I want to build a machine learning model to choose a location for the next vending machine in Boston. I want to divide the map into small neighborhood, and I came up with features that are related to the geospatial locations and user information.
One paradox that concerns me is that: we'll use past vending machines' features (X) and sales (y) to train the model; in that case, the model that predict the next vending machine will likely choose some place very close to the old vending machine locations. But at the same time, we wouldn't want to build a vending machine in exactly the same location as an older one.
Additionally, vending machines are usually built one by one as time goes by; the model used to choose the 10th vending machine in the city, and the 1300th vending machine could be very different. This almost made me think this is a time-series problem. How to build a model that can take these problems into consideration? Any insight on features, logistics, or models are all deeply appreciated!