Advanced Geographic Data Analysis
 Geog 414/514: Spring 2008, 2:00-3:20 TuTh, 206 Condon Hall
Topic:  Visualization and Data Analysis
Instructor:  Pat Bartlein, bartlein@uoregon.edu, 154 Condon Hall, 6-4967, office hours 4-5p M, 1-2 Weds
Course web page:  http://geography.uoregon.edu/bartlein/courses/geog414/
GeogR web page:  http://geography.uoregon.edu/GeogR/

Course overview:  Phenomena describable by multiple variables arise in many subfields of physical and human geography and related disciplines.  The focus of this course is on the analysis and display of geographical data by traditional data-analytical methods and by newer methods of scientific visualization.  The R data-analysis and computing environment will be used.
Schedule

Date

Lecture

Topic -- See individual pages for readings

 Exercises & Exams (out) due

4-1 (Tu)

1

 Intro, data analysis and visualization in R 1 Getting and using R  

4-3 (Th)

2

 Univariate plots 2 Univariate plots  

4-8 (Tu)

3

 Bivariate plots   1

4-10 (Th)

4

 Descriptive statistics 3 Bivariate plots and desc. stats  

4-15 (Tu)

5

 Maps in R examples: [Part1] [Part 2]   2

4-17 (Th)

6

 Multivariate plots examples: [Part1] [Part 2] 4 Multivariate plots  

4-22 (Tu)

7

 Descriptive plots and statistics for spatial data   3

4-24 (Th)

8

 Spatial neighbors and spatial autocorrelation 5 Spatial plots and stats  

4-29 (Tu)

9

 Reference distributions Exam 1 -- due 5-8  

5-1 (Th)

10

 Statistical inference   4

5-6 (Tu)

11

 Analysis of variance 6 CI's, t-tests, ANOVA  

5-8 (Th)

12

 Regression analysis   5

5-13 (Tu)

13

 More regression analysis 7 Regression analysis  

5-15 (Th)

14

 Nonparametric regression    

5-20 (Tu)

15

 GLMs, GAMs, and CARTs   6

5-22 (Th)

16

 Interpolation and contouring    

5-27 (Tu)

17

 Principal components analysis and factor analysis 8 Multivariate analysis  

5-29 (Th)

18

 MANOVA, discriminant analysis   7

6-3 (Tu)

19

 Mutivariate distances and cluster analysis Exam 2 -- due 6-11  

6-5 (Th)

20

 High-resolution and high-dimension data sets    
6-11 (W)

 

 Exam day   8
 
Online Readings:  (available on CRAN at http://cran.us.r-project.org/other-docs.html)

Kuhnert, P. and W. Venebles, 2005, An Introduction to R:  Software for Statistical Modelling & Computing.  CSIRO Australia (.pdf)

Owen, W.J., 2006, The R Guide.  Dept. of Mathematics and Computer Science, University of Richmond.  (.pdf)

Rossiter, D.G., 2006, Introduction to the R Project for Statistical Computing for use at ITC.  International Institute for Geo-information Science & Earth Observation (ITC)

Maindonald, J.H., 2004, Using R for Data Analysis and Graphics, an Introduction (.pdf)

Reserve Readings:

Cleveland, W.S., 1993, Visualizing Data, Hobart Press, 360 p.

Rogerson, P.A., 2001, Statistical Methods for Geography, Sage, 236 p.

Fotheringham, A.S., C. Brunsdon, and M. Charlton (2000) Quantitative Geography,  Sage, 270 p.)
 

Format and grading:  Lectures, mid-term and final take-home exams, and eight exercises.  Both exams and all exercises must be completed to receive a passing grade for the course.  Basis for grading:  Undergraduates:  exam 1 (20%), exam 2 (20%), exercise 1-8 (7.5% each, 60%) total.  Graduates:  exam 1 (15%), exam 2 (15%), exercise 1-8 (7.5% each, 60%), short write-up of the analysis of a "real" data sets (10%).
 
Prerequisite:  GEOG 4/516  Introductory Geographic Information Systems
 

Expected effort:  Lectures will meet for 1.5 hours each, twice a week, and the half-hour immediately following lectures will be devoted to issues that arise while using R for the exercises.  Exercises will require around 4 hours each for completion; more or less time may be required depending on the efficiency with which they are done.  Plan on spending about 4 hours per week on reading and reviewing class web pages and notes.  In addition a few hours may be required for downloading and setting up R.
 

Other topics:  As is implied by the topic of the course, the visual inspection and interpretation of the output of computer analyses will be important, but accommodation for alternative methods of course-material access may be possible--please see me a soon as possible.  Collaboration on the exercises is not prohibited (and in fact is a good idea) but the answers must be composed individually.  Similarly, discussion of the exam questions may be useful in forming answers, but again, the answers must be composed individually.  Other academic dishonesty policies will be enforced.  (see, for disussion):  http://studentlife.uoregon.edu/judicial/conduct/sai.htm). 

 
[Geog. 414/514] [syllabus] [lectures & exercises] | [GeogR] [topics] [data sets] [documentation]

Department of Geography, University of Oregon, bartlein@uoregon.edu