Introduction to ArcGIS Geostatistical Analyst & Fragstats Represent the Data Explore the Data Fit the Model Perform Diagnostics Compare Models Classify the Output Assess Pattern ArcGIS  Using applied geostatistical methods to produce a continuous predictive surface from a discrete collection of sampled points  Using GIS to reclassify the predictive surface into functional classes or bins  Using landscape ecology metrics to assess and quantify the composition and configuration of the classification Fragstats

Representing the Data Converting data to GIS format The first step requires that the data be converted into a format that can be used by the GIS software

How are the data distributed? Are there global trends in the data? Exploring the Data Are there outliers? “The interpolation techniques used by the software work best with normally distributed data….”

The semivariogram models the spatial dependence or autocorrelation between measured points. The semivariogram cloud shows ½ the squared difference between all pairs of points as a function of separation distance (h). Modeling the semivariogram is the key step between spatial description and spatial prediction. Choosing the best model or continuous function to fit the empirical semivariogram helps ensure that the predicted values are as accurate as possible. THE SEMIVARIOGRAM

Anisotropy Things are more alike for longer distances in some directions than in other directions Directional Trends When fitting a model to the semivariogram it is important to consider…

Set search neighborhood Assess the diagnostic stats Model the semivariogramAccount for global trends Choose the method The ArcGIS Geostatistical Wizard uses a series of steps to calculate a best fit model & create a predictive surface Fit the Model Perform Diagnostics &

Which model is best? Compare Models

Reclassify predictive surface raster into categories or functional groups Classify the Output

Assess Pattern Using metrics of landscape ecology, assess pattern of classified data using Fragstats… Input = classified input gridOutput = spreadsheet of selected metrics