STAT 406: Spatial Statistics

Spring 2016

SYLLABUS and other useful information

DRAFT until this notice removed

LecturesTR 2:10 - 3:30
Instructor:  Philip Dixon
pdixon at

2121 Snedecor Hall
Ames IA 50011-1210

on campus: 4-2142

Office Hours:
Questions:: Please feel free to e-mail pdixon at anytime with questions or comments.
Text Bivand, Pebesma and Gomez-Rubio, 2013. Applied Spatial Data Analysis with R, 2nd ed. Springer
1) Understand and appropriately use common methods for analyzing spatial data.
2) Be able to apply these methods to novel problems. 
Grading: Homework assignments, 25 pts each: 100 pts 
Take home exams, 75 pts each: 150 pts 
Project: 50 pts  
Course Outline :(proposed)
Note: dates not yet updated for 2016
Week Dates Topic
Jan 10, 12 Spatial Data, Sources of randomness,
Graphical Spatial Analysis
Jan 17, 19 Statistical Preliminaries,
Using R
3-6 Jan 24 - Feb 16 Geostatistics
Variograms, Kriging
7 Feb 24 Exam 1 due, 5 pm
7-8 Feb 21 - Mar 1 Areal data, Moran's I
9-11 Mar 6 - 29 Spatial point patterns
12 Apr 6 Exam 2 due, 5 pm
12-13 Apr 3-12 Spatial analysis of designed experiments
14 Apr 17-19 Simulation of spatial data
15 Apr 24-26 Project Presentations
Finals week May 4, 9:45-11:45 Project presentations

Student background: The official prerequisite for Stat 406 is 6 credits in statistics. I will assume you know applied non-spatial statistics at the level of Stat 401. Understanding spatial statistics requires some concepts of mathematical statistics (e.g. Stat 341/2 or Stat 447). I will teach what is needed. You will not be required to do any mathematical staistics, but knowing the concepts aids understanding course material.
Grading: Most, but not all, students in this class are grad students. I will use a graduate-level grading scheme (mostly A's and B's) but I reserve the right to give lower grades when appropriate.


Homework assignments will be posted on the web site and announced in class.
Goal is to provide practice using statistical methods to answer interesting/relevant questions. 
Discussion with friends and classmates is strongly encouraged. 
Please write up your answers individually. Copying papers is not a good way to learn and will not be tolerated. 
No late homework accepted.
Solutions will be posted on the class web page soon after the due date.
Computing: Spatial statistics has gone from the impossible to the possible because of modern computing. We will discuss the use of packages in R to analyze data. This is not the only way to analyze spatial data. For example, the ARC/GIS platform has a very good geostatistics module. However, R is the only platform that provides all the analyses we will use. No previous experience in R is expected. We will discuss how to use R. You will be expected to use R for homework and exams. I expect you to ask questions if you don't understand something.
"Lab time"
Exams: My goal is to see whether you can use what you have learned to analyze data. Exams will be take home exams. The exams are open notes and open book. I will give you study descriptions, data, and some questions to answer. You will be expected to use the computer. You are to work individually. but I am very willing to answer questions about code and help you fix computing problems.

Because the exams are takehome, there will be no makeup exams. If you are out of town during an exam week, talk with me about options.

Projects: The project provides a chance to explore a data set or topic that interests you. Potential projects include analyzing data that you have collected, analyzing a class data set in a way not done in class, analyzing data found on the web, or learning more about a topic or extension of a topic.

A one-page summary of your proposed project will be due mid-semester.
At the end of the semester, you will give a 12 minute presentation that describes
if a data analysis: your question and data, your method(s), and your results
if researching a topic: your topic, why it is interesting, and a summary of what you have learned. You will have 3 minutes to answer questions.

Projects will be presented during the last week of class and during the regularly scheduled final exam time. The tentative final exam schedule lists Friday, May 4, 9:45-11:45.
Presentation times will be assigned randomly.
DO NOT buy tickets to leave Ames before the final exam. You are expected to attend all presentations and ask questions. I expect everyone to ask at least one question sometime during the project presentations.
The project grade will be based on your presentation, your attendance at other presentations, and your questions about others presentations.

Please ask in class or e-mail me:  pdixon at 
Iowa State University complies with the Americans with Disabilities Act and Sect 504 of the Rehabilitation Act. If you have a disability and anticipate needing accommodations in this course, please contact Philip Dixon within the first two weeks of the semester. Retroactive requests for accommodations will not be honored. Before meeting with me, you need to obtain a SAAR form with recommendations for accommodations from Student Disability Resources, Room 1076 Student Services Building. Their telephone number is 515-294-7220.
The ISU academic honesty policy is printed in the University catalog and is available online. To clarify how this applies to your work in this class:
On homework assignments: I encourage you to help each other interpret the problems, write code, debug code, and interpret the output. You may share code, but I encourage you to understand that code even if you didn't write it. You are required to write your answers in your own words.
On exams: You are to do all work individually.