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EE 508 | DS 537

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  • Labs
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  • About
  • Schedule
  • Get started
  • Labs
  • Tests
  • Project

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  • 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
  • Labs
  • 2. Systematic conservation planning with Marxan

2. Systematic conservation planning with Marxan#

  • 2.1. Getting to know Marxan: the case of Tasmania
    • 2.1.1. Learn how Marxan works
    • 2.1.2. Examine Marxan’s inputs
    • 2.1.3. Run Marxan from the Terminal
    • 2.1.4. Examine Marxan’s outputs
    • 2.1.5. Map selection frequencies (independent coding)
    • 2.1.6. Wrap up
    • 2.1.7. Appendix
  • 2.2. Building a Marxan analysis from scratch
    • 2.2.1. Prepare data, folder structure, and notebook
    • 2.2.2. Create the planning unit files
    • 2.2.3. Create the species (features) file
    • 2.2.4. Create the planning-unit × species file
    • 2.2.5. Create the boundary file
    • 2.2.6. Check your datasets
    • 2.2.7. Run Marxan
    • 2.2.8. Map selection frequencies
    • 2.2.9. Wrap up
  • 2.3. Improving the plan: cost proxy and planning units
    • 2.3.1. Include a cost proxy
    • 2.3.2. Change the planning unit
    • 2.3.3. Write up an executive summary
    • 2.3.4. Wrap up
    • 2.3.5. Did you like this exercise?

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1.5. Spatial analytics exercise: ecosystem value mapping

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2.1. Getting to know Marxan: the case of Tasmania

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