What would be a good machine learning model to predict the next vending machine location? 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!

@whuber made a very good point: "Placing a new vending machine 'cannibalizes' sales from nearby ones". Cannibalization may be desirable or not, depending on whether the OP is eating the market of his competitor, or himself.
Instead of a writing a machine learner to predict locations, it might be more useful to simulate vending machines. We have a City, say 東京, with existing vending machines owned by a number of companies (maybe try modelling as 2, Us and Them). Initially I imagine that the obvious good locations, such railway stations,are taken. We have a number of graphical views, such number of potential customers, distance to nearest machine, revenue of each machine, etc. Now you can model the effects of new locations, and new products, subject to whatever assumptions you may be making. Next step might be allowing the simulation to try out new locations, Monte Carlo style.
