Course Schedule

Course Schedule

Point Referenced Data

Week One: Introduction: Git, R Studio, and Spatial Data Visualization

Weekly Resources:

Virtual Materials:

Class Overview:

  • Monday January 11: Course Overview and Class Conversation

  • Wednesday January 13: Intro to R Studio, Git, and GitHub Classroom. (Interactive Demo)
  • Friday January 15: Intro to Spatial Data Visualization in R with Leaflet (Interactive Demo)

Week Two: Linear Models and Bayesian Inference

Weekly Materials

  • Suggested Reading: Hierarchical Modeling and Analysis for Spatial Data (HMASD), Chapter 5: Basics of Bayesian Inference

  • Weekly Notes: PDF (RMD Source)

Class Overview:

  • Monday January 18: No Class MLK Day

  • Wednesday January 20: Linear Models Overview

  • Friday January 22: Linear Models + Bayes Intro

Week Three: Bayes and Stan

Weekly Materials
Class Overview:

  • Monday January 25: Bayes Theorem + Bayesian overview

  • Wednesday January 27: Visual Overview of Bayesian Analysis

  • Friday January 29: Stan Demo (Interactive Demo) (Stan Demo) (Download Repo)

Week Four: Linear Algebra and Conditional Multivariate Normal

Weekly Materials
Class Overview:

  • Monday February 1: Linear Algebra Recap

  • Wednesday February 3: Mathematical exploration of Multivariate Normal Distribution

  • Friday February 5: Correlated Normal Demo (Interactive Demo) Conditional Normal Demo (Download Repo)

Week Five: Gaussian Processes and GP regression

Weekly Materials
Class Overview:

  • Monday February 8: Theory of Gaussian Processes

  • Wednesday February 10: GP in 2D

  • Friday February 12: GP demo (Interactive Demo) GP Demo (Download Repo)

Week Six: Projections, Distance Calculations, and Spatial Graphics

Weekly Materials
Class Overview:
  • Monday February 15: President’s Day no class

  • Wednesday February 17: Projections and Distance Calculations

  • Friday February 19: Spatial data in R (ggmap reference)

Week Seven: Spatial Statistics Fundamentals

Weekly Materials
Class Overview:
  • Monday February 22: Stationarity and Variograms
    • Project 1: Introduction, Research Question, and Data viz due end of day Monday February 22
  • Wednesday February 24: Fitting variograms and covariance functions
  • Friday February 26: Spatial EDA: Lecture

Week Eight: Fitting Geostatistical Models

Weekly Materials
Class Overview:

Week Nine: Fitting Geostatistical Models, part 2

Weekly Materials
Class Overview:

  • Monday March 8: Anisotropy + other covariance structure

  • Wednesday March 10: Spatial prediction / model comparison
  • Friday March 12: Spatial prediction / model comparison

Areal Data

Week Ten: Spatial GLMS / Areal Data Intro

Weekly Materials
Class Overview:
  • Monday March 15: Spatial GLMS

  • Wednesday March 17: Spatial GLMs interactive demo

  • Friday March 19: Areal Data Intro

Week Eleven: Areal Data: Intro

Weekly Materials
Class Overview:

  • Monday March 22:

  • Wednesday March 24:

  • Friday March 26:

    • Project 1: due (Update existing repo)

Week Twelve: Areal Data Modeling

Weekly Materials
Class Overview:

Week Thirteen: Point Process Data

Weekly Materials
Class Overview:

Week Fourteen:

Weekly Materials
Class Overview:

Week Fifteen:

Weekly Materials
Class Overview:

Week Sixteen:

Class Overview:

Course Description

Statistical methods of spatial data analysis, stationary and nonstationary random fields, covariance structures, geostatistical models and analysis, spatial point process models and analysis, spatial lattice models and analysis. An emphasis will be placed on:

  1. Creating maps and other data visualization products with spatial data,

  2. Identifying differences between the three common spatial data types: point process, geostatistical, and areal data,

  3. Using statistical software and either Bayesian or classical statistical techniques to analyze spatial point process, geostatistical, and areal data structures, and

  4. Implementing version control tools, such as git and github, on spatial data analyses.

Learning Outcomes:

At the end of the course students will understand

  1. point process theory and applications including homogeneous and non-homogeneous Poisson point processes

  2. geostatistics including semivariogram estimation and kriging

  3. spatial autoregression including covariance estimation, spatial logistic and Poisson models, simultaneous autoregressive models, conditional autoregressive models.

Course Syllabus

A downloadable PDF of the course syllabus is available: Download PDF Syllabus

Prerequisites

  • Required: STAT 412, STAT 512, and STAT 422
  • Preferred: STAT 506, extensive experience with R, and an understanding or interest in Bayesian statistics

Textbooks

  • Hierarchical Modeling and Analysis for Spatial Data, Second Edition, by Bannerjee, Carlin, and Gelfand. While the second edition is preferred, the first edition will suffice.
  • Animal Movement: Statistical Models for Telemetry Data, by Hooten, Johnson, McClintock, and Morales. Optional

Additional Resources

Analysis, data visualization, and version control procedures will be implemented with:

  • R / R Studio
  • Git / Github

Course Policies

Grading Policy

  • 50% of your grade will be determined by homework assignments. Collaboration is encouraged on homework assignments, but everyone should complete their own assignments.

  • 25% of your grade will be determined by a midterm project.

  • 25% of your grade will be determined by a final project.

Collaboration

University policy states that, unless otherwise specified, students may not collaborate on graded material. Any exceptions to this policy will be stated explicitly for individual assignments. If you have any questions about the limits of collaboration, you are expected to ask for clarification.

In this class students are encouraged to collaborate on homework assignments, but exams and projects should be completed without collaboration.

Academic Misconduct

Section 420 of the Student Conduct Code describes academic misconduct as including but not limited to plagiarism, cheating, multiple submissions, or facilitating others’ misconduct. Possible sanctions for academic misconduct range from an oral reprimand to expulsion from the university.

Disabilities Policy

Federal law mandates the provision of services at the university-level to qualified students with disabilities. If you have a documented disability for which you are or may be requesting an accommodation(s), you are encouraged to contact the Office of Disability Services as soon as possible.

Masks

WEARING MASKS IN CLASSROOMS IS REQUIRED Face coverings that cover the mouth and nose are required in all indoor spaces and all enclosed or partially enclosed outdoor spaces. MSU requires all students to wear face masks or cloth face coverings in classrooms, laboratories and other similar spaces where in-person instruction occurs. MSU requires the wearing of masks in physical classrooms to help mitigate the transmission of SARS-CoV-2, which causes COVID-19. The MSU community views the adoption of these practices as a mark of good citizenship and respectful care of fellow classmates, faculty, and staff.

The complete details about MSU’s mask requirement can be found at https://www.montana.edu/health/coronavirus/index.html.

These requirements from the Office of the Commissioner of Higher Education are detailed in the MUS Healthy Fall 2020 Guidelines, Appendix B.

For more information: https://www.montana.edu/health/coronavirus/prevention/index.html

Compliance with the face-covering protocol is expected. If a you do not comply with a classroom rule, you may be requested to leave class. Section 460.00 of the MSU Code of Student Conduct covers “disruptive student behavior.”

Please evaluate your own health status regularly and refrain from attending class and other on-campus events if you are ill. MSU students who miss class due to illness will be given opportunities to access course materials online. You are encouraged to seek appropriate medical attention for treatment of illness. In the event of contagious illness, please do not come to class or to campus to turn in work. Instead notify me by email about your absence as soon as practical, so that accommodations can be made. Please note that documentation (a Doctor’s note) for medical excuses is not required. MSU University Health Partners - as part their commitment to maintain patient confidentiality, to encourage more appropriate use of healthcare resources, and to support meaningful dialogue between instructors and students - does not provide such documentation.

Course Communication

In the event that one or more students and/or the instructor are required to quarantine or if the university moves courses online, the course may need to continue in a virtual format. Communication about how the course will proceed will be available through D2l.

Virtual Attendance

Due to the ongoing pandemic and issues stemming from this, a synchronous virtual attendance option will be permitted for this course. The Microsoft Teams platform will be used for this virtual option. When attending virtually, if at all possible, please plan to have your video camera turned on.

Approximate Course Outline

  1. Course Intro & Preliminaries:
    • R
    • Git
    • Plotting spatial data
    • Linear Models
    • Stan / Bayesian Inference
  2. Gaussian Processes in 1D
  3. Point Referenced Data
  4. Areal Data
  5. Point Process Data (and potentially animal movement models)