I am trying to predict crime. I have data with factors: location, keyword description of the crime, time crime occurred and so on. This is for crimes that occurred in the past.
I would like to treat the prediction of crimes as a binary classification problem. In this model, the data I have collected would form the "positive" examples: they are all examples of a crime happening. However, I am unsure what to use for the negative examples.
Obviously, most of the time there is no crime at the location, but can I use this as negative data? For example, if I know there was a crime at 7pm at location X, and no other crimes there, should I generate new negative data points for every hour except 7pm?
Ideally, I want to create probabilities of crime based on a set of factors.