Applied Spatio-Temporal Statistics

Instructor information

name: Henry Scharf office: GMCS-518
email: office hours: T/Th 10:00-11:00am or by appt.

Course information

course number: STAT 696 semester: Spring 2020
meeting times: T/Th 11:00am–12:15pm room: NE-073

prerequisites: Strong programming skills in R. Coursework in statistics that includes linear modeling (e.g., STAT 610) and probability (e.g., STAT 670A).


“Everything is related to everything else, but near things are more related than distant things.” –Waldo Tobler

This course will focus on applications of spatial, temporal, and spatio-temporal statistical methodology. Emphasis will be on: (i) exploring and visualizing spatio-temporal data, (ii) specifying appropriate statistical models for natural processes in time and space, (iii) assessing and validating statistical models, and (iv) interpreting and communicating analyses of spatio-temporal data. The course will cover a mixture of mathematical properties of spatio-temporal models and implementation using R. The majority of applications used in the course will be drawn from environmental and biological sciences.

Learning outcomes

Students who succeed in this course will have a collection of potentially useful statistical tools at their disposal that they can appropriately apply to a wide variety of problems. Just as important, they will also be able to determine when certain statistical tools are not appropriate. The focus of the course will be on both holistic, general understanding of methodology, as well as specific implementation using the R statistical programming language and relevant packages.

Course Objectives

Students will be able to answer the following questions:

  • What are important differences between common types of spatial and spatio-temporal data? For example, how do fixed-reference data differ from point process data?
  • Why do spatially/temporally referenced data require specialized methods?
  • What sorts of visualizations are useful for understanding spatially/temporally referenced data? How can these be created in R?
  • What does ‘stationary’ mean? What does ‘isotropic’ mean? How do these characteristics impact decisions about how to analyze spatio-temporal data?
  • What are some of the most widely-used statistical models for spatio-temporal data? How can these be implemented in R?
  • What are some ways to evaluate the fit of a spatio-temporal statistical model? What are some ways to compare the analysis associated with different statistical models?


  • In-class quizzes (15%): A very short quiz will be given during the first 5-10 minutes of each class meeting. The lowest two scores will be dropped.

  • Homework (25%): There will be homework assignments. These will typically be continuations of in-class Rlabs (see schedule).

  • Independent research presentation (IRP) (25%): A short presentation/report prepared in pairs about a topic you find particularly interesting. Pair-based IRPs may be allowed; talk to me first.

  • Final project (35%): A presentation + report based on the analyses of a data set of your choosing. This will be a group effort, and your grade will be based on several deliverables building up to the final presentation + report. You may wish to approach the final project with the ENVR Data Challenge in mind. If the group commits to preparing a final report suitable for submission to the Data Challenge, the group may choose its own membership.

I try to grade assignments as quickly as I can, because I think it is most useful for students to receive feedback as soon as possible. Grades will be posted on Blackboard and marked assignments will either be available on Blackboard or handed back in class. If you have a question about grades or notice an inaccuracy, please let me know.

Student privacy: I will not post grades or leave graded assignments in public places. Students will be notified at the time of an assignment if copies of student work will be retained beyond the end of the semester or used as examples for future students or the wider public. Students maintain intellectual property rights to work products they create as part of this course unless they are formally notified otherwise.

Academic Honesty: The University adheres to a strict policy prohibiting cheating and plagiarism. The California State University system requires instructors to report all instances of academic misconduct to the Center for Student Rights and Responsibilities. Academic dishonesty will result in disciplinary review by the University and may lead to probation, suspension, or expulsion.



Course calendar

Available here, subject to change.

Student motivation

Motivation to participate in this class needs to come primarily from within. Some of the assessment structures provide minimal external nudging to keep students going (e.g., quizzes), but for the most part your success will be a product of your own internal desire to actually learn this stuff. For my part as the instructor, this means I have planned zero in-class exams, and will try to keep things as immediately relevant for you, the students, as possible. I will try to be responsive to all your requests throughout the semester. If you find something boring/useless, I’ll try to take it out. If you want me to go into more depth on a particular topic, I’ll try to make time to do that.

For your part as a student, this means you will have to manage your own time carefully, and do whatever you must to make assignments/projects relevant for you. When there is an opportunity, find data sets you care about and want to analyze. Focus on methods you want to be able to take with you throughout your career. A fully engaged student will probably find that she is frequently searching online for more information about something we discussed in class. He may find himself listening to unassigned podcasts and reading blogs written by experts. From time to time, they may bump up against a problem to which the collective response of humanity is “We don’t know how to do that…yet.”


I am committed to ensuring each student’s access to all course materials, time and attention from me as the instructor both in and out of class, and fair opportunities to demonstrate mastery of the course content. Please contact me if you require any special assistance or accommodations. To avoid any delay in the receipt of your accommodations, you should contact Student Ability Success Center as soon as possible. Please note that accommodations are not retroactive, and that I cannot provide accommodations based upon disability until I have received an accommodation letter from Student Ability Success Center.

Communication with instructor

I encourage students to reach out by email anytime they need help or have a question. I endeavor to respond to all emails within 24 hours during the work week. Generally I will not be able to respond during the weekend. For questions that require a longer response than a few sentences, please visit me during office hours or schedule a meeting with me. For questions that can be easily answered through a straightforward search online, you may receive a terse reply inviting you to find the answer on your own. (For example: STUDENT: When are your office hours again? ME: You can figure that out without my help. I believe in you!)

Religious observances: In accordance with the University Policy File, please notify me about planned absences for religious observances by the end of the second week of classes.

Medical-related absences: Please contact me if you need to miss class, etc. due to an illness, injury or emergency. For the purposes of addressing university policy, documentation may be requested.

SDSU Economic Crisis Response Team: If you or a friend are experiencing food or housing insecurity, or any unforeseen financial crisis, visit, email , or walk-in to Well-being & Health Promotion on the 3rd floor of Calpulli Center.

Additional Resources