ENVS 399: Special Topics in Environmental Studies
Environmental Data Analysis and Modeling

http://zebu.uoregon.edu/2004/es399.html

Class Discussion Page

Assigned Teams


Instructor: Greg Bothun
Office hours MUWHF 10 - noon. (but check the Camera First
Office: 417 Willamette
email is better nuts@bigmoo.uoregon.edu



Course Structure

This course is designed to introduce Environmental Science students to the task of constructing models based on sparse data and, in so doing, to understand the difficulties associated with this task. Virtually every environmental problem is characterized by three things:

  1. Noisy/ambiguous data that defines the problem problems are therefore usually defined by perception and opinion rather than by objective data.

  2. Difficult physical modeling since not all of the input physics/chemistry/biology/geology is known

  3. Policy makers/public interest groups that do not understand science and the scientific process.

This course is designed to immerse the students in these three intertwined difficulties. This class will function like an environmental consulting company with me as the Boss O Rama. The class will be broken up into 4 teams (this will be done on Thursday). Each team will be given the same problem to work on and to prepare and present a consulting report in class.

This class is being held in a wireless laptop classroom and we will be making use of that infrastructure throughout the term. A by product of this class is that you will learn how to use Microsoft Excel as a statistics and data analysis tool.

There will be no assigned text book as there is certainly not one that is relevant to this material.

Most all of the assignments in this class will be group based and will take two weeks to do. A few individual assignments to build skills in key statistics areas will also be given.

Lectures will be a mix of computer based presentations and standard blackboard lectures and derivations. Extensive notes for each lecture will appear on the course web site:

http://zebu.uoregon.edu/2004/es399.html

Make sure you reload the course homepage each time you visit it to pick up any changes.

Course Grading will occur around the following guidelines:

There will be no midterm in this course but there will be a final exam. The final exam will count for 1/3 of your grade.

All student teams will be expected to be active in class discussions and prepare presentations for the class during the course. There will be 5 team assignments in this class, each with presentation components. These exercises will found for the other 2/3 of your grade.

The goal in this class is to learn important techniques and to gain experience building real models from real data. In this manner, I regard this class as a skill building and proficiency based class and you will be graded accordingly. In the past, no one in this class has received a grade lower than B- .




Course Content

This class will be highly fluid. Expect that.

This course has four main goals:

    To familiarize students with some advanced statistical concepts involving goodness of fit tests.

    To get students to acquire data and understand sparse sampling techniques and reliable tracers.

    To use this data to construct models with predictive power and to assess their accuracy.

To get students to work together collaboratively and to become facile at presenting their models and their assumption set.

The course will begin with some tedious but necessary lectures on methodology and conceptual model making. We will definitely be extending what was introduced in ENVS 202 in terms of linear regression and fitting data to a much higher level.

We will then move onto various environmentally hot topics, such as global warming, forest thinning, removal of the snake river dams, population projections and energy generation problems. Group exercises around these themes will be designed.

Finally, this class will be using GPS units to learn how to convert coordinate data into a spatial map. There are 10 units available and two per team will be loaned out when the time comes.

Lecture Notes:

Jan 07 Primer on Sampling, Regresssion. Excel Tutorial

Jan 12 The Uncertain Role of Methane in Global Warming

Jan 14 Finding Events in Noisy Data

Jan 21 Student Presentations: I

Jan 26 Introduction to the chi2 Statistic

Jan 27 Oil Depletion Issues

Feb 02 Sustainable Energy Technologies

Feb 04 Trend Extrapolation/Resource Depletion

Feb 09 Estimation techniques and practice problems

Feb 11 Student Presentations (Well done Again!)

Feb 16 Population Dynamics

Feb 18 The KS Test for Comparing Distributions

Feb 23 Waveforms and wavelets, as applied to climate data

Feb 25 Student Zardhog presentations

Mar 01 A Case Study: The Coburg 900 MW proposed power plant

Mar 03 Applied Ecology - The SpookyTooky Assignment

Mar 08 Are humans the same as spookytookies ?