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SIE 512 - Overview

Spatial Analysis

Kate Beard
Room 326 Boardman Hall
beard@spatial.maine.edu

Course Objectives

This course introduces techniques for the statistical analysis of spatial data. Topics include characterization of spatial data, and techniques for visualizing, exploring and modeling spatial data distributed as point patterns, continuous data, area data, and methods and problems in spatial data sampling. Students will become familiar with methods for identifying, describing, modeling, and testing spatial patterns in observed data.

A. Class Sessions

  • On-campus Students: Tues and Thursday, 2:00 – 3:15 Tues & Thurs, Room 326 Boardman Hall
  • Live Broadcast: Available at http://connect.maine.edu/sie512/ Online students may view and participate in the live sessions but are not required to do so.
  • Archived Broadcasts: Links to the class broadcasts are made available at the end of each day through the Lectures and Assignments link for this course.
  • End of Week Live Audio Chat: Distance students can view the lectures at times of their own choosing during the week.  A  live discussion session can be arranged to discuss questions or lab issues. The audio technology used for these sessions is through  the Voice Over Internet Protocol of ConnectPro and/or through use of a Skype Conference call.

B. Course Materials

Students will be responsible for completing several lab exercises, one paper, a midterm exam and a final project.  Prerequisites:  an introductory statistics course.

Text:

  • Bailey, T. C.  and A. C. Gatrell. 1995. Interactive Spatial Data Analysis. Longmans Scientific and Technical

Supplementary  Readings:

Additional references:

  • Cressie, N. 1993. Statistics for Spatial Data. Revised ed. John Wiley & Sons, New York.
  • Diggle, P. Statistical Analysis of Spatial Point Patterns. London: Academic Press.
  • Fotheringham, S. Brunsdon, C. Charlton, M. 2000. Quantitative Geography: Perspectives on Spatial Data Analysis. Sage Publications: London.
  • Goovaerts, P. Geostatistics for Natural Resource Evaluation. Oxford University Press.
  • Isaaks, E., and R. Srivastava. 1989. An Introduction to Applied Geostatistics. Oxford University Press, New York.

Lab exercises:

Most lab exercises will be done using R an open source statistical software. R is easy to install and can be downloaded from the  CRAN webiste. R is also installed on the computers in the SIE Lab – Boardman 138.

We will also use  GeoDa, open source software from the Spatial Analysis Lab, Arizona State University.

Lab assignments are due weekly and must be turned in on the day they are due.

Papers: One short paper is required. For this paper assignment students will review a journal article that describes a spatial analysis method from one of the topic areas covered by the course (e.g point patterns, continuous data, area data, or sampling).  Papers should be approximately 3 pages in length. They are due December 3.

Midterm:

There will be a take home midterm exam distributed on October 24 and due October 29.

Students must complete a final project using analysis techniques learned in the course of the class. There are two options for the final project:

  • implement of a spatial analysis technique, or
  • carry out spatial analysis on a data set of your choice. For the first option, any programming or scripting language can be used to code an analysis method.  For the second option, the objective will be to select a data set of your choice, use exploratory techniques to examine the data, and develop a hypothesis or set of hypotheses concerning the data and test these using techniques discussed in class.  Any software of your choice can be used to perform the analysis.  Many spatial data sets are now available on the web but they can take some work to prepare for analysis. You should not leave planning for this project until the eleventh hour. A one-page project description of what you propose to do will be presented in class on November 5. Final presentations of projects will be scheduled during final exam week.

Grading

  • Lab Assignments – 20%
  • Midterm Exam – 20%
  • Journal Article review paper – 15%
  • Final project and presentation – 35%
  • Class Participation – 10%

C. Communications

  • You may want to have a Skype account for this course (see http://www.skype.com). Please forward your Skype username to beard@spatial.maine.edu after enrolling in the course. If the ConnectPro technology fails for a discussion session, the instructor may initiate a conference call on Skype. 

D. Important Notices

E. Instructor Office Hours & Discussion Session

  • For one-on-one discussions with the instructor, E-mail to beard@spatial.maine.edu is often the simplest way to get a message through and a response. You are also welcome to call my office at 207-581-2147.
  • On-campus Students: I am in the office most days and you are welcome to drop by or call at any time although appointments are better for longer discussions.
  • On-line Students (Live Discussion  Sessions): An online discussion session can be established on Thursday afternoon  from 4:00-5:00 pm East Coast US time for  distance students if needed.  Alternatively students can contact the instructor for a one-on-one discussion by Skype.

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