1. Definitions (Jim’s) Transformations: General term for anything that takes an input and provides an output (e.g. “transforms” data) Processing: Converting file formats, projections, data types, simplification/generalization, math Analysis: Extracting information from data. Or, extracting the “signal” of interest from the noise. Modeling: Searching for causation so we can do prediction

2. Traditional Analysis Hypothesis Testing vs. Data Mining Descriptive Stats Regression: Linear, Generalized Linear, Generalized Additive Models, etc. Tests: T, F, etc. Frequency Analysis Analysis of variance Correlation Interpolation

3. Types of Spatial Analysis Points –Cluster –Heat Maps –Autocorrelation, Interpolation Polylines –Networks –Stream flow Polygons –Zonal (w/rasters) Rasters –Slope, aspect –View sheds –Habitat Volumes –Flow –Strata TINS –Hydrology –Volumetric Calcs All have general stats: Mean, Max, Min, Std. Dev.

6. San Francisco Bay NOAA

7. Northern Humboldt Bay GoogleMaps, Digital Globe, USDA

8. Analysis Find the analysis that fits the problem What results does it provide? What data is required? What are the assumptions?

9. Trends, Autocorrelation, Noise Spatial Data Analysis in Ecology and Agriculture Using R

10. Linear Regression: Assumptions Predictors are error free Linearity Constant variance within and for all predictors (homoscedasticity) Independence of errors Lack of multi-colinearity Also: –All points are equally important –Residuals are normally distributed (or close)

11. Normal Distribution

12. Evaluate the Model

13. Good Model?

14. Linear Regression The natural world rarely follows linear relationships Be careful of using analysis that does not match what is really going on Document any “caveats” in discussions