This course is designed as a graduate level class in a workshop format to give students a theoretical framework, practical experience, expert knowledge, and statistical tools for analyzing spatiotemporal datasets. Topics include basic matrix algebra and statistics, exploratory data analysis, field correlation and regression analysis, autocorrelation and its statistical consequences in time and space, parametric and non-parametric significance testing and error analysis, empirical orthogonal functions including rotation, singular spectrum analysis, maximum covariance and canonical correspondence analysis, and traditional and multitaper spectral analysis. The course encompasses instruction and training in Python and in the use and manipulation of large multi-dimensional datasets. The major outcome for the class for each student will be a new and independent analysis of a substantial space-time dataset, a formal manuscript describing the motivation, methods, and results of this analysis, and a professional oral presentation. Students are encouraged to bring with them or seek out data relevant to their research to use for their final project. Ideally, students’ final projects will provide the material for a thesis chapter and/or peer-reviewed article.
This syllabus and course schedule are subject to change. Please check regularly for updated information on D2L and here on Github.
Tuesday and Thursday, 12:30PM to 1:45PM
ENR2, Room S547
Course materials online via D2L (http://d2l.arizona.edu)
Dr. Kevin Anchukaitis
Professor, School of Geography, Development, and Environment
Laboratory of Tree Ring Research
Room S514, Environment and Natural Resources Building 2 (ENR2)
Room 419, Bannister Tree Ring Lab Building
Email: [email protected]
Prerequisites
Continuing graduate student status in a degree program at the University of Arizona or permission from instructor. Some prior mathematics or statistical courses are encouraged. Prior programming experience encouraged but not required.
Course Objectives
This course has the following objectives:
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Factual: You will acquire fundamental knowledge of mathematical and statistical methods for the analysis of space-time data, including the basics of linear algebra and matrix operations. You will become familiar with the terminology used to described space-time data and the statistical procedures and outcomes applied in spatiotemporal data analysis. You will be able to locate and acquire appropriate data.
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Conceptual: You will develop an understanding of the available spatiotemporal statistical tools and when to best or appropriately apply them to exploratory data analysis, hypothesis testing, and data reduction and regularization. You will cultivate a first-order understanding of the motivations, advantages, and disadvantages for different procedures and how uncertainties in the underlying data and methods potentially propagate through your analyses. You will be able to identify sources of both signal and noise in your data and analyses.
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Procedural: You will learn how to apply specific methodologies to the analysis of spatiotemporal data, including the practical, hands-on procedures for managing data and implementing these methods and approaches in a high level programming language (Python). You will be able to differentiate between the relative magnitudes and significance of effects or processes and recognize and remove errors associated with data or the implementation of your procedures (‘debugging’).
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Metacognitive: You will recognize the potential and limitation of statistical and data analytical methods with respect to the constraints from the underlying physical, deterministic processes you seek to explore. You will be able to identify reasonable (and unreasonable) inferences or conclusions from your analyses. You will develop an enhanced recognition of how potential biases – including both methodological as well as cognitive – enter into statistical analyses of both deterministic and stochastic systems and inference based on these results. You will practice deploying both quantitative analyses and qualitative reasoning using your methodological skills and systems-based knowledge. You will recognize how the mathematical basis of the methods used may limit the utility of these for some data types or dynamic systems.
Learning Outcomes
By the end of the semester, students will be able to design and conduct a complete statistical analysis of a spatiotemporal dataset. The students will be able to build and test their own analytical programs using a high-level computer language and apply their code to the quantitative analysis of a dataset relevant to their own research. Students will be able to make abductive inferences about the physical or social system they are studying by applying their analysis and their understanding of the magnitude and sources of uncertainty. Students will be able to describe, support, and defend their inferences in a public presentation to the class.
Required Texts or Readings
There is no required textbook for this course. Required readings will be posted on D2L.
Precedent and Acknowledgement
This course and its structure and content was inspired and influenced by spatiotemporal data and objective analysis classes taught by Mike Evans (University of Maryland) and John Chiang (Berkeley).
Assignments and Methods of Assessment
Prework: [20%] These assignments will consist of readings and guided responses and will provide the theoretical or practical motivation or background for topics studied in this class. The assignments (including necessary readings) will be available a week or more prior to their due date, which will be indicated on the Course Schedule. These are individual assignments and students must prepare and submit to D2L an individual (non-collaborative) document. These assignments will be graded by the instructor. A rubric will be provided with each assignment.
Homework: [20%] These assignments will include mathematical, statistical, and programming problems intended to provide practical and hands-on learning in Python, and to prepare students to conduct their own analyses of their own chosen dataset. Assignments will be posted one week (or more) before they are due. In most cases classroom workshop time will be available for students to query the instructor. These are individual assignments and students must prepare and submit to D2L an individual (non-collaborative) document, although students are encouraged to discuss programming challenges they encounter. These assignments will be self-graded using the D2L online quiz system and a suggested solution set provided by the instructor.
Final Project (Paper): [50%] This assignment will be a manuscript, appropriately formatted for a significant peer-reviewed journal in the student’s field of interest. Specific length and content (figures and tables) depend both on the analysis, the data, the hypothesis and research question, and the standard of the target journal and scientific field, but should reflect a substantial body of work, reflect the standards of the field and journal, be free of errors, and be of appropriate quality and significance that the manuscript could reasonably be finalized for actual submission. This is an individual assignment and students must prepare and submit to D2L an individual (non-collaborative) manuscript. This assignment will be graded by the instructor. A rubric will be provided no later than Friday, November 19th. The assignment is due on D2L no later than Wednesday, December 13th at 5:00pm Arizona time. Students planning to attend AGU that week are encouraged to turn their assignment in early.
Final Project (Presentation): [10%] This assignment is a 15 minute (12 minute talk, 3 minutes for questions) professional talk describing the motivations and findings of the students’ paper and manuscript. The presentation schedule will be determined and a rubric made available no later than Friday, November 17th. Students are expected to prepare and give a talk reflecting the standards and practices of their field and to run within the given time window (15 minutes). This is an individual assignment. This assignment will be graded by the instructor with input from the student’s peers. Attendance is required for all students for the student presentation days (November 28th, November 30th, and December 5th).
Grade polices and Letter Grade Distribution:
University policies regarding grades and grading systems are available
at: https://catalog.arizona.edu/policy/grades-and-grading-system
Grade distribution for this course:
A: 90% and above
B: 80% to 89%
C: 70% to 79%
D: 65% to 69%
E: below 65%
Requests for incomplete (I) or withdrawal (W) must be made in accordance with University policies, which are available at https://catalog.arizona.edu/policy/grades-and-grading-system#incomplete and https://catalog.arizona.edu/policy/grades-and-grading-system#Withdrawal respectively. Please be aware of deadlines for requesting these grades. Requests for reconsideration of a grade received on a paper, project, or exam must be made to the instructor no later than 1 week after the assignment is made available to be returned to the student.
There is no final exam for this course.
Late work Assignments that are not completed or handed in on time, without prior arrangement with the instructor, will receive no more than 50% of the assigned points. Assignments not completed within 1 week of the original deadline, without prior arrangement with the instructor, receive no points for the assignment.
Course Communications All communications concerning class are via official UA email addresses. It is the student’s responsibility to regularly check for email communications concerning class information and policies, and to contact the instructor from the student’s official UA email address.
Absence and Class Participation Policy
The UA’s policy concerning Class Attendance, Participation, and
Administrative Drops is available at
https://catalog.arizona.edu/policy/class-attendance-and-participation.
The UA policy regarding absences for any sincerely held religious
belief, observance or practice will be accommodated where reasonable:
http://policy.arizona.edu/human-resources/religious-accommodation-policy.
Absences pre-approved by the UA Dean of Students (or the dean’s
designee) will be honored. Active participation in the course is vital to the learning process. As
such, attendance is strongly encouraged at all meetings of the class.
Life challenges If you are experiencing unexpected barriers to your success in your courses, please note the Dean of Students Office is a central support resource for all students and may be helpful. The Dean of Students Office can be reached at (520) 621-7057 or [email protected].
Physical and mental-health challenges If you are facing physical or mental health challenges this semester, please note that Campus Health provides quality medical and mental health care. For medical appointments, call (520) 621-9202. For After Hours care, call (520) 570-7898. For the Counseling & Psych Services (CAPS) 24/7 hotline, call (520) 621-3334.
Assignment and Grading Policy for Students Who Register Late
Students who register late for the course will be required to complete
all assignments. Due dates for assignments given prior to the student
adding the course will be agreed upon my both student and the
instructor.
Classroom Behavior Policy
To foster a positive learning environment, students and instructors have
a shared responsibility. We want a safe, welcoming, and inclusive
environment where all of us feel comfortable with each other and where
we can challenge ourselves to succeed. To that end, our focus is on the
tasks at hand and not on extraneous activities (e.g., texting, chatting,
reading a newspaper, making phone calls, web surfing, etc.). Students
observed engaging in disruptive activity will be asked to cease this
behavior. Those who continue to disrupt the class will be asked to leave
lecture or discussion and may be reported to the Dean of Students.
Threatening Behavior Policy
The UA Threatening Behavior by Students Policy prohibits threats of
physical harm to any member of the University community, including to
oneself. See http://policy.arizona.edu/education-and-student-affairs/threatening-behavior-students.
Accessibility and Accommodations
Our goal in this classroom is that learning experiences be as accessible
as possible. If you anticipate or experience physical or academic
barriers based on disability, please let me know immediately so that we
can discuss options. You are also welcome to contact the Disability
Resource Center (520-621-3268) to establish reasonable accommodations.
For additional information on the Disability Resource Center and
reasonable accommodations, please visit http://drc.arizona.edu. If you
have reasonable accommodations, please plan to meet with me by
appointment or during office hours to discuss accommodations and how my
course requirements and activities may impact your ability to fully
participate.
Code of Academic Integrity
Students are encouraged to share intellectual views and discuss freely
the principles and applications of course materials. However, graded
work/exercises must be the product of independent effort unless
otherwise instructed. Students are expected to adhere to the UA Code of
Academic Integrity as described in the UA General Catalog. See
https://deanofstudents.arizona.edu/student-rights-responsibilities/academic-integrity.
The University Libraries have some excellent tips for avoiding plagiarism, available at: https://lib.arizona.edu/research/citing/plagiarism.
Selling class notes, lectures, assignments, or other course materials to other students or to a third party for resale is not permitted without the instructor's express written consent. Violations to this and other course rules are subject to the Code of Academic Integrity and may result in course sanctions. Additionally, students who use D2L or UA e-mail to sell or buy these copyrighted materials are subject to Code of Conduct Violations for misuse of student e-mail addresses. This conduct may also constitute copyright infringement.
UA Nondiscrimination and Anti-harassment Policy
The University is committed to creating and maintaining an environment
free of discrimination; see http://policy.arizona.edu/human-resources/nondiscrimination-and-anti-harassment-policy.
Our classroom is a place where everyone is encouraged to express
well-formed opinions and their reasons for those opinions. We also want
to create a tolerant and open environment where such opinions can be
expressed without resorting to bullying or discrimination of others.
Additional Resources for Students
UA Academic policies and procedures are available at:
http://catalog.arizona.edu/policies.
Student Assistance and Advocacy information is available at:
https://deanofstudents.arizona.edu/support/student-assistance
Confidentiality of Student Records
Please see the University’s policy on the confidentiality of student
records here: https://www.registrar.arizona.edu/privacy-ferpa/about-ferpa
Subject to Change Statement
Information contained in the course syllabus, other than the grade and
absence policy, may be subject to change with advance notice, as deemed
appropriate by the instructor.
| Date | Content and Assignments |
|---|---|
| Tuesday, August 22 | Course introduction and overview, class survey Philosophy, statistics, inference, programming, and Python. Setting up your system for Python |
| Thursday, August 24 | Spatiotemporal Data Introduction to Python; Data handling, formats, sources, and large datasets |
| Tuesday, August 29 | Spatiotemporal data as matrices Introduction to matrix algebra & statistics Matrices and Linear Algebra in Python Pre-Work #1 due at start of class (Data and uncertainty) |
| Thursday, August 31 | Matrix algebra & statistics in Python, continued Variance and covariance Homework #1 assigned (Covariance and Correlation) |
| Tuesday, September 5 | No Class - Kevin at Packard Foundation in Colorado Springs |
| Thursday, September 7 | No Class - Kevin at Packard Foundation in Colorado Springs Prework #2 due on D2L (Linear Algebra) |
| Tuesday, September 12 | Covariance and Correlation Matrices Toward Empirical Orthogonal Functions Working with multidimensional data in |
| Thursday, September 14 | Homework #1 due at start of class Introduction to Empirical Orthogonal Functions Homework #2 assigned (Empirical Orthogonal Functions I) |
| Tuesday, September 19 | Workshop and code critique |
| Thursday, September 21 | Empirical orthogonal functions continued Homework #3 assigned (Empirical Orthogonal Functions II) |
| Tuesday, September 26 | Homework #2 due at start of class Workshop - Missing data and random data |
| Thursday, September 28 | Empirical orthogonal functions, Significance Testing, and Noise Prework #3 due at start of class (Selection, Significance, Meaning) Homework #3 due at start of class |
| Tuesday, October 3 | Empirical orthogonal functions (rotation) Prework #4 due at start of class (Orthogonal Rotation and Interpretation) EOF Rotation Workshop |
| Thursday, October 5 | No class |
| Tuesday, October 10 | Empirical orthogonal functions and compositing Prework #5 due at start of class (EOF interpretation) Homework #4 assigned (Significance and Compositing) |
| Thursday, October 12 | No class - Kevin at University of Minnesota |
| Tuesday, October 17 | Maximum Covariance Analysis (MCA) Prework #6 due at start of class (coupled fields) |
| Thursday, October 19 | Maximum Covariance Analysis (MCA) Workshop Homework #4 due at start of class Homework #5 assigned (Coupled patterns and Field Significance) |
| Tuesday, October 24 | Spatial correlation and regression Field significance and spurious relationships Prework #7 due at start of class (Field significance) |
| Thursday, October 26 | Field correlation and significance workshop |
| Tuesday, October 31 | Prework #8 due at start of class (Spectral analysis) Introduction to Spectral Analysis |
| Thursday, November 2 | Homework #5 due at start of class Spectral analysis, continued Singular Spectrum Analysis and temporal autocovariance Homework #6 assigned (Singular spectrum analysis) |
| Tuesday, November 7 | Spectral Analysis workshop and code critique |
| Thursday, November 9 | A coherent framework for spatiotemporal data analysis |
| Tuesday, November 14 | Student Project Work Homework #6 due by the start of class |
| Thursday, November 16 | Student Project Work |
| Tuesday, November 21 | No class, Thanksgiving Break. |
| Thursday, November 23 | No class, Thanksgiving Break. |
| Tuesday, November 28 | Student Presentations |
| Thursday, November 30 | Student Presentations |
| Tuesday, December 5 | Student Presentations |
Final Paper
The final paper will be due no later than Wednesday, December 13th by
5pm. Students attending the AGU are encouraged to turn their papers
in before the beginning of the meeting!
Workshop topics
Depending on the interests of the students and the type of data analysis
questions posed during the course, additional optional instruction may
be designed for workshop days