1. Spatial data processing and visualization

• 1.1. Countries and threatened species with QGIS
  • 1.1.1. Get the vector and raster data
  • 1.1.2. Visualize the data
  • 1.1.3. Add basemaps
  • 1.1.4. Calculate species richness stats by subdivision and species class
  • 1.1.5. Calculate average threatened species richness across all classes
  • 1.1.6. Make a map
  • 1.1.7. Reflect
  • 1.1.8. Wrap up
• 1.2. Prior knowledge and interests of EE 508 students: pandas
  • 1.2.1. Get the survey data
  • 1.2.2. Create a new notebook
  • 1.2.3. Import the survey data
  • 1.2.4. Assign an index
  • 1.2.5. Explore the survey data
  • 1.2.6. Visualize the data
  • 1.2.7. Create and visualize new indicators
  • 1.2.8. Save a figure
  • 1.2.9. Create the deliverables
  • 1.2.10. Independent coding challenge
  • 1.2.11. Wrap up
• 1.3. Countries and threatened species with geopandas
  • 1.3.1. Create a new notebook
  • 1.3.2. Load and explore the vector data
  • 1.3.3. Index and filter
  • 1.3.4. Create a map of country-level population density
  • 1.3.5. Make a map of average threatened species richness by subdivision
  • 1.3.6. Find the countries with the most biodiversity-rich subdivisions
  • 1.3.7. Wrap up
• 1.4. Species priorities in Colombia: rasterio, numpy, and GDAL
  • 1.4.1. Create a new notebook
  • 1.4.2. Get the species distribution data
  • 1.4.3. Explore the raster data
  • 1.4.4. Visualize the raster data
  • 1.4.5. Assessing memory usage
  • 1.4.6. Coarsen rasters with GDAL
  • 1.4.7. Create maps of weighted species richness
  • 1.4.8. Wrap up
• 1.5. Spatial analytics exercise: ecosystem value mapping
  • 1.5.1. Option 1: how much forest carbon was lost in the Colombian Amazon 2000-2024?
  • 1.5.2. Option 2: how do species distribution models affect conservation priorities?
  • 1.5.3. Option 3: help the national cadaster agency of Guatemala (policy brief)