Introduction to ArcGIS Geostatistical Analyst & FragstatsRepresent the DataExplore the DataFit the ModelPerform DiagnosticsCompare ModelsClassify the OutputAssess PatternArcGIS 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 classificationFragstats

Representing the DataConverting data to GIS formatThe 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 DataAre 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 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 TrendsWhen fitting a model to the semivariogram it is important to consider

Set search neighborhood Assess the diagnostic statsModel the semivariogramAccount for global trendsChoose the methodThe ArcGIS Geostatistical Wizard uses a series of steps to calculate a best fit model & create a predictive surfaceFit the ModelPerform Diagnostics12345&

Which model is best?Compare Models

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

Assess PatternUsing metrics of landscape ecology, assess pattern of classified data using FragstatsInput = classified input gridOutput = spreadsheet of selected metrics

Quantile - The range of possible values is divided into unequal-sized intervals so that the number of values is the same in each class. Classes at the extremes and middle have the same number of values. Because the intervals are generally wider at the extremes, this option is useful to highlight changes in the middle values of the distribution. A fundamental assumption for geostatistical methods is that any two locations that are a similar distance and direction from each other should have a similar difference squared. This relationship is called stationarity.

The 1st and 3rd quartiles correspond to the cumulative proportion of 0.25 and 0.75, respectively. If the data was arranged in increasing order, 25 percent of the values would lie below the first quartile, and 25 percent of the values would lie above the third quartile. The 1st and 3rd quartiles are special cases of quantiles. The quantiles are calculated as follows: quantile = (i) - 0.5 / N

where (i) is the ith rank of the ordered data values.