In the second week of the course we will concern ourselves with spatial patterns in biodiversity: where do species
occur? What are the local conditions there? Can we infer species’ preferences for particular areas? In the morning
we will have plenary sessions where we will review the theory and current practices, illustrated with several case
studies. In the afternoon we will do hands-on work to develop our own case of geospatial biodiversity analysis: we
will collect (and clean) our own occurrence data, import it for visualization, prepare it further, then analyze it
by species distribution modelling. We will present our findings in a brief report.
Day 1 - Introduction
Day 3 - (Spatial) Data management
- Lecture I: Basic database management (e.g. records, variables, types, querying, geodatabases)
- Lecture II: Data conversions and transformations (e.g. vector <=> raster, reclassification, (re)projections)
- Cool website for comparing country sizes in reality, thanks Richard Frische
- Lecture III: Data and file versioning (e.g. version management, backup, history, provenance) - Rutger Vos
- Practical: Analysing your data in ArcGIS: Data preparation with use of spatial tools for SDM 2 pers.
Day 4 - (Spatial) Data analysis
- Lecture I: Methods for spatial data analysis (e.g. overlay, reclassification, spatial join, clip, neighbourhood analysis)
- Lecture II: Automated spatial data analysis (e.g. flowcharts, model builder, python scripting)
- Lecture III: Other tools, for spatial data (e.g. ArcGIS extensions, QGIS, DIVA-GIS, R, MAXENT)
- Guest lecture: Species Distribution Modelling using Deep Learning - Mark Rademaker