Lectures:	TR 2:10 - 3:30, Snedecor 3121
Instructor:	Philip Dixon, pdixon at iastate dot edu 2121 Snedecor Hall Ames IA 50011-1210 515-294-2142 on campus: 4-2142
Office Hours:	Dr. Dixon: Tuesday 4-5 pm, 2121 Snedecor
Questions::	Please feel free to e-mail me pdixon at iastate dot edu anytime with questions or comments.
Text:	Bivand, Pebesma and Gomez-Rubio, 2013. Applied Spatial Data Analysis with R, 2nd ed. Springer
Goals:	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  Two take-home midterm exams, 75 pts each: 150 pts  Project: 50 pts
Course Outline :	(subject to change)

Week	Dates	Topic
1	Jan 14, 16	Spatial data
2	Jan 21, 23	Locations and Distances Visualizing spatial data
3	Jan 28, 30	Spatial sampling
4-7	Feb 4 - Feb 27	Spatial prediction Geostatistics: Variograms, Kriging, Cokriging Sampling to estimate the variogram
7	Feb 27	Exam 1 due, 5 pm
8-9	Mar 3 - 12	Areal data, Moran's I
10-11	Mar 24 - Apr 2	Spatial point patterns
12	Apr 9	Exam 2 due, 5 pm
12	Apr 7-9	Space-time data
13-14	Apr 14-23	Topics determined by class interest. Potential topics: Spatial analysis of designed experiments Simulation of spatial data Multi-type point patterns Other topics. Choice depends on class interests
15	Apr 28-30	Catchup or Project Presentations
Finals week	Assigned final time, TBD	No final exam. Project presentations (potentially)

Details:

Student background:	The official prerequisite for Stat 406 is 6 credits in statistics. I assume you know applied non-spatial statistics at the level of Stat 301/587. Understanding spatial statistics requires some concepts of mathematical statistics (e.g. Stat 341/2 or Stat 588). I will review/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. I will expect you to take responsibility for your learning. In particular, that means asking questions about anything you don't understand.
Computing:	Spatial statistics has become practical because of modern computing. We will discuss the use of packages in R to analyze spatial 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 required, but you will be expected to use R for homework and exams. If you have never used R, you will have to spend some extra time becoming familiar with it. I will hold an introduction to R session for those with no prior experience. Part of lecture time will be detailed discussion on using many of the R spatial analysis packages. I am always happy to help with R, especially debugging code. I expect you to ask questions if you don't understand something.
"Lab time"	There is no separate lab period for this course. Computing is a huge part of spatial statistics. Class meets in the upstairs computer room in Snedecor (3121). Most days, especially at the beginning of the semester, I will lecture there. I trust you to not be distracted by the computer in front of you. Some days, I will hold "lab time", where I discuss computing and have you work through exercises. I will circulate and answer questions. You are welcome to bring and use your own laptop.
Homework:	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.  Write up your answers yourself. Copying papers is not a good way to learn and will not be tolerated.  Unless you get prior approval to turn in late homework, it will be penalized 2 points per day late and not accepted after solutions are posted. Solutions will be posted on the class web page soon after I get the last HW submission.
Exams:	My goal is to see whether you can use what you have learned to analyze data. Exams will be take home, 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 must work individually, on all aspects of the exam (deciding what method to use, coding the analysis, interpreting the results, and writing up your answers). You are allowed to find information on on-line forums, but you are not allowed to post a question to a forum or discussion group. I am very willing to answer questions about code and help you fix computing problems. I will answer other questions to the extent possible. If in doubt, ask me. Don't ever ask a friend. I have sent students to the Judicial Administrator to discuss suspected academic dishonesty. I don't like to do that. Please honor the class rules and save us all from embarrassment and trouble. 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 15 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. Your presentation will be followed by a 5 minutes question and answer session. I expect everyone to be engaged with the project presentations and ask questions. Part of your poject grade will be based on the number and quality of questions you ask. Projects will be presented during the last week of class and if necessary during the regularly scheduled final exam time. The tentative final exam schedule lists Tuesday, May 5, 2:15 - 4:15 pm. Presentation times will be assigned randomly. DO NOT buy tickets to leave Ames before the final exam without talking with me first. If we have project presentations during finals week, you will be expected to attend the final. The project grade will be based on your presentation, your attendance at other presentations, and your questions about other's presentations.
Other questions:	Please ask in class or e-mail me:  pdixon at iastate.edu
Syllabus Statements	Information on disability accommodations, academic honesty, dead week, religious accommodations and other university informationa is here. To clarify how the academic honesty policy applies in this class On homework: I encourage you to work together, including sharing code and output. I require that you write up your own answers. On exams: I require that you work individually. Ask me if you have any questions or need help with computing. Even if a classmate is working next to you or in the same room, ask me, not them.
Contact Information:	If you are experiencing, or have experienced, a problem with any of the above issues, email academicissues@iastate.edu.