Quantitative Methods in Geography

• GEO 340 Quantitative Methods in Geography
• Schedule # 37290
• Fall, 2007
• MWF 10:10 - 11:00
• Room: Morrill 2 245
• Instructor: W. D. McCoy (Office: Morrill 236; Phone: 545-1535)

Overview

This course explores methods of data analysis and visualization that are useful in geographic research. We will discuss descriptive and inferential statistics through linear regression and analysis of variance. We will explore some applications of quantitative methods to spatial data. Discussions will draw on examples from both human and physical geography. The course will involve intensive use of computers, but you don't need to have your own computer to take this course. This course satisfies the Analytical Reasoning (R2) General Education requirement.

Textbook and documentation

• James E. Burt and Gerald M. Barber, 2003, Elementary Statistics for Geographers: New York, Guilford Press, 640 p. (This book should be available at the UMass Textbook Annex.)
• John Verzani, 2002, simpleR -- Using R for Introductory Statistics, 143 p. (This is a free pdf document available at the author's website.)
• R Development Core Team, 2007, An Introduction to R, 94 p. (The pdf version is in the file R-intro.pdf.)

Additional resources

• For help with functions and datasets in R, you can use help.start() in your R session and access documentation through your browser.
• For some excellent examples of graphics in R and the R code used to make them, see the web page for Paul Murrell's R Graphics book.
• Another good site with many examples of graphics in R along with the code used to produce them is the R Graph Gallery
• For additional documentation, datasets, libraries, information, or your own copy of R software visit a Comprehensive R Archive Network (CRAN) site.

List of topics to be covered

Week	Topic		Reading
1	Introduction; data types		Chap. 1
2	Data description, summarization, EDA		Chap. 2, 16.1-16.3, 17.1
3	Spatial data		Chap. 3
4	Time series		Chap. 4
	First exam
5	Introduction to probability		Chap. 5
6	Frequency distributions		Chap. 6
7	Sampling designs		Chap. 7
8	Introduction to inferential statistics		Chap. 8
	Second exam
9	Hypothesis testing		Chap. 9
10	Correlation and spatial autocorrelation		Chap. 12
11	Regression analysis		Chap. 13
12	More regression analysis		Chap. 14
13	Time series analysis		Chap. 15
	Third exam

Lecture notes

Some of the lecture notes are available online here as Portable Document Format (pdf) files. They are provided in a format suitable for printing (four slides per page).

• Notes related to material in the first two lectures
• Notes related to material in Chapters 2, 16 and 17
• More notes related to exploring data (bivariate data)
• Notes related to spatial data material in Chapter 3
• Notes related to time-series material in Chapter 4
• Notes related to material on probability in Chapter 5
• Notes related to material on probability distributions in Chapter 6
• Notes related to material on sampling in Chapter 7
• Notes related to material on point estimation and confidence intervals in Chapter 8
• Notes related to material on hypothesis tests in Chapters 9 and 10
• Notes related to material on correlation and regression in Chapters 12 and 13

Homework

Homework assignments can be found here.

Functions and datasets

This is the location of files containing source code of miscellaneous functions and datasets to be used to complete your homework assignments.

Evaluation

Final grades will be based on performance on three equally weighted exams (to total 45% of the final grade), graded weekly assignments (to total 45% of the final grade), and a final data analysis project (10%). You must keep up with the weekly assignments -- if you fall behind you will never catch up! Assignments that are turned in late will be marked down in proportion to the length of time that they are overdue. Class attendance is mandatory!