Labs

Four journeys into high-dimensional data and methods to support complex spatial conservation decisions.

• 1. Spatial data processing and visualization
  • 1.1. Countries and threatened species with QGIS
  • 1.2. Prior knowledge and interests of EE 508 students: pandas
  • 1.3. Countries and threatened species with geopandas
  • 1.4. Species priorities in Colombia: rasterio, numpy, and GDAL
  • 1.5. Spatial analytics exercise: ecosystem value mapping
• 2. Systematic conservation planning with Marxan
  • 2.1. Getting to know Marxan: the case of Tasmania
  • 2.2. Building a Marxan analysis from scratch
  • 2.3. Improving the plan: cost proxy and planning units
• 3. Optimal policy targeting with predictive machine learning
  • 3.1. Predicting land acquisition cost and forest change across Massachusetts
  • 3.2. Optimality of policy targeting: simulating incentives to avoid carbon loss
• 4. Quasi-experimental impact evaluation with matching
  • 4.1. Estimating the effects of protected areas on Amazon deforestation