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							     Ok, so projection is not the problem ... at the top of the Geoprocessing pane in your screenshot, it says there are pending edits.  I couldn't reproduce this either though.  Let us know if the problem still persists! We are still looking into it.    -Jenora       
						
					
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		10-20-2020
	
		
		10:19 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Hi Stephen,     I would make sure that the training points and the rasters are maintaining the same projection and ensure that is not the problem (If it is, thank you for finding this bug.  It shouldn't traceback.).  It also seems like you have edits that are pending and not saved (probably not the actual problem).  We are looking into this and will try to reproduce the traceback.  Let me know if either of those suggestions work for you or if you are running into the same error.     -Jenora D’Acosta   
						
					
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		10-15-2020
	
		
		11:15 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Hello!    Different results can happen for a couple different reasons.   - Parallel processing can affect precision of the analysis which can create slightly different results from run to run.  - Permutations will also create slightly different results.  You can fix this in one of 2 ways.  You can set the permutations parameter to 0 or you can use the Random Number Generator Environment.  If you set the random seed to anything other than 0 (let's say 2 and then use the seed 2 every time), you should receive the same results every run.      Hope this was helpful.  Let me know if you have any other questions.  -Jenora   
						
					
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		01-29-2020
	
		
		02:05 PM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Hi there!     As you point out there are significant differences between the two maps you shared above, but this is because the outputs are based on two very different methodologies, standard GWR and multiscale GWR.  We would not expect the two outputs to be the same.  Currently, we have not implemented multiscale GWR.  Our implementation of standard GWR closely matches the implementation in open source packages such as GWR4.  We do not provide an option to standardize the variables within our implementation of GWR (this is done primarily for computational efficiency).  We are intrigued by the multiscale GWR methodology and it is on our list of research and potential enhancements.     Documentation for the tool in ArcGIS Pro can be found here:  Geographically Weighted Regression  How Geographically Weighted Regression Works     References:  Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4), 281-298.  Fotheringham, Stewart A., Chris Brunsdon, and Martin Charlton. Geographically Weighted Regression: the analysis of spatially varying relationships. John Wiley & Sons, 2002.  I hope this is helpful and please reach out if there are any other questions!  - Jenora D'Acosta   
						
					
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		09-06-2019
	
		
		10:10 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Hi Jorma!  We updated the site for 2.4 just the other day.  The add-in should be first on the list.  Let me know if you still don't find it there.  -Jenora   
						
					
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		07-17-2019
	
		
		01:47 PM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Hi Martin,  Just to clarify, the Random Trees Classifier in 10.5 works with rasters and is using the Random Forest algorithm as in other software.  The documentation is here:  http://desktop.arcgis.com/en/arcmap/10.5/tools/spatial-analyst-toolbox/train-random-trees-classifier.htm    The Forest-based Classification and Regression tool was added to Pro 2.2 and is not in ArcMap 10.5.  It works with both raster and vector data using the same Random Forest algorithm.  The documentation is here:  https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/forestbasedclassificationregression.htm    Hope this is helpful,  Jenora   
						
					
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		04-30-2019
	
		
		11:57 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Both are correct!  The Mann-Kendall statistic is calculated for different things depending on which tool you are using.    1- When you are initially creating your cube (using either the Create Space Time Cube by Aggregating Points or Create Space Time Cube from Defined Locations tools), the Mann-Kendall is calculated for the aggregated values you are bringing into the cube.    2- When you are using the Emerging Hot Spot Analysis tool, the Mann-Kendall is calculated for the z-scores at each location.    Hope this helps!  Let me know if you have any other questions.    -Jenora   
						
					
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		07-09-2018
	
		
		01:02 PM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Hello Jimmy, I had the programmer take a look at this issue and it seems that the issue lies in the xyDomain variable ... there seems to be a Unicode character inside it.  This variable needs to be able to be transformed from a string to a float.  You could try to change only the bolded line below in the code for the tool and see if that fixes it.  It would also help if you provided the data that you are using, a portion of the data with the spatial reference that is causing the traceback or at least letting us know the spatial reference you are using so we can look into it and try to fix it for the future.  Let us know if this works for you!  Thank you!    -Jenora      def strToFloat(floatAsStr😞      """Robust Methodology to Convert to and From Alternative Locale Decimals.         INPUT:      floatAsStr (str): numeric rep of a float         RETURN:      value (float): resulting float      """         if isNumeric(floatAsStr😞          return float(floatAsStr)      else:          sep = LOCALE.localeconv()['decimal_point']          sepTypes = [",", "."]          sepTypes.remove(sep)          if sep in floatAsStr:              return LOCALE.atof(UTILS.decodeString(floatAsStr))          else:              if sepTypes[0] in floatAsStr:                  newStr = floatAsStr.replace(sepTypes[0], sep)                  return LOCALE.atof(newStr)              else:                  return float(floatAsStr)   
						
					
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		05-15-2018
	
		
		10:51 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Hi!  Can you tell me if you are using a language pack?   
						
					
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		02-05-2018
	
		
		09:18 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Dr. Gudes, I couldn't repro this myself, but I responded to your email and am happy to look into this further. -Jenora   
						
					
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		10-09-2017
	
		
		01:38 PM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Hmmm, looking into this now ... will reach out soon.  Are you by any chance running the tool on a selection set?    -Jenora   
						
					
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		10-05-2017
	
		
		09:47 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Hello!    I am moving this to Spatial Stats and will look into it in the morning.  Can you tell me if you are using a language pack?      -Jenora   
						
					
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		07-19-2017
	
		
		06:30 PM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     Hello,    The tool output does include z_i for each feature, but not the Lag z_i (this would be a nice enhancement and I will bring it up with the team.)  You can calculate the average for each neighborhood by:  - Create a .swm file (Generate Spatial Weights Matrix) with the neighborhood relationships   - Convert the Spatial Weights Matrix to Table  - Then you can join this table to the feature class based on the neighbor ID  - Then the neighborhood averages can be summarized (Summary Statistics) and averaged based on each feature's original ID    Hope this helps,   Jenora   
						
					
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		07-19-2017
	
		
		09:38 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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							     slynch-esristaff or EKrause-esristaff Any insight?   
						
					
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		07-06-2017
	
		
		11:01 AM
	
	
	
	
	
	
	
	
	
	
	
	
	
	
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