How do I use JMP Pro to do machine learning predictions on a dataset with predictors that include latitudes/longitudes (i.e. a geospatial dataset)?

[#1]

Hi - I would like to do a machine learning algorithm in JMP Pro 17 to predict Y based on 7 variables (X1, X2, ...X7), two of which are spatial coordinates (latitude and longitude) and one of which is a date.  I would use a portion of my records as a training set, and a portion as my verification/testing set.   Is JMP Pro equipped to machine learn on this geospatial (lat/long) dataset?   And, if so, could you recommend a good starting point or test type for my workflow?  I am using JMP Pro 18.

Thanks in advance!

[#2]

Geospatial modeling or kriging often uses Gaussian Process models. They are available under Analyze > Specialized Modeling.

[#3]

Thank you, Mark.  I experimented with this yesterday but realized that I need to better understand this type of model (Gaussian Process) to properly interpret the results.  I'm curious though if this is the only geospatial modeling option in JMP Pro?  I have found very little in the JMP literature on the use of geospatial coordinates for predictions, and yet this seems like such a basic component to understanding, for example, environmental data.  I'm probably missing something right in front of me!  

[#4]

This is a broad question within a broad field. JMP does not strive to excel in this field, but many modeling platforms and add-ins could be applied. It makes sense to me to start with researching the field and then check to see if JMP provides appropriate methods.