Instructor: Matt McGranaghan PSB 313, 956-7092, email@example.com
Meeting: MW 8:30-9:20 F 8:30-10:20
Description: Application of geostatistics to estimate spatial dependence to improve soil (and regional) sampling; provide insight into underlying soil, geographic, and geologic process; and to provide quantitative scaling up of point measurements to fields, regions, and watersheds. State-space modeling also will be included. (-from the catalog, when no one was looking!)
(This one would be within a 25 word limit:) Spatial autocorrelation, regionalized variable theory, semivariograms, kriging, conditional simulation, and Bayesian maximum entropy to estimate spatial fields; improve sampling; and examine spatial dependence and pattern.
Pre-requisites: familiarity with GIS (GEOG 488, NREM xxx, or equivalent) esp ArcGIS 9.x which will be used for many examples) and (Graduate level ?) coursework in statistics, e.g. ZOO 631 or a 400 level or higher or a course in Time Series analysis would be a good idea.
Grading: Grades will be assigned using my best professional judgement of your performance based on the following weighted components: quizes and exercises 40% mid-term 20% term project 40% Class attendance and participation are assumed requisites.
Software: ArcGIS Statistical Analyst, Surfer, and geoR
Webster & Oliver Geostatistics for Environmental Scientists
Diggle & Ribeiro (2007) Model-based Geostatistics