Section outline

    • Learning Objectives

      Learners will receive detailed instruction on the following:

      • Implementing best practices for data management focused on file management, formatting, storing, and documenting data for use in GIS, specifically:
        • Adopt a consistent file and attribute naming convention and folder structure
        • Understand the importance of maintaining raw and intermediate versions of data files to facilitate traceability of data processing.
        • Start and maintain metadata records for effective and consistent data tracking and accountability (including date captured, data source, version, terms of use, etc.)
      • Understanding the restrictions imposed by GIS when importing tabular data, and consequently being able to identify and correct common errors within table editing software (Excel or other open source alternative)
      • How to import data with GPS locations (latitude and longitude) in a table format into QGIS and plot them on the map canvas
      • Applying methods to identify and correct spatial data errors in QGIS
      • How to determine which spatial data format to adopt when embarking on a project:
        • Overview of available spatial data formats, pros and cons of each
        • Consider the arguments for and against the use of GeoPackage over shapefile
        • Appreciate the benefits of GeoPackage for efficient data storage, stability, and enhanced functionality

      Learning Outcomes

      By the end of the training, learners will attain:

      • An understanding of the vital importance of good data stewardship as the foundation of effective GIS work; how data ultimately determines the quality of analysis
      • A set of data management protocols and procedures, grounded in best practice, to enact when embarking on a new GIS project
      • An understanding of how to critically assess input data, how to troubleshoot and clean data prior to GIS analysis and visulaization
      • An understanding of the importance of enforcing clear data management standards within their organisation and teams, to benefit from greater efficiencies and internal cohesion