| name: Henry Scharf | office hours: T/Th 10:00-11:00am or by appt. |
| email: hscharf@sdsu.edu | office: zoom room |
| course number: STAT 596 | meeting times: T/Th 11:00am–12:15pm |
| semester: Spring 2021 | location: zoom room |
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| prerequisites: Experience programming in R. STAT 350B, 551A. | |
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.
Students will be able to:
In-class quizzes (15%): A short quiz will be given during the first 5-10 minutes of each class meeting. The lowest two scores will be dropped. Late quizzes will not be accepted.
Homework (35%): Homework assignments will be given every 2-3 weeks in the form of an Rlab (see course calendar). Each assignment will begin in class (virtually), and students 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 presentation (IRP) (25%): Each student 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 (25%): 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 2021 ENVR Data Challenge in mind. If a group of students commit to preparing a final report suitable for submission to the Data Challenge, they may choose their own group.
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 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 20% 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. 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.
Applied Spatial Data Analysis with R [ASDAR] - Second Edition (2013) by Roger S. Bivand, Edzer J. Pebesma, and Virgilio Gómez-Rubio
Thorough and modern text written by some of the primary developers of spatial statistical packages for R. Not free, sorry.
Spatial Data Science [SDS] by Edzer J. Pebesma and Roger S. Bivand
Free, online textbook which is currently in development and intended to essentially replace ASDAR.
Spatio-Temporal Statistics with R [STSR] by Christopher K. Wikle, Andrew Zammit-Mangion, and Noel Cressie
Free online through book’s website.
Statistics for Spatio-Temporal Data by Noel Cressie and Christopher K. Wikle.
More theoretically advanced version of STSR.
Spatial Data Science with R by Robert J. Hijmans
Good online, free text for discrete/raster type models. Written by the primary developer of the raster 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.
This semester the university has distributed the days normally reserved for spring break into several Rest and Recovery (R&R) days (see Calendar). To ensure that these days provide a genuine opportunity for rest and recovery, no meetings or assignments will take place on those days, and no assignments will be due on days following R&R days.
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.
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 sdsu.edu/ecrt, email ecrt@sdsu.edu, or walk-in to Well-being & Health Promotion on the 3rd floor of Calpulli Center.
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!