Handling geospatial coordinates in machine learning

Question

I'm building a machine learning model where some columns are physical addresses (which I can translate into X / Y coordinates) but I'm a little bit confused on how this will be handled by the ML algorithm. Is there a particular way to translate a GEO location into columns for use into ML (classification and/or regression) ?

Thanks in advance !

Answer 1

The choice of features would, in general, depend on what kind of relationship you anticipate between the features and the target variable. You are right in saying that post code number itself does not bear any relation to the target. Here the postcode is simply a string, or a category. What kind of model are you planning to use? Linear regression and Decision tree are two examples. These models capture relationships in different ways. As an example for a feature, you could compute the straight line distance between the source and destination, and use that in the model, since intuitively, the farther they are, the higher the transit time is likely to be. What else does the transit time depend on? See if you can relate the factors influencing the travel time to the information that you have, i.e., the postcodes / XY co-ordinates, in some way.