Non-local crime density estimation incorporating housing information.

ABSTRACT: Given a discrete sample of event locations, we wish to produce a probability density that models the relative probability of events occurring in a spatial domain. Standard density estimation techniques do not incorporate priors informed by spatial data. Such methods can result in assigning significant positive probability to locations where events cannot realistically occur. In particular, when modelling residential burglaries, standard density estimation can predict residential burglaries occurring where there are no residences. Incorporating the spatial data can inform the valid region for the density. When modelling very few events, additional priors can help to correctly fill in the gaps. Learning and enforcing correlation between spatial data and event data can yield better estimates

SUBMITTER: Woodworth JT 

PROVIDER: S-EPMC4186253 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature