There are some simple ML techniques that can be used to easily predict latitude/longitude co-ordinates, such as predicting the latitude and longitude separately using two different models. However, I get the sense that this is a simple hack that doesn't give the best results. To quote another paper:
Most regression methods assume either that either only one real number is to be predicted, or if multiple real numbers are to be predicted that they are independent. The problem of predicting a point on the surface of a sphere is more complicated as the latitudes and longitudes involved are not independent.
Unfortunately, the authors of the linked paper just side-step the issue by using kNN. I'd like to use supervised learning with some non-geographical inputs (strings, numbers, etc...) to predict a latitude/longitude co-ordinate, and I'd like to approach it using "best practices" rather than a simple hack. How should I go about it? Any links to any papers or blog posts would be much appreciated. Thanks!