1. Geographically Weighted Regression Use and application of spatially weighted regression for environmental data analysis Ken Sheehan - April 5, 2010

2. - John Muir in “My first summer in the Sierra” Marveled that “one could run down the boulder field at full speed and the rocks were perfectly spaced for such an endeavor” Some data has inherent spatial qualities Ignore, or address?

3. Progression of ideas at WVU Spatial analysis for resource management Advanced spatial analysis Can’t find the fish? Study it’s habitat… –Important because –stream habitat dictates stream biota –Principle of “What’s there” is dictated by “what’s there” (which goes for many systems, not just environmental).

4. Spatial Data and Streams Likely to be autocorrelated Geology –Substrate Flow Depth Sheehan and Welsh (2009)

5. Most Recently Research on Grayling and Wapiti Creeks, Greater Yellowstone ecosystem.

6. `

7. Before Delving into GWR… Background on linear regression Fitting a line to a data set –Assumes homoskedacity Static (flat variance) –Great for predicting relationships –Heavily used, perhaps most dominant type of statistical analysis in environmental and other fields Classic examination of observed versus expected

8. Spatial autocorrelation (Legendre 1993) Red herring (Diniz 2003) or sweet new tool ? –Yes and no Progression of ideas

9. Background Continued.. Fotheringham and Brunsden (1998) Modification of linear regression formula to include spatial attributes of data. Standard regression formula GWR regression formula

10. Concepts –Different than adding x,y coordinates to ordinary linear regression analysis datasets –Creates a moving variance for data with non-stationarity (regional variation). –Not all data is appropriate for Geographically Weighted Regression. –Still a work in progress- econometrics

11. Demonstration of GWR Wapiti and Grayling Deceptively complex process