EGEO 305, Analysis of Spatial Data, Spring 2007

 "Preliminary Syllabus"

Class Web-Page

http://myweb.facstaff.wwu.edu/~medlerm/classes/06_07/305

Time and Location: MW 2:00-3:50 pm, AH 14, (lab access AH16 MW 3:00-3:50)
Instructor: Michael Medler
Office: AH 210  x3173
Email: michael.medler@wwu.edu
Office Hours: By appointment, and Wed. 12:00 - 2:00. (I am in my office often, drop by)

TA: Kellee Timpson

Office: AH 24

Office Hours: MT 11:00-1:00, and by appointment

Email: kelleetimpson@yahoo.com

Text: An Introduction to Statistical Problem Solving in Geography, 2nd Edition

Analysis of Spatial Data:

Statistical tools and techniques are now central to the activities of many academic disciplines and careers.  In geography an understanding of spatial statistics and the ability to interpret statistical results are often vital.  In fact such skills are a vital part of participatory democracy and essential in many arenas of decision making ranging from the boardroom to the bedroom.  This course is intended to advance student’s statistical literacy and improve the ability to both use statistical tools and, perhaps more importantly, interpret the vast amount of statistical data and information we are all subjected to every day.

This course covers a wide range of the basic statistical techniques used in spatial science.  This set of spatial statistical tools is similar to the tools one might study in other disciplines, but in this course we will focus specifically on the tools used in spatial examinations.  Though this course is not intended to be computationally advanced, this course does assume each student has taken the prerequisites and has an understanding of the basic interests of geographers.  We also assume a working familiarity with basic statistical concepts such as probability, range, and mean.   The course is also specifically providing the statistical techniques necessary for a host of upper division geography courses as well as introducing a set of techniques that are at the heart of many geographic careers.

GIS is now the standard tool in the analysis of spatial data.  Students will learn basic GIS use, and we will use ArcGIS software in many of the lab exercises in this class.

Course learning outcomes:

Students will practice and improve their ability to interpret and discuss statistical results and spatial analysis they find in scientific literature as well as the popular press.

Students will learn to analyze spatial data. They will also be introduced to tools such as spread sheet and GIS software to help in this analysis.

Students will be able to use and interpret the results of statistical tools including basic descriptive statistics such as; minimum, maximum, range and average. They will understand spatial patterns and be able to interpret correlation and regression results

Grade Components

Labs (50 points):

We will use a combination of tools and techniques for the labs in this class, but each student will need consistent access to Microsoft Excel as well as the internet. Labs are worth 10 points each: Due at the day and time specified on the lab. If the TA is notified before hand, late labs loose only 20% each day late, otherwise late labs are worth 0 points.

Application paper (10 points): Due May 30.

Each student will select a single application of spatial statistics and produce a 2-3 page literature review describing the application and conveying to the reader the current "state of the art" of the selected application. The review will include references to, and bibliographic situations for at least two articles found in peer-reviewed scientific journals.

Exams (20 points):

    We will have two cumulative exams.

Current event reports: (10 points)

    Each student will do at least one current event report in class.

Participation pop-quizzes and attendance: (10 points)

Current Event Report Schedule

Grades To Date

Preliminary Schedule

Exams

Exam #1, Wed. April 30

Exam #2, Wednesday June 6