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From a statistical point of view, crime data present certain peculiaritiesthat have led to a growing interest in their analysis. In particular, acharacteristic that some property crimes frequently present is the existence ofuncertainty about their exact location in time, being usual to only have a timewindow that delimits the occurrence of the event. There are different methodsto deal with this type of interval-censored observation, most of them based onevent time imputation. Another alternative is to carry out an aoristicanalysis, which is based on assigning the same weight to each time unitincluded in the interval that limits the uncertainty about the event. However,this method has its limitations. In this paper, we present a spatio-temporalmodel based on the logistic regression that allows the analysis of crime datawith temporal uncertainty, following the spirit of the aoristic method. Themodel is developed from a Bayesian perspective, which allows accommodating thetemporal uncertainty of the observations. The model is applied to a dataset ofresidential burglaries recorded in Valencia, Spain. The results provided bythis model are compared with those corresponding to the complete cases model,which discards temporally-uncertain events.

A Bayesian aoristic logistic regression to model spatio-temporal crime risk under the presence of interval-censored event times