Can topic modeling be used to solve the kaggle SF Crime challenge? • /r/MachineLearning

The SF Crime challenge: the training set consists of crime-events labeled by a date/time, the police district it occurred in, the lat-lon coordinates, an address, and the crime-category. You're asked to predict the crime-category in a test set. Could one think of the date/time features as "words" generated in a police district (or perhaps yearly/monthly/hourly) "document" with a distribution of crime-category "topics" in the spirit of LDA for document classification? If I'm on the right track, but don't quite have the analog of graphical model elements quite right, could you explain?