Spring 2024

Class meets: 8:00 - 9:15am T/TR @ BEL 320A

Dr. Ziqi Li
Assistant Professor in Quantitative Geography
Office: Bellamy 314
Email: [email protected]
Website: https://sites.google.com/view/ziqi-li/home
Office Hours: 2:00PM – 3:00PM, every Wednesday in-person, or by appointment.

In this course, you will learn a wide range of spatial statistical models from parametric to non-parametric, from global to local, for inferential and predictive tasks. You are expected to learn the theories and principles behind these models through simulation experiments and empirical examples. You will practice statistical exercises using Python and open-source packages.

• Understand statistical inference in modelling spatial data
• Explain the principles of machine learning
• Evaluate asusmptions, advantages and disadvantages among different statistical/machine learning methods
• Apply appropraiate statistical/machine learning methods to real-world data
• Use Python and open-source packages for spatial data analysis

We will be using Canvas/Github sites for hosting course materials. Students are expected to check sites regularly for announcements, schedule, lecture slides, materials, grades, assignments, and other postings. Students can use their own programming environment/code editor including (Google Colab, Anaconda, Visual Studio Code, etc.)

No formal requirements on textbooks, but there are a few of useful references:

• Statistical Methods for Geography by Rogerson
• Geographic Data Science by Rey et al.
• GIST Book of Knowledge
• Interpretable Machine Learning by Molnar

I may also occasionally distribute other reading materials.

Your final grade is based on the following components:

and this scheme:

There will be six assignments that will be completed over the duration of this course. Each assignment will be introduced after the lecture is completed. Students will have some time in class to start the assignment and ask any questions, and will be required to work on them after class.

Final projects are individual based. You will be asked to investigate a spatial problem of your choice/interest using appropriate data and methods covered in class. More will be introduced later in the semster.

Assignments/project are due at 11:59 pm on the day indicated on Canvas. Late assignments will have a penalty of 20% for each late day. Late panelty is implemented automatically through Canvas's gradebook.

Assignments will not be accepted more than 5 days past the due date. Exception to this is that you have legitimate and/or religeous reasons. Evidence is required.

This will subject to change:

• W1 - W3 Statistical inference
• W4 - W5 Point pattern analysis
• W5 - W6 Linear regression recap
• W7 Spatial regression/Regimes
• W8 Geographically Weighted Regression/MGWR
• W9 Spring Break
• W10 Multi-level models
• W11 Non-linear models
• W12 - W14 Machine learning
• W15 AAG
• W16 Final Presentation

Students are expected to attend all lectures, and to arrive to class on time. Regular attendance is important because assessments will include material from in-class discussions. Students are responsible for getting missed class materials from classmates. The instructor will not provide (physical) handouts or notes other than slides.

Students are expected to come to class having read the reading assignments, to be attentive and ready to learn, to participate in class discussions with pertinent questions and ideas, and to be respectful of fellow students. Classroom disturbances like personal conversations and ringing cell phones are not welcome.

Cell phone use is prohibited except for classroom use. Laptops and tablet computers may be used for taking notes. If you cannot avoid the temptation of the Internet, please sit in the back of the room so that you do not distract other students. Students in violation of this policy may be asked to leave class.

The instructor decides what effect unexcused absences will have on grades and will explain class attendance and grading policies in writing at the beginning of each semester. Instructors must accommodate absences due to documented illness, deaths in the family and other documented crises, call to active military duty or jury duty, religious holy days, and official University activities and must do so in a way that does not arbitrarily penalize students who have a valid excuse. Official University activities include official events at which the student is representing the University, such as athletic competitions and academic activities sponsored by a student’s academic department or college. Registered Student Organizations (RSO’s) and Greek Life activities are not considered official university activities. The current list of Registered Student Organizations can be found at: https://nolecentral.dsa.fsu.edu/organizations. Consideration should also be given to students whose dependent children experience serious illness. All students are expected to abide by each instructor’s class attendance policy. Students must also provide advance notice of absences (when possible) as well as relevant documentation regarding absences to the instructor as soon as possible following the illness or event that led to an absence. Regardless of whether an absence is excused or unexcused, the student is responsible for making up all work that is missed. ** University-wide policy requires all students to attend the first class meeting of all classes for which they are registered. Students who do not attend the first class meeting of a course for which they are registered will be dropped from the course by the academic department that offers the course. ** In order to enforce this policy, instructors are required to take attendance at the first class meeting and report absences to the appropriate person in their department or school/college. For further information, consult the FSU General Bulletin at: http:https://registrar.fsu.edu/bulletin/undergraduate/. Please note that some colleges and special programs have more stringent requirements for class attendance. Also, see “Medical Excuses” and “Military Short-Term Absence Accommodation Policy” in this chapter.

The Florida State University Academic Honor Policy outlines the University's expectations for the integrity of students' academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process. Students are responsible for reading the Academic Honor Policy and for living up to their pledge to "...be honest and truthful and...[to] strive for personal and institutional integrity at Florida State University." (Florida State University Academic Honor Policy, found at http:https://fda.fsu.edu/Academics/Academic-Honor-Policy)

Students with disabilities needing academic accommodation should:

1. register with and provide documentation to the Student Disability Resource Center; and
2. bring a letter to the instructor indicating the need for accommodation and what type.

Please note that instructors are not allowed to provide classroom accommodation to a student until appropriate verification from the Student Disability Resource Center has been provided.

This syllabus and other class materials are available in alternative format upon request.

For more information about services available to FSU students with disabilities, contact the: Student Disability Resource Center 874 Traditions Way 108 Student Services Building Florida State University Tallahassee, FL 32306-4167 (850) 644-9566 (voice) (850) 644-8504 (TDD) [email protected] http:https://www.disabilitycenter.fsu.edu/