| name: Henry Scharf (you can call me Henry) | office: GMCS 518 or virtual |
| email: hscharf@sdsu.edu | office hours: T/Th 2:00-3:00pm or by appt. |
| (please let me know you’re coming) |
| course number: STAT 596 | location: GMCS 325 backup zoom room |
| semester: Spring 2023 | meeting times: T/Th 9:30am–10:45am |
| mode of delivery: lecture/lab | platform: Canvas, Gradescope, Discord (invite) |
| prerequisites: Experience programming in R. STAT 350B and 551A, or graduate standing. | |
For millennia, the Kumeyaay people have been a part of this land. This land has nourished, healed, protected and embraced them for many generations in a relationship of balance and harmony. As members of the San Diego State University community, we acknowledge this legacy. We promote this balance and harmony. We find inspiration from this land, the land of the Kumeyaay.
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.
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.
The structure of this course is designed to support graduate students by training them to conduct research as part of their degrees, and prepare senior undergraduate statistics majors planning to transition to graduate programs. For example, the two major class projects (see below: IRP, Final project) allow for a broad range of topics which graduate students can match to their personal discipline/research interests, potentially even dovetailing with an ongoing thesis. For undergraduate students, the independent and group-based presentations can provide evidence of maturity and preparedness in applications to competitive graduate programs.
Students will be able to:
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. All quizzes will contribute equally.
Homework (45%): Homework assignments will be given every 2-3 weeks in the form of an Rlab (see course schedule). Each assignment will begin in class, and you will have 8 days to submit their solutions online. Unless prior arrangements have been made, late homework assignments will be penalized 25% each day paste the due date. Please contact me at least 48 hours before a due date if you would like to request an extension (or as soon as possible for unexpected emergencies).
Independent research project (IRP) (20%): Each of you will prepare a short presentation in the form of a pre-recorded video or written vignette on a topic of their choosing relevant to spatio-temporal statistics. Presentations should be aimed at an audience of peers (i.e., classmates). Pair-based IRPs may be allowed; please talk to me first.
Final project (20%): 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. Rubrics used to grade the written component and oral presentation will be provided. Groups will be made up of about 4 students chosen by me.
I try to grade assignments as quickly as I can because I think it is most useful for you to receive feedback as soon as possible. Grades will be posted on Canvas and marked assignments will either be available on Canvas or handed back in class. If you have a question about grades or notice an inaccuracy, please let me know.
Letter grade: Students earning final grades in the following ranges will receive the corresponding letter grade or higher (square brackets are inclusive, round parentheses are not).
| Percent | Letter | Grade Point |
|---|---|---|
| [90, 100] | A | 4.0 |
| [80, 90) | B | 3.0 |
| [70, 80) | C | 2.0 |
| [60, 70) | D | 1.0 |
| [0, 60) | F | 0 |
Late Policy: Brief extensions will be granted for assignments when a reasonable request is made at least 48 hours before the due date. If no arrangements have been made in advance, a late penalty of 25% of the total assignment grade per day will be assessed.
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 including:
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.
Spatial Data Science [SDS] by Edzer J. Pebesma and Roger S. Bivand
Free, online textbook (replaces Applied Spatial Data Analysis with R; see below).
Spatio-Temporal Statistics with R [STSR] by Christopher K. Wikle, Andrew Zammit-Mangion, and Noel Cressie
Free online through book’s website.
Applied Spatial Data Analysis with R [ASDAR] - Second Edition (2013) by Roger S. Bivand, Edzer J. Pebesma, and Virgilio Gómez-Rubio
Precursor to Spatial Data Science written by some of the primary developers of spatial statistical packages for R. Not free, sorry.
Statistics for Spatio-Temporal Data by Noel Cressie and Christopher K. Wikle.
More theoretically advanced version of STSR.
Spatial Data
Science with R and terra by Robert J. Hijmans
Good online, free text for discrete/raster type models. Written by
the primary developer of the terra package in
R.
Applied Spatial Statistics for Public Health by Lance Waller and Carol Gotway
Good application-specific text. In my humble opinion, Lance Waller is one of the best statistical writers out there and a super nice guy.
Available here, subject to change.
Motivation to participate in this class needs to come primarily from within. Some of the assessment structures provide minimal external nudging intended to help keep you 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 will try to keep topics as immediately relevant for you as possible. I will try to be responsive to 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 and I will be happy to make a plan with you.
To avoid any delay in the receipt of accommodations arranged through the SASC, you should contact the center as soon as possible. Please note that such accommodations are not retroactive, and that I cannot provide accommodations based upon disability until I have received an accommodation letter from SASC.
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.
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 sdsu.edu/ecrt, email ecrt@sdsu.edu, or walk-in to Well-being & Health Promotion on the 3rd floor of Calpulli Center.
For essential information about student academic success, please see the SDSU Student Academic Success Handbook.
Simple
Features for R: homepage for sf package
Geocomputation with R by Robin Lovelace, Jakub Nowosad, and Jannes Muenchow
The Art of R Programming by Norman Matloff
R for Data Science by Hadley Wickham and Garrett Grolemund. Especially for those who live in, or are curious about, the tinyverse.
There are many other useful references online. If you find a particularly good one you think others would appreciate, please let us know!