SIE 512: Overview

Spatial Analysis

Kate Beard
Room 326 Boardman Hall

Course Objectives

This course introduces techniques for the statistical analysis of spatial data. The course will cover characterization of spatial data, and techniques for visualizing, exploring and modeling spatial data distributed as point patterns, continuous (geostatistical) data, area data, and methods and problems in spatial data sampling. Students will become familiar with methods for identifying, describing, modeling, and evaluating spatial patterns in observed data. Students will become familiar with using R for applied spatial data analysis.

A. Class Sessions

  • On-campus Students: Tues and Thursday, 2:00 – 3:15 Tues & Thurs, Room 326 Boardman Hall
  • Live Broadcast: Available at 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.
  • Audio Chat: Distance students can view the lectures at any time of their choosing during the week.  A  discussion session can be arranged to discuss questions or lab issues.

B. Course Materials

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

Supplementary  Readings:

Additional references:

  • Banerjee, S., Carlin, B. & Gelfand, A. 2014 Hierarchical Modeling and Analysis for Spatial Data. Chapman and Hall/CRC Press
  • 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.
  • 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.
  • Plant, R. 2012. Spatial Data Analysis in Ecology and Agriculture using R. CRC Press.
  • Schabenberger, O. and Gotway, C. 2004. Statistical Methods for Spatial Analysis. Chapman and Hall/CRC Press

Lab exercises:

Most lab exercises will be done using R, an open source statistical software.  RStudio is an open source  integrated development environment (IDE) for R which I recommend as it supports syntax checking,  direct code execution, and tools for plotting, history, and debugging. It runs on Windows, Mac and Linux and is easy to install. The downloaded site is here.

We will also use Geoda, open source software from the Spatial Analysis Lab, from the University of Chicago available from their download site here.

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

Papers: One short review 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).  Review papers should be approximately 3 pages in length. They are due November 29.


There will be a take home midterm exam distributed the third or fourth week of October.

Final Projects:

Students must complete a final project using analysis techniques learned in the course of the class. A one-page project description of what you propose to do will be presented in class on November 6. Final presentations of projects will be scheduled during final exam week. There are two options for the final project:

  • implement a spatial analysis technique. For this option, any programming or scripting language can be used to code an analysis method.
  • carry out spatial analysis on a data set of your choice. For this 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.


  • Lab Assignments – 30%
  • Midterm Exam – 25%
  • Journal Article review paper – 10%
  • Final project and presentation – 35%

C. Communications

  • For distance students I can schedule a Zoom meeting to discuss any course questions you may have.  Email me, to schedule a meeting.

D. Important Notices

E. Instructor Office Hours & Discussions

  • For one-on-one discussions with the instructor, E-mail to You are also welcome to call my office at 207-581-2147.

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