Open-Source GIScience Online Course

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:

  1. apply QGIS to visualize, query, and analyze vector and raster spatial data.
  2. use available resources to further expand your knowledge of open-source technologies.
  3. describe and use a variety of open data formats.
  4. code in Python at an intermediate-level.
  5. read, summarize, visualize, and analyze data using open Python libraries.
  6. create spatial predictive models using Python and associated libraries.
  7. use SQL to perform table and spatial queries at an intermediate-level.

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Last Updated November 3, 2021, 01:36 (UTC)
Created November 3, 2021, 01:22 (UTC)