Introduction to Spatial Analysis in QGIS (Online)

Learning

Introduction to Spatial Analysis in QGIS (Online)

COURSE DESCRIPTION: An introductory course for students interested in applying GIS as a tool to help answer important questions in the natural sciences, or for those with ArcGIS experience looking to transition to an Open-Source platform. This course presents the concepts upon which GIS technology is based including the following fundamentals: cartography, geodesy, coordinate systems, and projections. Conceptual overview and hand-on experience of vector data analyses and table queries are introduced. Students will use QGIS to classify data, query tables, analyze spatial relationships, set map projections, build spatial databases, edit data, and create map layouts. Lectures are followed by hands-on activities to develop and reinforce methodologies for GIS analyses.

TOPICS:

  • Intro to QGIS
  • Downloading GIS data online (Maine Geolibrary)
  • Preparing Spatial Data in Excel
  • Creating Vector Data (points)
  • Digitizing Vector Data (polygon)
  • Raster Data Basics
  • Projections and transformations (Coordinate systems)
  • Extracting values to points
  • Exporting data from QGIS
  • Map design
  • Publishing maps online

PREREQUISITES: No experience using a Geographical Information System (GIS) is required. QGIS is free, open-source, and runs on Linux, Unix, Mac OSX, and Windows. QGIS supports vector, raster, and database formats. QGIS is licensed under the GNU General Public License and supports many common spatial data formats (e.g., ESRI ShapeFile, geotiff). 

FORMAT: Students will take the course at their own pace over a four week period. The course is divided into discrete competency-based modules composed of pre-recorded lecture material and hands-on exercises. Student interaction and instructor feedback will be provided in the form of online discussion forums and live Q&A. Each student will also have the opportunity over a 2-week period to meet with the instructor via video consultation to discuss course materials or their individual datasets.