I am attempting to train a classifier to predict different prices for an item in different suburbs. I have several features, two of which are a latitude and longitude for the centroid of the suburb.
I am attempting to train the model to classify the price of an item in a bin of $10 size. The geospatial element will definitely affect the price of the item, however the training data I have will have gaps in it (i.e. I don't have prices for all suburbs).
What is the best way to engineer a feature that will include this geospatial information and be able to fill in the gaps in the training/test data?
So far I have tried creating new features for the bearing and distance from the capital city which seemed to work okay, as well as binning the latitude and longitude which performs worse than the bearing/distance. I did consider using a geohash, however I think that this will be too complex a feature for a classifier to understand.