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

Applications;Methodology