Schedule

Below is the schedule for GSP 470/570. We'll also be using content from the R For Spatial Stats web site.

Check out this web site with dynamic stats tools

Data sets:

• Example tree data from the FIA database
• BioClim layers
  • CONUS at 1km
  • Subtset of layers for North American at 4km
• Douglas-Fir Count data for California
• Douglas-Fir Presence data for California
• Douglas-Fir Maximum Height in FIA plots for all of California at 8km
• Redwood trees in Humboldt County - I just recreated this data from the FIA database. It should contain counts and other information for redwood trees in FIA plots in Humboldt County.
• Dominant Trees for California
• Ref_Species Table from FIA - includes species codes (spcd) and Common Names

New resources:

• Report rubric
• Modeling Method Summary

1. Introduction to Geospatial Modeling

• Topic: Syllabus review (see Canvas)
• Topic: Introductions
• Optional Reading: Plant, Chapter 1
• Learning Module: Linear Regression
• Note on p-values
• For fun: Spurious Correlations

2. Synthetic Data

• Optional Reading: Plant, Chapter 2: R Programming Environment up to 2.3.2.
  • Note that we will be using R Studio in lab. I highly recommend it over other environments.
• Presentation: Overview of Spatial Modeling
• Discussion: Your Modeling Projects
• Optional Reading: Synthetic data generation — a must-have skill for new data scientists
• Learning Module: Synthetic Data with R

3. Nature of Spatial Data

• Optional Reading: Plant, Chapters 3 and 4 (read once but don't feel like you need to fully understand it all)
• Learning Module: Nature Of Spatial Data

4. Generalized Linear Methods

• Optional Reading: Plant, Chapter 9, all of sections 9.1, 9.2, and 9.4.1
• Learning Module: GLMs
• Presentations:
  • Setting Up For Modeling (intro)
  • Creating Predicted Models
• Presentation: Evaluating Models
• Presentation: How good is the model?
• Presentation: Summary of Regression Modeling

5. Generalized Additive Methods

• Reading: See learning module
• Presentation: Setting up for Modeling
• Learning Module: GAMs
• Reading for Thursday: Jensen, Winter distribution of blue crab Callinectes sapidus in Chesapeake Bay: application and cross-validation of a two-stage generalized additive model
  • Slides for discussion
• Reading (skim): Generalized Additive Models by Hastie and Tibshirani

6. Regression Trees

• Optional Reading: Plant, Chapter 8 (focus on 8.3)
• Converting Data Types - in case you need to convert your data to categorical
• Reading for Thursday: Martínez-Rincón, Comparative performance of generalized additive models and boosted regression trees for statistical modeling of incidental catch of wahoo (Acanthocybium solandri) in the Mexican tuna purse-seine fishery
  • Figures from the reading
• Learning Module: CART

7. Estimating Uncertainty

• Optional Reading: Plant, Chapters 5, 6, and 7 (focus on 5.2.2 and all of 6)
• Presentation: Modeling Process
• Presentation: Regression Summary
• Learning Module: Estimating Uncertainty
• Activity: Statistics without the pain

8. Spring Break

9. Habitat Suitability Modeling

• Learning Module: Habitat Suitability Modeling with HEMI 2
• Tuesday Reading: Visualizing uncertainty in habitat suitability models with the hyper-envelope modeling interface, version 2

10. Habitat Suitability Modeling With MaxEnt

• Learning Module: Habitat Suitability Modeling with MaxEnt
• Readings and discussion: See Canvas

11. Agent-Based Spatially Explicit Models

• Learning Module: Agent Based Spatially Explicit Models
• Reading and discussion: See Canvas

12. Mixed Models

• Topic: Mixed Models
  • Optional Reading (at least scan to see all the equations): Generalized Additive Models with Spatio-temporal Data
  • Optional Reading: The Influence of Environmental Variables on the Presence of White Sharks, Carcharodon carcharias at Two Popular Cape Town Bathing Beaches: A Generalized Additive Mixed Model
  • Optional: Spatial Tutorial
• Reading and discussion:
  • Does hillslope trenching enhance groundwater recharge and baseflow in the Peruvian Andes?
• Lab: Work on projects

13. Other Topics

• Topic: Bayes
  • Reading/Discussion: Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach
  • Interesting web page on diagrams for hierarchical models
  • Optional reading: Frequentism and Bayesianism: A Practical Introduction
• Topic: Autocorrelation and Interpolation
• Topic: Big Rasters
• Topic: HPC
• Lab: Work on projects

14. Going Global

• Presentation & Discussion: See Canvas
• Older optional stuff:
  • Presentation: How Western Science is Leaving Room for Faith in How the University Works
  • Old: What does the future look like?
• Reports: Humboldt Bay SLR Vulnerability and Adaptation Planning
• Maps: A Terrifying, Fascinating Time-lapse of 30 Years of Human Impact on Earth
• Wrap up on Modeling, Discussion on what we can do.
• Reading: How fossil fuels will save the world
• Writing Readable R Code
• Lab: Work on projects

15. Finals Week

Final Exam: See Canvas

Final Projects: See Canvas

Additional Resources

Converting Data Types

Applied Spatial Statistics In R

R Labs for Vegetation Ecologists from MSU

Regression Modeling Steps (incomplete)

RStoolbox: Tools for Remote Sensing Data Analysis

Additional Potential Topics

• Learning Module: Modeling Uncertainty in Models from MaxEnt
• Reading: Mapping sources, sinks, and connectivity using a simulation model of northern spotted owls
• Optional Reading:
  • Revised Recovery Plan for the Northern Spotted Owl
  • Endangered and Threatened Wildlife and Plants; Designation of Revised Critical Habitat for the Northern Spotted Owl
  • MODELING SUPPLEMENT to the Final Rule to Revise Critical Habitat for the Northern Spotted Owl
• Topic: Big Rasters
• Topic: Machine Learning Approaches
• Topic: High-Performance Computers
• Reading: The Fourth Paradigm, Forward and Introduction by Jim Gray
• Reading: A working guide to boosted regression trees
• Lecture: Discussion on The Fourth Paradigm
• Guest Lecture: Heather Lintz - Non parametric Multiplicative Regression
• Guest Lecture: Heather Lintz - Quantifying ecological thresholds from response surfaces

Old Learning Module: Obtaining and Managing Data

Reading: Graduate Student’s Guide to Necessary Skills for Nonacademic Conservation Careers

• Ask yourself, are you prepared for the career you are entering into? If not, what can you do to gain the knowledge, skills, and experience you need?

Old Lab: Synthetic Data and Polynomial Trend Surfaces with Excel and ArcMap