Course Syllabus

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Contact information

This table includes information about how to contact your instructor and other important details about your class

Instructor Email: neeson@ou.edu 

 

Live Session: Mondays 7-8 PM, Central Time

Office Hours: Mondays 8-9 PM, Central Time

 

Zoom link for Tom's office hours and live session: https://oklahoma.zoom.us/j/92903812228?pwd=U09kTU9UaGNGYXhSR3hlQmh0ekxvQT09

Meeting ID: 929 0381 2228

Password: 90293253

 

 

Dr. Tom Neeson

Associate Professor

Atmospheric and Geographic Sciences

 

Course details

Course description

Spatial statistics concerns the analysis of data with spatial structure, which arise in many fields: public health, ecology, meteorology, mining, and economics among others. This course covers visualization and statistical methods for spatial data and important considerations for analyzing these data sets. Lectures and problems sets will cover both theoretical explanations and derivations and analysis of real-world data sets. We will make extensive use of the open-source statistical software R.

Course prerequisites 

Students should have introductory statistics and introductory computer programming. Familiarity with R is beneficial but no prior experience is assumed.

Course materials

Textbook

The primary textbook for this course is:

  • O’Sullivan and Unwin (2010) Geographic Information Analysis. Wiley. OU Library Access

Students may also wish to consult these other textbooks, two of which are available online via OU libraries:

  • Brunsdon and Comber (2015) An Introduction to R for Spatial Analysis and Mapping. Sage.  
  • Dalgaard (2008) Introductory Statistics with R. Springer. OU library Access
  • Bivand, Pebesma, and Gomez-Rubio (2013) Applied Spatial Data Analysis with R. Springer. OU library Access

How to be successful in this course

I’ve taught this course since 2015. Along the way, I’ve had an opportunity to observe many successful students and the strategies they use to master the material. Based on my observations, here are a few suggestions about how to succeed in this course:

  • First, please attend office hours if you have questions. The TA and I will be happy to answer any questions you have or to review material with you.
  • Second, please start early on the problem sets. The TA and I are available to help during office hours and during live sessions, but these sessions are most useful if you come prepared with questions for us. If you can, please start the problem sets before office hours so that you can ask questions when we meet.
  • Finally, please participate in the online discussion forum. If you have a question, chances are that other students have the same question. Interactions with your fellow students are an important part of learning together.

Grades

Breakdown

Course activities and grades listed for each activity
Activity Grading description and percentage Points
Weekly problem sets 15 problem sets, 10 points each 150 
Total      150


Grading scale

Final grades will be assigned using a traditional grading scheme of A is 100 - 90%, B is 89 - 80%, C is 79 - 70%, D is 69 - 60%, and F is 60% and below. 
Course components

Weekly problem sets

There will be fifteen problem sets assigned throughout the semester. You are encouraged to work in groups but must write your own answers, ensuring that the R code and computer output you submit is your own. Students submitting identical computer code and assignment reports will both receive a zero. Problem sets will occasionally have additional questions.

 

Course policies

Communication

Students are encouraged to email me (neeson@ou.edu) with any questions about the course content or assessment. My policy is to respond within 24 hours during weekdays. 

The teaching assistant will aim to grade all problem set assignments within 7 days. Grades will be posted promptly.

Late policy

Late assignments will be marked down 5% per day past the due date.

University Academic Policies and Student Support

Course Summary:

Course Summary
Date Details Due