Add Interpolation analytics to ArcGIS Velocity Analytics suite

[Teresa_Blader]

Add some interpolation tools to the analytics. Like for creating on the fly air quality, or rain fall maps. Even if it means only having so many rasters cells per analysis (like the larger the area covering, the larger the raster cell has to be). Any of the tools in ArcGIS Pro already would be fine. Obviously interpolation can involve major processing... but for some projects where there's like 10 sensors collecting data in a small area, being able to display as a "heat map" (not based on density of points, but the value of the point) would be really helpful. We haven't jumped into Velocity yet, but this might be one the tools missing that keeps me from jumping on board. Are there other ways of getting around this? 

[PeterNasuti]

@Teresa_Blader Thanks so much for your suggestion! This is something we are continuing to consider for the addition of future real-time and big data tools.

Here are the related tools that are already offered in the product:

• Calculate Density
  • calculate a magnitude-per-unit area from point features that fall within a neighborhood around each cell 
  • https://doc.arcgis.com/en/iot/analyze/calculate-density.htm
• Find Hot Spots
  •  identify statistically significant hot spots and cold spots in the spatial pattern of the data using the Getis-Ord Gi* statistic
  • https://doc.arcgis.com/en/iot/analyze/find-hot-spots.htm
• Generalized Linear Regression
  • Perform Generalized Linear Regression (GLR) to generate predictions or to model a dependent variable in terms of its relationship to a set of explanatory variables. This tool can be used to fit Continuous (Gaussian), Count (Poisson), and Binary (Logistic) model types.
  • https://doc.arcgis.com/en/iot/analyze/generalized-linear-regression.htm
• Forest-based Classification and Regression
  • Create models and generates predictions using an adaptation of Leo Breiman's random forest algorithm, which is a supervised machine learning method. Predictions can be performed for categorical variables (classification) and continuous variables (regression). Explanatory variables can take the form of fields in the attribute table of the training features. In addition to validation of model performance based on the training data, predictions can be made to features.
  • https://doc.arcgis.com/en/iot/analyze/forest-based-classification-and-regression.htm