100+ datasets found
  1. Inform E-learning GIS Course

    • png-data.sprep.org
    pdf
    Updated Feb 20, 2025
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    SPREP (2025). Inform E-learning GIS Course [Dataset]. https://png-data.sprep.org/dataset/inform-e-learning-gis-course
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    pdf(658923), pdf(501586), pdf(1335336), pdf(587295)Available download formats
    Dataset updated
    Feb 20, 2025
    Dataset provided by
    Pacific Regional Environment Programmehttps://www.sprep.org/
    License

    Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
    License information was derived automatically

    Area covered
    Pacific Region
    Description

    This dataset holds all materials for the Inform E-learning GIS course

  2. G

    QGIS Training Tutorials: Using Spatial Data in Geographic Information...

    • open.canada.ca
    html
    Updated Oct 5, 2021
    + more versions
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    Statistics Canada (2021). QGIS Training Tutorials: Using Spatial Data in Geographic Information Systems [Dataset]. https://open.canada.ca/data/en/dataset/89be0c73-6f1f-40b7-b034-323cb40b8eff
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    htmlAvailable download formats
    Dataset updated
    Oct 5, 2021
    Dataset provided by
    Statistics Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    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.

  3. Open-Source GIScience Online Course

    • ckan.americaview.org
    Updated Nov 2, 2021
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    ckan.americaview.org (2021). Open-Source GIScience Online Course [Dataset]. https://ckan.americaview.org/dataset/open-source-giscience-online-course
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    Dataset updated
    Nov 2, 2021
    Dataset provided by
    CKANhttps://ckan.org/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  4. D

    Geographic Information System GIS Tools Market Report | Global Forecast From...

    • dataintelo.com
    csv, pdf, pptx
    Updated Sep 12, 2024
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    Dataintelo (2024). Geographic Information System GIS Tools Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-geographic-information-system-gis-tools-market
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    pptx, pdf, csvAvailable download formats
    Dataset updated
    Sep 12, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Geographic Information System (GIS) Tools Market Outlook



    The global Geographic Information System (GIS) tools market size was valued at approximately USD 10.8 billion in 2023, and it is projected to reach USD 21.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2032. The increasing demand for spatial data analytics and the rising adoption of GIS tools across various industries are significant growth factors propelling the market forward.



    One of the primary growth factors for the GIS tools market is the surging demand for spatial data analytics. Spatial data plays a critical role in numerous sectors, including urban planning, environmental monitoring, disaster management, and natural resource exploration. The ability to visualize and analyze spatial data provides organizations with valuable insights, enabling them to make informed decisions. Advances in technology, such as the integration of artificial intelligence (AI) and machine learning (ML) with GIS, are enhancing the capabilities of these tools, further driving market growth.



    Moreover, the increasing adoption of GIS tools in the construction and agriculture sectors is fueling market expansion. In construction, GIS tools are used for site selection, route planning, and resource management, enhancing operational efficiency and reducing costs. Similarly, in agriculture, GIS tools aid in precision farming, crop monitoring, and soil analysis, leading to improved crop yields and sustainable farming practices. The ability of GIS tools to provide real-time data and analytics is particularly beneficial in these industries, contributing to their widespread adoption.



    The growing importance of location-based services (LBS) in various applications is another key driver for the GIS tools market. LBS are extensively used in navigation, logistics, and transportation, providing real-time location information and route optimization. The proliferation of smartphones and the development of advanced GPS technologies have significantly increased the demand for LBS, thereby boosting the GIS tools market. Additionally, the integration of GIS with other technologies, such as the Internet of Things (IoT) and Big Data, is creating new opportunities for market growth.



    Regionally, North America holds a significant share of the GIS tools market, driven by the high adoption of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to increasing investments in infrastructure development, smart city projects, and the growing use of GIS tools in emerging economies such as China and India. Europe, Latin America, and the Middle East & Africa are also expected to contribute to market growth, driven by various government initiatives and increasing awareness of the benefits of GIS tools.



    Component Analysis



    The GIS tools market can be segmented by component into software, hardware, and services. The software segment is anticipated to dominate the market due to the increasing demand for advanced GIS software solutions that offer enhanced data visualization, spatial analysis, and decision-making capabilities. GIS software encompasses a wide range of applications, including mapping, spatial data analysis, and geospatial data management, making it indispensable for various industries. The continuous development of user-friendly and feature-rich software solutions is expected to drive the growth of this segment.



    Hardware components in the GIS tools market include devices such as GPS units, remote sensing devices, and plotting and digitizing tools. The hardware segment is also expected to witness substantial growth, driven by the increasing use of advanced hardware devices that provide accurate and real-time spatial data. The advancements in GPS technology and the development of sophisticated remote sensing devices are key factors contributing to the growth of the hardware segment. Additionally, the integration of hardware with IoT and AI technologies is enhancing the capabilities of GIS tools, further propelling market expansion.



    The services segment includes consulting, integration, maintenance, and support services related to GIS tools. This segment is expected to grow significantly, driven by the increasing demand for specialized services that help organizations effectively implement and manage GIS solutions. Consulting services assist organizations in selecting the right GIS tools and optimizing their use, while integration services ensure seamless integr

  5. w

    Dataset of books called Learning GIS using open source software : an applied...

    • workwithdata.com
    Updated Apr 17, 2025
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    Work With Data (2025). Dataset of books called Learning GIS using open source software : an applied guide for geo-spatial analysis [Dataset]. https://www.workwithdata.com/datasets/books?f=1&fcol0=book&fop0=%3D&fval0=Learning+GIS+using+open+source+software+%3A+an+applied+guide+for+geo-spatial+analysis
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    Dataset updated
    Apr 17, 2025
    Dataset authored and provided by
    Work With Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset is about books. It has 1 row and is filtered where the book is Learning GIS using open source software : an applied guide for geo-spatial analysis. It features 7 columns including author, publication date, language, and book publisher.

  6. a

    Telling Stories with GIS Maps

    • hub.arcgis.com
    Updated May 17, 2019
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    State of Delaware (2019). Telling Stories with GIS Maps [Dataset]. https://hub.arcgis.com/documents/delaware::telling-stories-with-gis-maps
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    Dataset updated
    May 17, 2019
    Dataset authored and provided by
    State of Delaware
    Description

    In this course, you will explore different kinds of story maps and learn to create your own.GoalsUse GIS maps to communicate a story.Interpret different types of story maps.Create a web app.Use a template to make a story map.

  7. d

    Datasets for Computational Methods and GIS Applications in Social Science

    • search.dataone.org
    Updated Sep 25, 2024
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    Fahui Wang; Lingbo Liu (2024). Datasets for Computational Methods and GIS Applications in Social Science [Dataset]. http://doi.org/10.7910/DVN/4CM7V4
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    Dataset updated
    Sep 25, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Fahui Wang; Lingbo Liu
    Description

    Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

  8. Teaching and Learning With ArcGIS Online

    • lecturewithgis.co.uk
    Updated Jan 28, 2023
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    Esri UK Education (2023). Teaching and Learning With ArcGIS Online [Dataset]. https://lecturewithgis.co.uk/datasets/teaching-and-learning-with-arcgis-online-1
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    Dataset updated
    Jan 28, 2023
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri UK Education
    Description

    Prior experience of GIS is variable, but a number of PGCE students and in-service teachers reported negative prior experiences with geospatial technology. Common complaints include a course focussed on data students found irrelevant, with learning exercises in the form of list-like instructions. The complexity of desktop GIS software is also often mentioned as off-putting.

  9. f

    Data from: Visual programming-based Geospatial Cyberinfrastructure for...

    • tandf.figshare.com
    docx
    Updated Mar 4, 2025
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    Lingbo Liu; Weihe Wendy Guan; Fahui Wang; Shuming Bao (2025). Visual programming-based Geospatial Cyberinfrastructure for open-source GIS education 3.0 [Dataset]. http://doi.org/10.6084/m9.figshare.28472871.v1
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    docxAvailable download formats
    Dataset updated
    Mar 4, 2025
    Dataset provided by
    Taylor & Francis
    Authors
    Lingbo Liu; Weihe Wendy Guan; Fahui Wang; Shuming Bao
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Open-Source GIS plays a pivotal role in advancing GIS education, fostering research collaboration, and supporting global sustainability by enabling the sharing of data, models, and knowledge. However, the integration of big data, deep learning methods, and artificial intelligence deep learning in geospatial research presents significant challenges for GIS education. These include increasing software learning costs, higher computational power demand, and the management of fragmented information in the Web 2.0 context. Addressing these challenges while integrating emerging GIS innovations and restructuring GIS knowledge systems is crucial for the evolution of GIS Education 3.0. This study introduces a Visual Programming-based Geospatial Cyberinfrastructure (V-GCI) framework, integrated with the replicable and reproducible (R&R) framework, to enhance GIS function compatibility, learning scalability, and web GIS application interoperability. Through a case study on spatial accessibility using the generalized two-step floating catchment area method (G2SFCA), this paper demonstrates how V-GCI can reshape the GIS knowledge tree and its potential to enhance replicability and reproducibility within open-source GIS Education 3.0.

  10. d

    Seattle Parks and Recreation GIS Map Layer Web Services URL - Environmental...

    • catalog.data.gov
    Updated Jan 31, 2025
    + more versions
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    data.seattle.gov (2025). Seattle Parks and Recreation GIS Map Layer Web Services URL - Environmental Learning Centers [Dataset]. https://catalog.data.gov/dataset/seattle-parks-and-recreation-gis-map-layer-web-services-url-environmental-learning-centers-b6f93
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    Dataset updated
    Jan 31, 2025
    Dataset provided by
    data.seattle.gov
    Area covered
    Seattle
    Description

    Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Environmental Learning Centers dataset.

  11. Geospatial Deep Learning Seminar Online Course

    • ckan.americaview.org
    Updated Nov 2, 2021
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    ckan.americaview.org (2021). Geospatial Deep Learning Seminar Online Course [Dataset]. https://ckan.americaview.org/dataset/geospatial-deep-learning-seminar-online-course
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    Dataset updated
    Nov 2, 2021
    Dataset provided by
    CKANhttps://ckan.org/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  12. Data from: Automatic extraction of road intersection points from USGS...

    • figshare.com
    zip
    Updated Nov 11, 2019
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    Mahmoud Saeedimoghaddam; Tomasz Stepinski (2019). Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks [Dataset]. http://doi.org/10.6084/m9.figshare.10282085.v1
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    zipAvailable download formats
    Dataset updated
    Nov 11, 2019
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Mahmoud Saeedimoghaddam; Tomasz Stepinski
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tagged image tiles as well as the Faster-RCNN framework for automatic extraction of road intersection points from USGS historical maps of the United States of America. The data and code have been prepared for the paper entitled "Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks" submitted to "International Journal of Geographic Information Science". The image tiles have been tagged manually. The Faster RCNN framework (see https://arxiv.org/abs/1611.10012) was captured from:https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

  13. a

    Integrating Data in ArcGIS Pro

    • hub.arcgis.com
    Updated Mar 25, 2020
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    State of Delaware (2020). Integrating Data in ArcGIS Pro [Dataset]. https://hub.arcgis.com/documents/3a11f895a7dc4d28ad45cee9cc5ba6d8
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    Dataset updated
    Mar 25, 2020
    Dataset authored and provided by
    State of Delaware
    Description

    In this course, you will learn about some common types of data used for GIS mapping and analysis, and practice adding data to a file geodatabase to support a planned project.Goals Create a file geodatabase. Add data to a file geodatabase. Create an empty geodatabase feature class.

  14. Data from: GIScience

    • ckan.americaview.org
    Updated Sep 10, 2022
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    ckan.americaview.org (2022). GIScience [Dataset]. https://ckan.americaview.org/dataset/giscience
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    Dataset updated
    Sep 10, 2022
    Dataset provided by
    CKANhttps://ckan.org/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In this course, you will explore the concepts, principles, and practices of acquiring, storing, analyzing, displaying, and using geospatial data. Additionally, you will investigate the science behind geographic information systems and the techniques and methods GIS scientists and professionals use to answer questions with a spatial component. In the lab section, you will become proficient with the ArcGIS Pro software package. This course will prepare you to take more advanced geospatial science courses. You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises, assignments, and less guided challenges. Please see the sequencing document for our suggestions as to the order in which to work through the material. To aid in working through the lecture modules, we have provided PDF versions of the lectures with the slide notes included. This course makes use of the ArcGIS Pro software package from the Environmental Systems Research Institute (ESRI), and directions for installing the software have also been provided. If you are not a West Virginia University student, you can still complete the labs, but you will need to obtain access to the software on your own.

  15. e

    Get to Know GIS - Learning Plan for Secondary School Students

    • gisinschools.eagle.co.nz
    Updated Nov 13, 2014
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    GIS in Schools - Teaching Materials - New Zealand (2014). Get to Know GIS - Learning Plan for Secondary School Students [Dataset]. https://gisinschools.eagle.co.nz/items/f74cd488f30b4f5eabc91859d9f88bbd
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    Dataset updated
    Nov 13, 2014
    Dataset authored and provided by
    GIS in Schools - Teaching Materials - New Zealand
    Description

    Learn the basics of GIS. Work with ArcGIS Online to interact with GIS maps, explore real world problems, and tell a story. Find out how workers use GIS and what it takes to become a GIS professional.

  16. H

    Digital Elevation Models and GIS in Hydrology (M2)

    • hydroshare.org
    zip
    Updated Jun 7, 2021
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    Irene Garousi-Nejad; Belize Lane (2021). Digital Elevation Models and GIS in Hydrology (M2) [Dataset]. http://doi.org/10.4211/hs.9c4a6e2090924d97955a197fea67fd72
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    zip(88.2 MB)Available download formats
    Dataset updated
    Jun 7, 2021
    Dataset provided by
    HydroShare
    Authors
    Irene Garousi-Nejad; Belize Lane
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Description

    This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

    In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

    Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

  17. 13.2 Building Models for GIS Analysis Using ArcGIS

    • hub.arcgis.com
    Updated Mar 4, 2017
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    Iowa Department of Transportation (2017). 13.2 Building Models for GIS Analysis Using ArcGIS [Dataset]. https://hub.arcgis.com/documents/383bea21ddd94319a3cf86c1994ac652
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    Dataset updated
    Mar 4, 2017
    Dataset authored and provided by
    Iowa Department of Transportationhttps://iowadot.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ArcGIS has many analysis and geoprocessing tools that can help you solve real-world problems with your data. In some cases, you are able to run individual tools to complete an analysis. But sometimes you may require a more comprehensive way to create, share, and document your analysis workflow.In these situations, you can use a built-in application called ModelBuilder to create a workflow that you can reuse, modify, save, and share with others.In this course, you will learn the basics of working with ModelBuilder and creating models. Models contain many different elements, many of which you will learn about. You will also learn how to work with models that others create and share with you. Sharing models is one of the major advantages of working with ModelBuilder and models in general. You will learn how to prepare a model for sharing by setting various model parameters.After completing this course, you will be able to:Identify model elements and states.Describe a prebuilt model's processes and outputs.Create and document models for site selection and network analysis.Define model parameters and prepare a model for sharing.

  18. Iowa K-12 GIS Educator Resources

    • ckan.americaview.org
    Updated Jan 24, 2023
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    ckan.americaview.org (2023). Iowa K-12 GIS Educator Resources [Dataset]. https://ckan.americaview.org/dataset/iowa-k-12-gis-educator-resources
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    Dataset updated
    Jan 24, 2023
    Dataset provided by
    CKANhttps://ckan.org/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Iowa
    Description

    Do you need help in learning how to include GIS into your classroom activities? IowaView has developed a helpful guide for K-12 educators to learn what tools are available and how to integrate them into the classroom. Some of the geography content in this guide is specific to Iowa but the tools and resources are useful for anyone involved in K-12 education.

  19. m

    GIS course Training Flier

    • maconinsights.maconbibb.us
    Updated Aug 19, 2021
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    Macon-Bibb County Government (2021). GIS course Training Flier [Dataset]. https://maconinsights.maconbibb.us/documents/ed385f781f584f48b26bf5d1fd967611
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    Dataset updated
    Aug 19, 2021
    Dataset authored and provided by
    Macon-Bibb County Government
    Area covered
    Description

    This is GIS course announcement flier.

  20. Geospatial data for the Vegetation Mapping Inventory Project of Pictured...

    • catalog.data.gov
    Updated Sep 26, 2025
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    National Park Service (2025). Geospatial data for the Vegetation Mapping Inventory Project of Pictured Rocks National Lakeshore [Dataset]. https://catalog.data.gov/dataset/geospatial-data-for-the-vegetation-mapping-inventory-project-of-pictured-rocks-national-la
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    Dataset updated
    Sep 26, 2025
    Dataset provided by
    National Park Servicehttp://www.nps.gov/
    Area covered
    Pictured Rocks
    Description

    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. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

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SPREP (2025). Inform E-learning GIS Course [Dataset]. https://png-data.sprep.org/dataset/inform-e-learning-gis-course
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Inform E-learning GIS Course

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pdf(658923), pdf(501586), pdf(1335336), pdf(587295)Available download formats
Dataset updated
Feb 20, 2025
Dataset provided by
Pacific Regional Environment Programmehttps://www.sprep.org/
License

Public Domain Mark 1.0https://creativecommons.org/publicdomain/mark/1.0/
License information was derived automatically

Area covered
Pacific Region
Description

This dataset holds all materials for the Inform E-learning GIS course

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