A crime prediction model based on spatial and temporal data

In a world where data has become precious thanks to what we can do with it

like forecasting the future, the fight against crime can also benefit from this

technological trend. In this work, we propose a crime prediction model based

on historical data that we prepare and transform into spatiotemporal data by

crime type, for use in machine learning algorithms and then predict, with

maximum accuracy, the risk of having crimes in a spatiotemporal point in the

city. And in order to have a general model not related to a specific type of

crime, we have described our risk by a vector of n values that represent the

risks by type of crime.