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.

LANDISVIEW is a tool, developed at the Knowledge Engineering Laboratory at Texas A&M University, to visualize and animate 8-bit/16-bit ERDAS GIS format (e.g., LANDIS and LANDIS-II output maps). It can also convert 8-bit/16-bit ERDAS GIS format into ASCII and batch files. LANDISVIEW provides two major functions: 1) File Viewer: Files can be viewed sequentially and an output can be generated as a movie file or as an image file. 2) File converter: It will convert the loaded files for compatibility with 3rd party software, such as Fragstats, a widely used spatial analysis tool. Some available features of LANDISVIEW include: 1) Display cell coordinates and values. 2) Apply user-defined color palette to visualize files. 3) Save maps as pictures and animations as video files (*.avi). 4) Convert ERDAS files into ASCII grids for compatibility with Fragstats. (Source: http://kelab.tamu.edu/)

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Foundation spatial data is defined as “the authoritative geographic information that underpins, or can add significant value to, any other information. It supports evidence-based decisions across government, industry and the community”1. Criteria has been developed to determine non-foundation from foundation. To be classified as foundation, the spatial data must contain one or more of the following characteristics to be deemed ‘foundation’: • be geospatial • essential for public safety and wellbeing • critical for a national and state or government function • contribute significantly to economic, social and environmental sustainability.

NSW has adopted the ten data themes identified by ANZLIC. These are: 1. Administrative Boundaries 2. Land Parcel and Property 3. Geocoded Addressing 4. Transport 5. Positioning 6. Place Names 7. Elevation and Depth 8. Imagery 9. Water 10. Land Cover.

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2032, growing at a CAGR of 12.10% during the forecast period 2026-2032.Geospatial Solutions Market: Definition/ OverviewGeospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth's surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today's interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

An National Geospatial Data Asset (NGDA) is defined as a geospatial dataset that has been designated by the FGDC Steering Committee and meets at least one of the following criteria: used by multiple agencies or with agency partners such as State, Tribal and local governments; applied to achieve Presidential priorities as expressed by OMB; required to meet shared mission goals of multiple Federal agencies; or expressly required by statutory mandate. Together, these datasets comprise the NGDA Portfolio. This metadata points to a spreadsheet that contains the official list of NGDA with a link to specific NGDA metadata maintained by the dataset owners on Data.gov, GeoPlatform.gov, a link to their associated NGDA Theme, and the agency responsible for the NGDA.

Within the U.S. Geological Survey (USGS), three-dimensional (3D) geologic models are created as part of geologic framework studies, to support energy, minerals, or water resource assessments, and to inform geologic hazard assessments. Such models are often used within the organization as digital input into process and predictive models. 3D geological modeling typically supports research and project work within a specific part of the USGS – called Mission Areas – and as a result, 3D modeling activities are decentralized and model results are released on a project-by-project basis. This digital data release inventories and catalogs, for the first time, 3D geological models constructed by the USGS across all Mission Areas. This inventory assembles in catalog form the spatial locations and salient characteristics of previously published USGS 3D geological models. This inventory covers the time period from 2004, the date of the earliest published model through 2022. This digital dataset contains spatial extents of the 3D geologic models as polygon features that are attributed with unique identifiers that link the spatial data to nonspatial tables that define the data sources used and describe various aspects of each published model. The nonspatial DataSources table includes full citation and URL address for both published model reports and any digital model data released as a separate publication. The nonspatial ModelAttributes table classifies the type of model, using several classification schemes, identifies the model purpose and originating agency, and describes the spatial extent, depth, and number of layers included in each model. A tabular glossary defines terms used in the dataset. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and the accompanying nonspatial tables.

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North America Geographic Information System Market Size 2025-2029

The geographic information system market size in North America is forecast to increase by USD 11.4 billion at a CAGR of 23.7% between 2024 and 2029.

The market is experiencing significant growth due to the increasing adoption of advanced technologies such as artificial intelligence, satellite imagery, and sensors in various industries. In fleet management, GIS software is being used to optimize routes and improve operational efficiency. In the context of smart cities, GIS solutions are being utilized for content delivery, public safety, and building information modeling. The demand for miniaturization of technologies is also driving the market, allowing for the integration of GIS into smaller devices and applications. However, data security concerns remain a challenge, as the collection and storage of sensitive information requires robust security measures. The insurance industry is also leveraging GIS for telematics and risk assessment, while the construction sector uses GIS for server-based project management and planning. Overall, the GIS market is poised for continued growth as these trends and applications continue to evolve.

What will be the Size of the market During the Forecast Period?

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The Geographic Information System (GIS) market encompasses a range of technologies and applications that enable the collection, management, analysis, and visualization of spatial data. Key industries driving market growth include transportation, infrastructure planning, urban planning, and environmental monitoring. Remote sensing technologies, such as satellite imaging and aerial photography, play a significant role in data collection. Artificial intelligence and the Internet of Things (IoT) are increasingly integrated into GIS solutions for real-time location data processing and operational efficiency.
Applications span various sectors, including agriculture, natural resources, construction, and smart cities. GIS is essential for infrastructure analysis, disaster management, and land management. Geospatial technology enables spatial data integration, providing valuable insights for decision-making and optimization. Market size is substantial and growing, fueled by increasing demand for efficient urban planning, improved infrastructure, and environmental sustainability. Geospatial startups continue to emerge, innovating in areas such as telematics, natural disasters, and smart city development.

How is this market segmented and which is the largest segment?

The market 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.

Component

  Software
  Data
  Services


Deployment

  On-premise
  Cloud


Geography

  North America

    Canada
    Mexico
    US

By Component Insights

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

The Geographic Information System (GIS) market encompasses desktop, mobile, cloud, and server software for managing and analyzing spatial data. In North America, industry-specific GIS software dominates, with some commercial entities providing open-source alternatives for limited functions like routing and geocoding. Despite this, counterfeit products pose a threat, making open-source software a viable option for smaller applications. Market trends indicate a shift towards cloud-based GIS solutions for enhanced operational efficiency and real-time location data. Spatial data applications span various sectors, including transportation infrastructure planning, urban planning, natural resources management, environmental monitoring, agriculture, and disaster management. Technological innovations, such as artificial intelligence, the Internet of Things (IoT), and satellite imagery, are revolutionizing GIS solutions.

Cloud-based GIS solutions, IoT integration, and augmented reality are emerging trends. Geospatial technology is essential for smart city projects, climate monitoring, intelligent transportation systems, and land management. Industry statistics indicate steady growth, with key players focusing on product innovation, infrastructure optimization, and geospatial utility solutions.

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Market Dynamics

Our North America Geographic Information System Market researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in the adoption of the North America Geographic Information System Market?

Rising applications of geographic

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

OSM is produced as a public good by volunteers, and there are no guarantees about data quality. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

OSM represents physical features on the ground (e.g. roads or buildings) using tabs attached to its basic data structure (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by the specific node, way or relation.

Nodes are one of the core elements in the OSM data model. It consists of a single point in space defined by its latitude, longitude and node id. Nodes can be used to define standalone point features.

Summary: Creating the world’s first open-source, high-resolution, land cover map of the worldStorymap metadata page: URL forthcoming Possible K-12 Next Generation Science standards addressed:Grade level(s) K: Standard K-ESS3-1 - Earth and Human Activity - Use a model to represent the relationship between the needs of different plants or animals (including humans) and the places they liveGrade level(s) K: Standard K-ESS3-3 - Earth and Human Activity - Communicate solutions that will reduce the impact of humans on the land, water, air, and/or other living things in the local environmentGrade level(s) 2: Standard 2-ESS2-1 - Earth’s Systems - Compare multiple solutions designed to slow or prevent wind or water from changing the shape of the landGrade level(s) 2: Standard 2-ESS2-2 - Earth’s Systems - Develop a model to represent the shapes and kinds of land and bodies of water in an areaGrade level(s) 3: Standard 3-LS4-1 - Biological Evolution: Unity and Diversity - Analyze and interpret data from fossils to provide evidence of the organisms and the environments in which they lived long ago.Grade level(s) 3: Standard 3-LS4-1 - Biological Evolution: Unity and Diversity - Analyze and interpret data from fossils to provide evidence of the organisms and the environments in which they lived long ago.Grade level(s) 3: Standard 3-LS4-4 - Biological Evolution: Unity and Diversity - Make a claim about the merit of a solution to a problem caused when the environment changes and the types of plants and animals that live there may changeGrade level(s) 4: Standard 4-ESS1-1 - Earth’s Place in the Universe - Identify evidence from patterns in rock formations and fossils in rock layers to support an explanation for changes in a landscape over timeGrade level(s) 4: Standard 4-ESS2-2 - Earth’s Systems - Analyze and interpret data from maps to describe patterns of Earth’s featuresGrade level(s) 5: Standard 5-ESS2-1 - Earth’s Systems - Develop a model using an example to describe ways the geosphere, biosphere, hydrosphere, and/or atmosphere interact.Grade level(s) 6-8: Standard MS-ESS2-2 - Earth’s Systems - Construct an explanation based on evidence for how geoscience processes have changed Earth’s surface at varying time and spatial scalesGrade level(s) 6-8: Standard MS-ESS2-6 - Earth’s Systems - Develop and use a model to describe how unequal heating and rotation of the Earth cause patterns of atmospheric and oceanic circulation that determine regional climates.Grade level(s) 6-8: Standard MS-ESS3-3 - Earth and Human Activity - Apply scientific principles to design a method for monitoring and minimizing a human impact on the environment.Grade level(s) 9-12: Standard HS-ESS2-1 - Earth’s Systems - Develop a model to illustrate how Earth’s internal and surface processes operate at different spatial and temporal scales to form continental and ocean-floor features.Grade level(s) 9-12: Standard HS-ESS2-7 - Earth’s Systems - Construct an argument based on evidence about the simultaneous coevolution of Earth’s systems and life on EarthGrade level(s) 9-12: Standard HS-ESS3-4 - Earth and Human Activity - Evaluate or refine a technological solution that reduces impacts of human activities on natural systems.Grade level(s) 9-12: Standard HS-ESS3-6 - Earth and Human Activity - Use a computational representation to illustrate the relationships among Earth systems and how those relationships are being modified due to human activityMost frequently used words:areaslandclassesApproximate Flesch-Kincaid reading grade level: 9.7. The FK reading grade level should be considered carefully against the grade level(s) in the NGSS content standards above.

Buildings_BACI File Geodatabase Feature Class Thumbnail Not Available Tags Buildings, structures, ruins, storage tanks, silos, water towers, Baltimore City Planimetric, Biophysical Resources, Land, Socio-Economic Resources, Capital Summary This data was created as a landbase feature as part of the planimetric data. Description This dataset represents photogrammetrically captured Building footprints => 100sq. ft. including storage tanks, silos, water towers, power plants, substations, and structures under construction and ruins. Feature capture rules: Buildings - Outline edge of roofline. All buildings shall be captured as polygons. In commercial areas especially, it is important that the plotted building represent the face of the building where it meets the sidewalk. Polygons shall be created for the outer boundary of the building when a partywall exists. Does not include sheds and small temporary structures. Attached garages shall be represented as part of the building structure. Large structures such as stadiums shall also be represented. Structures under construction or demolition - Delineate the rooflines of all buildings under construction as interpreted from aerial photography. If roofline is not visible compile visible foundation or walls Ruins - Delineate old overgrown areas of old structures that have been demolished or are in disrepair. Original data will be reclassified to define as separate subtype. Storage tanks, silos, and water towers - Outlines of all storage tanks, silos and water towers. . Original data will be reclassified to define as separate subtype. Power plants and substations - Outline of power plant and substation structure. . Original data will be reclassified to define as separate subtype. Credits There are no credits for this item. Use limitations Every reasonable effort has been made to ensure the accuracy of these data. The City of Baltimore, Maryland makes no representations nor warranties, either express or implied, regarding the accuracy of this information or its suitability for any particular purpose whatsoever. The data is licensed "as is" and the City of Baltimore will not be liable for its use or misuse by any party. Reliance of these data is at the risk of the user. Extent West -76.714715 East -76.525355 North 39.375162 South 39.193953 Scale Range There is no scale range for this item.

The Geospatial Analytics Marketsize was valued at USD 79.06 USD billion in 2023 and is projected to reach USD 202.74 USD billion by 2032, exhibiting a CAGR of 14.4 % during the forecast period. Recent developments include: February 2024: Placer.ai and Esri, a Geographic Information System (GIS) technology provider, partnered to empower customers with enhanced analytics capabilities, integrating consumer behavior analysis. Additionally, the agreement will foster collaborations to unlock further features by synergizing our respective product offerings., December 2023: CKS and Esri India Technologies Pvt Ltd teamed up to introduce the 'MMGEIS' program, focusing on students from 8th grade to undergraduates, to position India as a global leader in geospatial technology through skill development and innovation., December 2023: In collaboration with Bayanat, the UAE Space Agency revealed the initiation of the operational phase of the Geospatial Analytics Platform during its participation in organizing the Space at COP28 initiatives., November 2023: USAID unveiled its inaugural Geospatial Strategy, designed to harness geospatial data and technology for more targeted international program delivery. The strategy foresees a future where geographic methods enhance the effectiveness of USAID's efforts by pinpointing development needs, monitoring program implementation, and evaluating outcomes based on location., May 2023: TomTom International BV, a geolocation technology specialist, expanded its partnership with Alteryx, Inc. Through this partnership, Alteryx will use TomTom’s Maps APIs and location data to integrate spatial data into Alteryx’s products and location insights packages, such as Alteryx Designer., May 2023: Oracle Corporation announced the launch of Oracle Spatial Studio 23.1, available in the Oracle Cloud Infrastructure (OCI) marketplace and for on-premises deployment. Users can browse, explore, and analyze geographic data stored in and managed by Oracle using a no-code mapping tool., May 2023: CAPE Analytics, a property intelligence company, announced an enhanced insurance offering by leveraging Google geospatial data. Google’s geospatial data can help CAPE create appropriate solutions for insurance carriers., February 2023: HERE Global B.V. announced a collaboration with Cognizant, an information technology, services, and consulting company, to offer digital customer experience using location data. In this partnership, Cognizant will utilize the HERE location platform’s real-time traffic data, weather, and road attribute data to develop spatial intelligent solutions for its customers., July 2022: Athenium Analytics, a climate risk analytics company, launched a comprehensive tornado data set on the Esri ArcGIS Marketplace. This offering, which included the last 25 years of tornado insights from Athenium Analytics, would extend its Bronze partner relationship with Esri. . Key drivers for this market are: Advancements in Technologies to Fuel Market Growth. Potential restraints include: Lack of Standardization Coupled with Shortage of Skilled Workforce to Limit Market Growth. Notable trends are: Rise of Web-based GIS Platforms Will Transform Market.

This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section provides spatial data files that describe the rivers, reservoirs, and observational data in the Delaware River Basin included in this release. One shapefile of polylines describes the 459 river reaches that define the modeling network, and another shapefile of polygons includes the three reservoirs (Pepacton, Cannonsville, and Neversink) for which data are included in this release. Additionally, a point shapefile contains locations of monitoring sites along the reaches with supporting attributes that describe the monitoring location.

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

From the site: "The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary."

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

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.

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

OSM is produced as a public good by volunteers, and there are no guarantees about data quality. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

OSM represents physical features on the ground (e.g. roads or buildings) using tabs attached to its basic data structure (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by the specific node, way or relation.

Nodes are one of the core elements in the OSM data model. It consists of a single point in space defined by its latitude, longitude and node id. Nodes can be used to define standalone point features.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Final WUPA map classes used for interpreting the aerial photographs were derived (1) from plant associations and alliances described by CPRS, (2) from the Anderson (1976) Level II land use classification system, (3) from land cover classes, and (4) from unique stands specific to WUPA. A draft hard copy vegetation map at the 1:12,000 scale was printed and checked against the interpreted aerial photographs. As a final internal accuracy check, RSGIG applied photointerpretation observations and classification relevés over the vegetation map to determine if the polygon labels matched the field data. Map validation occurred prior to the accuracy assessment. Because of the difficulties in interpreting the vegetation directly from the aerial photographs, we eventually mapped and/or validated much of the project area in the field. Metadata are required for all spatial data produced by the federal government. RSGIG used SIMMS™ software to create the three FGDC-compliant metadata files attached to the spatial databases and to this report. The metadata files explain the vegetation coverage and ancillary coverages created by RSGIG, the plot data coverage created by CPRS, and the accuracy assessment data created by CPRS.

The Referrals Spatial Database records locations of referrals submitted to the Department under the Environment Protection and Biodiversity Conservation (EPBC Act) 1999. A proponent (those who are proposing a development) must supply the maximum extent (location) of any proposed activities that need to be assessed under the EPBC Act through an application process. Referral boundaries should not be misinterpreted as development footprints but where referrals have been received by the Department. It should be noted that not all referrals captured within the Referrals Spatial Database, are assessed and approved by the Minister for the Environment, as some are withdrawn before assessment can take place. For more detailed information on a referral a URL is provided to the EPBC Act Public notices pages. Status and detailed planning documentation is available on the EPBC Act Public notices (http://epbcnotices.environment.gov.au/referralslist/). This includes some summary statistics and public notifications. In the first instance where a proponent provides GIS data through a referral application, this is loaded as the polygons for a referral. Where this doesn't exist other means to digitise boundaries are employed to provide a relatively accurate reflection of the maximum extent for which the referral may impact. This sometimes takes the form of heads up digitising planning documents, sourcing from other state databases (such as PSMA Australia) features and coordinates supplied through the application forms.This data has been licensed under the Creative Commons Attribution 3.0 Australia Licence. More information can be found at http://www.ausgoal.gov.au/creative-commons. Referral spatial boundaries are indicative only and should not be used to define the totality of onground works including being used as a substitute for an onground development footprint. Through the assessment and approval process, boundaries may be updated by a proponent to reflect improved understanding of the extent of a development. Where advised ERIN update the EPBC Act Referrals Spatial Database to reflect these changes however no guarantee is given to this. (c) Commonwealth of Australia (Department of the Environment and Energy) 2018 CC - Attribution (CC BY)