EE 508 Data Science for Conservation Decisions
Contents
About this course
Schedule
Getting started
Labs
1. Spatial data processing and visualization
2. Systematic conservation planning with Marxan
3. Optimal policy targeting with predictive machine learning
4. Quasi-experimental impact evaluation with matching
Tests
Project
EE 508 Data Science for Conservation Decisions
Labs
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Labs
Labs will appear here as the term progresses.
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 in the Amazon