Latest Contributions by EricKrause

Hi @CStandley_1 , There seems to be a bit of confusion in that the word "confidence" isn't exclusively used for confidence intervals, and here it is referring to something slightly different.  Here, it is referring to a confidence level, not a confidence interval. The confidence level is tied to the significance (alpha) level.  There are several ways to say exactly the same thing: A feature is statistically significant at significance level 0.05. The p-value of a feature is less than 0.05. A feature is statistically significant with greater than 95% confidence. (This is the context that the label uses). A feature is not within a 95% confidence interval constructed assuming the null hypothesis is true. So the symbology of the features is based on the confidence level (or equivalently, the significance level).  While this is related to confidence intervals, to make the labels be about confidence intervals, they would have to change to something like: "Cold Spot (Not Within 90% Confidence Interval)".  People can certainly disagree, but I don't think this would help with understanding the output. Further, actually constructing confidence intervals for hot spot analysis results is much harder than you might suspect.  If you've done much work with confidence intervals, you're probably familiar with estimating the standard error, then creating a 95% confidence interval using a formula that looks like: Value +/- 1.96 * StandardError. However, this isn't really valid for hot spot analysis. This is honestly too complicated to try to explain in a forum environment, but there are nuances about when you can and cannot construct confidence intervals of this type (distinctions between sampling distributions and raw value distributions).  For hot spot analysis specifically, if you want to create a confidence interval for the level of "hotness" of a feature, you'd need to use something more sophisticated (like resampling), but the confidence intervals that you'd produce would be different than the confidence level of the symbology (because they would be estimating the variability of very subtly different things).  You're not expected to understand that, but just know that there is a reason that the hot spot analysis tools do not produce confidence intervals and only refer to the confidence level. ... View more

Hi @EugenioJairEscobarSánchez, There are a few things to keep in mind.  First, the GWR tool that you are using in ArcMap is not that same one that is only available in ArcGIS Pro. The newer GWR was designed to match the implementation of the GWR4 software (as it was at the time), and has been tested to give matching results. Second, there is not one single "correct" bandwidth, and there are a variety of methods to estimate it that agree/disagree with each other to varying extents.  I am not certain the method that ArcMap's GWR tool uses to optimize the bandwidth, but I can verify that Fatheringham (the "father" of GWR) approved of the methodology and created at least one tutorial using the ArcMap tool: https://gwr.maynoothuniversity.ie/wp-content/uploads/2016/01/GWR_Tutorial.pdf ... View more

Hi @GarethNash, I'm only seeing this thread now.  The "Custom 3D points" option will produce a netCDF file with predictions at the specified location, but it isn't configured in a way where it can be represented as a voxel layer.  Only the gridded points option can produce a voxel layer. ... View more

Apologies, the above reply was from me, but I accidentally posted from the wrong account. ... View more

The idea has been implemented in ArcGIS Pro 3.4 as the Directional Trend tool, available in the Geostatistical Analyst toolbox (Utilities toolset) and Spatial Statistics toolbox (Measuring Geographic Distributions toolset).  The tool re-creates the polynomial trend lines on the X-Z axis as a scatter plot chart on a feature layer. The tool is available at all license levels (a Geostatistical Analyst license is not required). ... View more

Hi @MasoodShaikh, Kriging is an "inexact" interpolation method, meaning that the prediction surface does not pass perfectly through the values of the input points and instead has a tendency to smooth predictions (meaning that the range of predictions is usually more narrow than the range of the original data values).  The weaker the autocorrelation and the more noisy the data, the more it tends to smooth.  For your data, it's likely that you have many locations where high values are very close to low values, and the kriging surface effectively smooths over the highs and lows. There are a couple things you can do.  First, you can use an interpolation method like Radial Basis Functions (aka splines) or Inverse Distance Weighting that will always honor the range of the input data values.   Second, you can disable the Nugget effect of kriging, which will force it to honor the input data range.  You can do this on the semivariogram page of the Geostatistical Wizard by changing the “Model Nugget” option to “Disable”.  However, not using a nugget effect can often create strange artifacts in the output, so it is generally not recommended.  If you do this, pay close attention to strange behavior in the resulting surface. -Eric ... View more

I mean the second.  And, yes, if the new dataset has fundamentally different properties than the first, it will be quite inaccurate.  This is only appropriate in cases like daily measurements of the same data, where the changes in the data values will be relatively small, and you're willing to sacrifice a small amount of accuracy in order to not have to manually perform kriging in the Geostatistical Wizard every day. Kriging is never particularly safe to do in an automated environment, but if it's something you really need to do, we recommend using Empirical Bayesian Kriging for it.  It is available as a geoprocessing tool, so setting up automation is relatively simple. ... View more

Hi @MWmep013, The reason for the discrepancy is that the GWR tool was reimplemented in ArcGIS Pro 2.3, and the previous version (equivalent to ArcMap) was deprecated.  Among other things, the newer version uses a different and more common formula for global and local R-squared and optimizes bandwidths differently.  The newer version follows the design and formulas of the GWR4 software (not from Esri). While you will not find it in the Geoprocessing pane, the deprecated version can still be used through arcpy (for example, in a Python Notebook or the Python Window) with arcpy.stats.GeographicallyWeightedRegression().  You can see the documentation for the deprecated version here:  https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/geographically-weighted-regression.htm Using the deprecated version should provide the same results as the ArcMap version.  As for why using Distance Band lowers the R-squared, I am not certain, but it likely has something to do with your particular data. Please let me know if you have any other questions. -Eric ... View more

Hi @ZhichengZhong, ArcGIS does not have a GTWR tool, but in principle, including time should not alter the question of if/how to test for significant explanatory variables.  While the GWR tools does provide significance results for local models, it's understandable that other softwares and publications do not.  The problem is that GW(T)R isn't really a single model: it is a collection of local models that are each estimated at the locations of the input features.  Further, these models are correlated with each other, as they often share the same features in their neighborhood.  This can create problems related to multiple hypothesis testing where you are effectively testing N times the number of explanatory variables, and you should definitely be cautious in interpreting any particular p-value when performing so many hypothesis tests.  This is why, generally, explanatory variables are chosen using global models like OLS, and statistics like R-squared and AIC are used to determine how much better GW(T)R does compared to the global model. ... View more

Hi @af2k24, I don't believe there is any way to do this.  If I'm understanding correctly, you're wanting to use the coefficients from the original model and use them for the new rasters.  However, EBK Regression Prediction will rebuild the coefficients for any new rasters, estimated from the input features that you provide; you can't save the coefficients are reuse them, unfortunately.  The only thing that comes to mind is trying to forecast the input point values to 2070-2099 and use them along with the forecasted rasters to get a prediction surface for 2070-2099.  Though that is obviously easier said than done, especially if you do not have historical point data to build a forecast model. -Eric ... View more

This has been included in the product plan for ArcGIS Pro 3.4.  The reimplementation will be a geoprocessing tool that creates a customized scatter plot chart on a feature layer that displays the projected scatterplot and trend line of the XZ plane. In ArcGIS Pro 3.3, you can create this scatter plot manually with customized Arcade code using these steps: On a feature layer, create a scatter plot chart by right-clicking the layer -> Create Chart -> Scatter Plot. In the Chart Properties pane, for the "Y-axis Number", provide the analysis field. In the "X-axis Number", click the "Set an expression" button to the right of the pulldown menu. Paste the Arcade code at the end of this post into the "Expression" code block (make sure that the "Language" at the top is set to "Arcade").  Change the second line of the code to any desired direction.  The direction is provided as degrees clockwise from North.  For example, 0 is north, 90 is east, 180 is south, and 270 is west. Click OK. The directional trend scatter plot will be displayed in the Chart pane.  You can click "Set an expression" button again and change the direction, and the scatter plot will update to show the trend in the new direction. To show the polynomial trend line in the scatter plot, check the "Show trend line" checkbox in the Chart Properties pane, choose "Polynomial" from the dropdown, and provide a desired "Trend Order".       // Input direction as clockwise degrees from north var angleFromNorth = 0; // Convert direction to counterclockwise radians from east var adjustedAngleDegrees = 90 - angleFromNorth; var adjustedAngleDegrees = adjustedAngleDegrees%360; var angleInRadians = adjustedAngleDegrees * PI / 180; // Return x-coordinate of rotated coordinate system return Centroid($feature).X * Cos(angleInRadians) + Centroid($feature).Y * Sin(angleInRadians)       ... View more