Syllabus. Stat 222, Geog m205, UP m215. Spatial Statistics.
Prof. Rick Paik Schoenberg.
Spring 2021.

Lectures: TTh 2pm-315pm. Online via zoom with meeting ID 918 499 4904. The password for week 1 is 142196. It will change in future lectures and I will announce the password in class during week 1. .

Contact instructor by email for the password if needed.

Office Hours: Thu 130-2pm, same website as the lectures.


Course webpage: .

I am not maintaining the CCLE site. See the site above for course information.

Statistics 222 will explore the methods used in the analysis of spatial-temporal data, especially point process data. The course will be both theoretical and applied. Students will learn standard concepts in modeling and parameter estimation, followed by some more recent topics such as point process residual analysis and nonparametric estimation of triggering functions in Hawkes models. Examples will be provided throughout the instruction of the course, and students will implement the techniques discussed in class, using real data sets. Dedicated students should come away with an in-depth understanding of statistical concepts related to spatial-temporal point processes, as well as a thorough comprehension of how and when to implement various techniques in practice.

The course is designed for graduate students in statistics or mathematics, and may also be taken by students from other disciplines provided those students have sufficient mathematical and statistical backgrounds. Some experience in statistical computing with R is recommended.

exam (45%) Thu May 20 2-315pm.
Oral Presentation (10%) -- via zoom, last 2 weeks of class.
Written project (45%) -- due Wed Jun9, 8pm, by email to me.

There will most likely be no extensions for the project or presentation. Students who are unable to make these dates or otherwise fulfill the course requirements must consult with the instructor in advance, if possible.

I am almost always open to questions, but one question I would like you not to ask me is "What did we do in class?" If you miss class, please get caught up from a fellow classmate.

Written Project:
Find a spatial-temporal point process dataset and analyze it using some of the relevant methods described in class. Your report should contain about 4-6 pages of text, followed by as many figures as appropriate. You may include as many figures as you like, but the text itself should not exceed 6 pages, double spaced.

In selecting your dataset, choose something that interests you, and try to choose a dataset where the quantification of clustering or inhibition of the locations of the points would be of interest. Begin your paper with an introduction, a description of your data and how they were obtained, and a summary of the main question(s) to be addressed in your paper. Then summarize your analyses, paying special attention to any assumptions you are making and the plausibility of those assumptions. Conclude by assessing how effective the methods you used were in helping to answer your main question(s).

Oral presentations of project results will take place on the last few days of class. These will involve simply presenting a clear and concise summary of your dataset including a couple of your main results. Do not try to show all the results from your paper in your oral presentation! I will talk more about the presentations as we get closer to the date.