the Point Data output format: ASCII Gridded XYZ (xyz), ASCII Gridded CSV (.csv). The Point Data available formats are text CSV (.csv) (comma separated values) and text XYZ (.xyz) (space separated values). The format specification is the same for both (ASCII Gridded XYZ) and can be obtained from: https://www.gdal.org/frmt_xyz.htmlSelect the image resolution: 0.75 arc seconds, 1.5 arc seconds, 3 arc seconds, 6 arc seconds, 12 arc secondsEmail address (yourname@domain.com): When processed results will be deposited to the given email. The email information that you provide on this site is collected in accordance with the federal Privacy Act. You will be notified once your request has been processed and when it is ready for delivery. Informations about your privacy rights.The job status is listed and can be refreshed to see updates.Automatic Extraction DataThe maximum extraction area is 50000km. Users are able to extract the following data:BuildingsOutput Options: OGC GeoPackage, ESRI file Geodatabase, ESRI ShapefileCoordinate System Options: NAD83 CSRS (EPSG:4617), WGS 84 / Pseudo-Mercator (EPSG:3857), NAD83 / Canada Atlas Lambert (EPSG:3979)Email address (yourname@domain.com): When processed results will be deposited to the given email. The email information that you provide on this site is collected in accordance with the federal Privacy Act. You will be notified once your request has been processed and when it is ready for delivery. Informations about your privacy rights.The job status is listed and can be refreshed to see updates.

The collection contains raster and vector data acquired during the bathymetric study of Lake Issyk. The primary data is provided as a digital elevation model of the lakebed, stored in GeoTIFF format. The raster file (Esik_DEM.tif) possesses a spatial resolution of 1 meter and offers comprehensive data on the depths and morphology of the lakebed. A The digital elevation model (Esik_DEM.tif) is created based on a shapefile with point data representing depth values reduced to the corresponding coordinates. Spatial sampling algorithms were employed to generate the point shapefile. Employing an echo sounder with a data gathering interval of 1-2 seconds ensured the data's accuracy. The error in-depth readings is ±0.5% of the observed depth, attributable to the features of the echo sounder and external influences, including vessel speed and weather conditions. Nonetheless, the impact of these effects is mitigated due to the cyclical nature of data gathering.

This data release contains vector and raster geospatial data for delineating drainage basins in the Nevada StreamStats study area. Included are vector streamline, waterbody, and inner wall data used to hydrologically condition the digital elevation model. The hydrologically-conditioned digital elevation model, flow direction, flow accumulation, and stream delineation raster data are also included.

This data set includes the NetLogo code that was used to generate the agent-based model along with the required spatial data required to run the simulation. The spatial data included are one raster data set representing biomass and 1 vector shapefile representing trade cities. See the READ ME for instructions on how to get the model running.

Soil surface salinity is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Victoria River Water Resource Assessment (VIWRA) through the digital soil mapping process (DSM). Soil salinity represents the salt content of the soil. This raster data represents a modelled dataset of salinity at the soil surface and is derived from field measured and laboratory analysed site data, and environmental covariates. Data values are: 1 Surface salinity absent, 2 Surface salinity present. Soil surface salinity is a parameter used in land suitability assessments as it hinders seed establishment and retards plant growth. This raster data provides improved soil information used to underpin and identify opportunities and promote detailed investigation for a range of sustainable regional development options and was created within the ‘Land Suitability’ activity of the CSIRO VIWRA. A companion dataset and statistics reflecting reliability of this data are also provided and can be found described in the lineage section of this metadata record. Processing information is supplied in ranger R scripts and attributes were modelled using a Random Forest approach. The DSM process is described in the CSIRO VIWRA published report ‘Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. The Victoria River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Victoria catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: The soil surface salinity dataset has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO VIWRA published reports and in particular ' Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create soil surface salinity Digital Soil Mapping (DSM) attribute raster dataset. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 8. QA Quality assessment of this DSM attribute data was conducted by three methods. Method 1: Statistical (quantitative) method of the model and input data. Testing the quality of the DSM models was carried out using data withheld from model computations and expressed as OOB and confusion matrix results, giving an estimate of the reliability of the model predictions. These results are supplied. Method 2: Statistical (quantitative) assessment of the spatial attribute output data presented as a raster of the attributes “reliability”. This used the 500 individual trees of the attributes RF models to generate 500 datasets of the attribute to estimate model reliability for each attribute. For categorical attributes the method for estimating reliability is the Confusion Index. This data is supplied. Method 3: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. Across each of the study areas a two week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling using the reliability data of the attribute. The modelled DSM attribute value was assessed against the actual on-ground value. These results are published in the report cited in this metadata record.