Spring 2016
SYLLABUS and other useful information
Lectures:  TR 2:10  3:30 
Instructor:  Philip Dixon
pdixon at iastate.edu 2121 Snedecor Hall
5152942142

Office Hours:  
Questions::  Please feel free to email pdixon at iastate.edu anytime with questions or comments. 
Text:  Bivand, Pebesma and GomezRubio, 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
Take home exams, 75 pts each: 150 pts Project: 50 pts 
Course Outline :  (proposed) Note: dates not yet updated for 2016 
Week  Dates  Topic  
1  Jan 10, 12  Spatial Data, Sources of randomness, Graphical Spatial Analysis 

2  Jan 17, 19  Statistical Preliminaries, Using R 

36  Jan 24  Feb 16  Geostatistics Variograms, Kriging 

7  Feb 24  Exam 1 due, 5 pm  
78  Feb 21  Mar 1  Areal data, Moran's I  
911  Mar 6  29  Spatial point patterns  
12  Apr 6  Exam 2 due, 5 pm  
1213  Apr 312  Spatial analysis of designed experiments  
14  Apr 1719  Simulation of spatial data  
15  Apr 2426  Project Presentations  
Finals week  May 4, 9:4511:45  Project presentations  
Details:
Student background:  The official prerequisite for Stat 406 is 6 credits in statistics. I will assume you know applied nonspatial 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 graduatelevel grading scheme (mostly A's and B's) but I reserve the right to give lower grades when appropriate. 
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. 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 onepage summary of your proposed project will be due midsemester.
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, 9:4511:45. 
Other
questions: 
Please ask in class or email me: pdixon at iastate.edu 
Disability accommodation: 
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 5152947220. 
Academic honesty policy 
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. 