Topics covered will focus on skill-based approaches, thus allowing transferal of material presented in class to other software packages. Note all that software used in this course is available for Mac OS, Windows, and Linux. Detailed examples, resources, etc. can be found here. A Brief introduction to GIS concepts and terminology will be presented. Further reading on GIS, which is highly advised for those in this seminar, is listed below. I will post suggested sections and page numbers as we progress.

 
Schedule:

• 10/2: Introduction
Agenda
Course overview and approach. Quick descriptions of OSS GIS software.
Homework
Get a copy of :
- An Introduction to R
- R Reference Cards (lamination might be fun)
 
Reading for next week:
1. Articles #2 and #3 under the "OSS In the Sciences" heading, found below. Skim article #2 for ideas on how GRASS, R, and databases can be used together effectively. Article #3: read as much as interests you, it is mostly about the idea of open source software.
 
2. Chapters 1-6 from "An Introduction to R", note that much of this information functions as a good reference. Skim these sections for background on some of the terms that will be used in class on monday.
• 10/9: System requirements: jargon, Installation of R, discussion of resources, data types, examples
Agenda
First 6 chapters of "An Introduction to R", focusing on common data types, functions, and objects
Homework
1. Get a copy of R, and install it on your machine:
MacOS X users : this version
Windows users : this version
Linux users : contact me
 
2. Look over the worked out example of a comparison between pipette and laser granulometer derived soil texture values data.
 
3. If you have installed R, attempt the above example on your own. Note that all of the data used in the above example is included at the bottom of that page.
 
4. Optionally look over Section 2.1 from the "R Import/Export Manual". Note that this is a very short read, and will answer most questions regarding getting your data into R.
• 10/16: Installing R, soil texture examples
Agenda
Installation of R
 
Time permitting, we will run through a sample session in R highlighting:
1. importing soil texture data
2. plotting it
3. performing a simple linear regression
4. plotting the data on a textural triangle
Homework
1. Look over the second example on soil texture data, and attempt to replicate the results on your own machine. Note that the files referenced are attached at the bottom of that page.
 
2. Read chapter 12 from "An Introduction to R". This will help to form a solid base for all plot-related operations with R.
 
3. Try the sample session outlined in Appendix A, from "An Introduction to R".
• 10/23: More examples of plotting in R: textural triangle and XRD plots
Agenda
Please bring the handout from last week. I will have extras if needed.
 
We will run through a sample session in R highlighting basic R operations and plotting, starting from the second soil texture example. Time permitting we will go over the import, plotting, and simple automated analysis of XRD data.
Homework
1. Look over the Carlo-Erba Example R session. Several important concepts on linear models, multi-figure plotting, plot layout, and working with data frames are covered. We will quickly go over this example next week.
 
2. Install the 'sp' and 'spatstat' packages into your local R installation. Go to the Spatstat website and download the short manual for future reference.
 
3. Download the Introduction to Spatstat article, and read pages 1-6. Keep this article as we will be using it for the next couple of weeks.
 
4. Take a quick look at the spatial analysis 1 example. We will go over this in class on Monday.
• 10/30: Spatial Point Analysis in R: SpatStat
Agenda
1. Quickly review the Carlo-Erba example session, answer any questions.
 
2. Discussion on complex datatypes, and accessing their attributes
 
3. Introduction to spatial point analysis with the 'spatstat' and 'sp' R packages. Please have these package installed on your machine, and download the two papers referenced in the last homework assignment. We will go through a couple worked through examples of exploratory spatial statistics. Please look over the first 6 pages of the Introduction to Spatstat article for background.
 
4. Work through the spatial analysis 1 example
Homework
1. Install the 'custer' package on your local copy of R.
• 11/6: R: Spatiail clustering approaches using the cluster package. GRASS concepts
Agenda
1. Look over the spatial clustering example
 
2. Cover some GRASS Basics: database/location/mapset
Homework
1. Look over GRASS resources:

• GIS.m Demo
• The GRASS Wiki
• Numerous examples, notes, and ideas on GRASS

 
2. Time permitting, skim through the GIS Concepts section of the GRASS Wiki. This will help define many of the terms we will be using in subsequent classes.

• 11/13: GRASS: more examples
Agenda
1. Give Markus' Como2006 presentation on GRASS.
2. GIS/GRASS resources:

• The GRASS Manual Pages
• GRASS mailing list and archives
• GRASS Wiki
• GRASS Documentation
• GRASS 6.0 Tutorial
• GRASS Book

 
3. Native QGIS+GRASS for Windows

Homework
none
• 11/20: No Class
• 11/27: Document Preperation with LATEX
Agenda
LATEX is part of a suite of applications commonly used for the preperation and electronic publishing of complicated, cross-referenced texts such as book chapters, journal articles, or a dissertation. Since the use of LATEX and associated tools is such a broad topic, I will give a general overview of the approach.
 
Installation
1. Mac OS X
2. Windows
3. Linux
 
Some Resources
3. LATEX CV template
4. Tips on writing a resume in LATEX
5. The not-so-short introduction to LATEX
6. Guide to LATEX 4th Edition by Helmut Kopka and Patrick W. Daly
• 12/4: GRASS and R: overview of approach, example analysis
Agenda
LATEX is part of a suite of applications commonly used for the preperation and electronic publishing of complicated, cross-referenced texts such as book chapters, journal articles, or a dissertation. Since the use of LATEX and associated tools is such a broad topic, I will give a general overview of the approach.
 
Background and reference material
1. Latest guide on interfacing GRASS and R. Article by Roger Bivand.
2. Fundamentals of interfacing GRASS and R. This article is slightly dated, see above article for minor differences in the current approach.

 
Some Examples
1. slope analysis
2. terrain classification

 
Case Study: sampling raster values at/within vector features
1. r.what in=elevation.dem,landcover.30m east_north=`v.out.ascii in=bugsites | awk -F"|" '{ORS=","; OFS=","}{print $1, $2}'`
2. v.what.rast vector=bugsites raster=elevation.dem column=elevation
3. StarSpan

 
Comparing Raster Data
1. comparison of spatial aggregation methods
2. visual comparison of imagery to vegetataion map

 
Misc. Tips
1. Raster and Vector Tips
2. See the GRASS Wiki for more ideas.

 
OSS In the Sciences

1. Tufto, J. & Cavallini, P. 2005: Should wildlife biologists use free software? - Wildlife Biology 11:67-76.
2. Open Source geocomputation: using the R data analysis language integrated with GRASS GIS and PostgreSQL data base systems.
3. The Cathedral and the Bazaar

 
Statistical Methods in Soil Science

1. Arkley, R.J. Brady, N.C. (ed.) Advances in Agronomy Statistical methods in soil classification research Academic Press, 1976, 37 - 69

 
GIS Resources

1. Robinson, A.H.; Morrison, J.L.; Muehrcke, P.C. & Guptil, S.C. Elements of Cartography John Wiley and Sons, 1995
2. Issaks, E.H. & Srivastava, R.M. An Introduction to Applied Geostatistics Oxford University Press, 1989
3. Lo, C.P. & Yeung, A.K.W. Concepts and Techniques of Geographic Information Systems Prentice Hall, 2006
4. Neteler, M. & Mitasova, H. Open Source GIS A GRASS GIS Approach Kluwer Academic Publishers, 2003
5. Jenson, J.R. Remote Sensing of the Environment: and earth science perspective Prentice Hall, 2000