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SIE 512 - Overview

Spatial Analysis

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

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

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:

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 30.

Midterm:

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 2. Final presentations of projects will be scheduled during final exam week. There are two options for the final project:

Grading

C. Communications

D. Important Notices

E. Instructor Office Hours & Discussions


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Contact Information

Computing and Information Science Courses Online
The University of Maine
Orono, Maine 04469
207.581.1110
A Member of the University of Maine System