Latest Contributions by EricKrause

@JustinLee Great question!  Forest-based Classification and Regression does not make any normal distribution assumption about the data.  Generally speaking, outliers and extreme values will be most problematic for the model.  Ideally, you'll have a roughly even spread of values between the minimum and maximum, but there's no requirement that the distribution be bell-shaped. ... View more

Hi @Lacin_Ibrahim,  The Visualize Space Time Cube in 2D tool will re-create the most recent result of Emerging Hot Spot Analysis for the analysis variable.  If you rerun EHSA on the same variable, the Visualize STC in 2D tool will start creating the output of the most recent EHSA tool run.   Please let me know if you have any other questions or need any clarifications. -Eric ... View more

Hi Elijah, I'll try with an example of Inverse Distance Weighting with five points: p1, p2, p3, p4, and p5.  Each of these points have a location and a measured value.   Cross validation would start by removing p1.  It would then use p2, p3, p4, and p5 to predict the value of p1.  In IDW, this means taking the weighted average of the values of p2 to p5 (weighted by inverse distance). This will result in some prediction (called the cross validation prediction) that can be compared to the measured value of p1.  Next, p2 would be removed, and p1, p3, p4, and p5 would be used to predict to the location of p2 (note that p1 is added back to the dataset after being cross validated). The same is done for p3, p4, and p5, each using the other four points. This would produce five cross validation errors that would be used to calculate, among other things, the root mean square error of the IDW model. But when actually making the prediction surface (after cross validation), all points are used to make the predictions.  The surface also predicts values everywhere, including at the input point locations.  So, what will it predict at, say, the location of p3? The prediction is the weighted average of all the points p1, p2, p3, p4, and p5, weighted by the inverse distance to p3.  But the distance from p3 to itself is zero, which gives the value of p3 a weight of infinity.  This forces the predicted value to be exactly equal to the measured value at p3.  This is what makes IDW an "exact" interpolation method.   Please let me know if that still is not clear. -Eric   ... View more

@Elijah  Each horizontal line of points in the graph appears to have the same Measured value on the y-axis (or very close to equal).  However, they each have a different Predicted value on the x-axis.  Having many repeated values in the field you used to interpolate would produce this kind of graph.  This isn't necessarily a problem, but you should look into it and confirm that the repeated values are expected in your data. ... View more

Hi @ArnieWaddell1  The graph is showing the distribution each field in the cross validation Table (change the field with the Field pulldown) using a kernel density estimation.  Think of it like a smoothed histogram of the Measured values, Predicted values, or Errors.   Overlaying the Measured and Predicted fields is the most important use of the Distribution tab.  This lets you compare the distribution of true values and the cross validated predictions.  Ideally, the two should have very similar distributions, and big deviations might indicate problems in the interpolation model. -Eric ... View more