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. 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, Gerald M. Barber and David L. Rigby, 2009, Elementary Statistics for Geographers, 3rd Ed.: New York, Guilford Press, 653 p., available at the UMass Textbook Annex.
- R Development Core Team, 2009, 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 display; EDA | Chap. 2 |
| 3 | Descriptive statistics | Chap. 3 |
| 4 | Statistical relationships | Chap. 4 |
| First Exam | ||
| 5 | Introduction to probability | Chap. 5 |
| 6 | Frequency distributions | Chap. 5 |
| 7 | Sampling designs | Chap. 6 |
| 8 | Statistical estimation | Chap. 7 |
| Second Exam | ||
| 9 | Hypothesis tests | Chap. 8 |
| 10 | Two-sample hypothesis tests | Chap. 9 |
| 11 | Correlation and regression analysis | Chap. 12 |
| 12 | More regression analysis | Chap. 12 |
| 13 | Spatial patterns | Chap. 14 |
| 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 Chapter 2
- More notes related to exploring data (bivariate data)
- Notes related to time-series material in Chapter 3
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!