I have geolocational data(coordinates and times with device id), I can bucket this down using say 5m by 5m squares to represent a vertice on a graph. Then following the device id and creating edges chronologically creates a walk(or a graph). I also have a label for each walk, either a True or a False on whether they visited a particular location or not.
I want to use a supervised machine learning method to use this data to predict whether future walks would be a True or a False. One idea that I'm currently exploring is to generate a graph kernel with unit edge weight and try logistic regression or support vector machines on that.
Does anyone else have any ideas on other machine learning methods that I could explore? Is there a way of leveraging the time-series data such that I don't have to even create the graph? I should also mention that I have data on walks that I don't know if they correspond to a True or a False as well, so a semi-supervised method would be welcome as well. Thanks