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PM 599_Spring2012

PM 599_Spring2012 - Spatial Statistics Course Syllabus...

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Spatial Statistics Course Syllabus Course : PM 599 Room : SSB 301 Time : Mondays 1-4pm, Spring 2012 Instructor : Meredith Franklin Office Hours : By appointment Office Location : SSB 202A (Keck) AHF B57E (College) Email : [email protected] Course Scope and Purpose This course is intended as an introduction to spatial statistics, and aims to provide students with the background necessary to investigate geographically represented data. There are a large number of problems involving spatial data, but focus will be placed on methods that are relevant in the fields of public health, environmental science and social science. Lectures will cover the three main areas of spatial statistics: geostatistical data, lattice (areal) data and point patterns. Learning Outcomes Upon completion of this course, students should be able to: Distinguish different types of spatial data (geostatistical, areal, point process) and understand how spatial autocorrelation plays a role in statistical modeling. Use existing methods to investigate spatial autocorrelation in example datasets provided as exercises. Determine which spatial methods to use to in their own research and implement them using statistical software and GIS. Read and discuss new methods in the spatial statistics literature based on an understanding of the basic spatial statistics approaches, principles and main assumptions. Course Requirements Prerequisites Students should have a background in statistics at the level of regression as well as in statistical computing. The course will involve a great deal of computing, primarily in R. Students without the computing prerequisite, and without other experience in R, SAS or Matlab, may be allowed to take the course should be aware that they will need to become familiar with R on their own. It is also strongly encouraged that students have knowledge
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