Information in this syllabus is subject to change over the semester.
| name: Henry Scharf (you can call me Henry) | office: ENR2 319 or virtual |
| email: hscharf@arizona.edu | office hours: M/W 9:20am–10:50am or by appt. |
| (please let me know you’re coming) |
| course number: STAT 574E | location: MLT 124 |
| semester: Fall 2024 | meeting times: M/W 11:00am–12:15pm |
| mode of delivery: lecture/lab | platform: D2L, Gradescope, TBA (Discord/Slack/Teams…) |
| prerequisites: MATH/STAT 571A or similar class on regression methods. Experience with R is very helpful. | |
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, ecological, and biological sciences.
Students who succeed in this course will have a collection of 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.
Spatial Linear Models for Environmental Data [SLM] by Dale Zimmerman and Jay M. Ver Hoef
Essentially, this text concerns implementing linear statistical models while accounting for spatial dependence. This will be a major focus for the class. Sadly, not free.
Spatial Point Patterns: Methodology and Applications with R [SPP] by Adrian Baddeley, Ege Rubak, and Rolf Turner
Analysis of point patterns, or applications where the locations of
measurements are themselves a random process. Aimed at Scientists as
much as Statisticians. Written by authors of the spaptstat
R package. Online edition available through the library.
Spatial Statistics for Data Science: Theory and Practice with R [SSDS] by Paula Moraga
Free, online textbook.
Spatial Data Science [SDS] by Edzer J. Pebesma and Roger S. Bivand
Free, online textbook (intended to replace the text Applied Spatial Data Analysis with 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.
Spatio-Temporal Statistics with R [STSR] by Christopher K. Wikle, Andrew Zammit-Mangion, and Noel Cressie
Free online textbook on methods for spatio-temporal data (targeted at a more general audience than Statistics for Spatio-Temporal Data; see below).
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.
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 schedule). 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. Rubrics used to grade the written component and oral presentation will be provided.
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, assignments will not be accepted after the due date.
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.
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 actively try to make assignments/projects relevant for you/your research. 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 they are frequently searching online for more information about something we discussed in class. They may find themselves listening to unassigned podcasts and reading blogs or journal articles 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.” ## Access
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.
“At the University of Arizona, we strive to make learning experiences as accessible as possible. If you anticipate or experience barriers based on disability or pregnancy, please contact the Disability Resource Center (520-621-3268, https://drc.arizona.edu/) to establish reasonable accommodations.”
I am committed to ensuring that students in the online sections for this course have access to all materials, are able to engage with me as an instructor and ask questions, and are able to actively participate in the group project. Please make use of virtual office hours, our class discussion forum, and email for communication.
Students in online sections are expected to take quizzes before viewing recorded lectures. The quizzes are much more valuable for learning this way, and it is the only ethical way to earn grades for the quizzes. While I do not anticipate a need for it, I do record time stamps associated with time-of-access for quizzes and recordings and will adjust grades if there is evidence of unethical behavior.
I encourage you to reach out by email anytime you 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: Please notify me about planned absences for religious observances in advance.
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.
Class rosters are provided to the instructor with students’ legal names. Please let me know if you would prefer an alternate name and/or gender pronoun.
You are expected to attend all in-person class meetings. However, I understand that occasionally timing conflicts may arise for various personal reasons. Whenever possible, please let me know in advance if you will be absent from a class.
Be respectful of your peers and the instructor and comply with the University’s policy on threatening behavior.
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.
Fundamentals of Data Visualization by Claus O. Wilke
There are many other useful references online. If you find a particularly good one you think others would appreciate, please let us know!
We respectfully acknowledge the University of Arizona is on the land and territories of Indigenous peoples. Today, Arizona is home to 22 federally recognized tribes, with Tucson being home to the O’odham and the Yaqui. Committed to diversity and inclusion, the University strives to build sustainable relationships with sovereign Native Nations and Indigenous communities through education offerings, partnerships, and community service.