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.

翻译：从统计学角度看，犯罪数据具有某些特殊性，这引发了学界对其分析日益增长的兴趣。特别是，部分财产犯罪常呈现的一个特性是：其确切发生时间存在不确定性，通常仅能获得一个限定事件发生的时间窗口。针对此类区间删失观测数据，存在多种处理方法，其中多数基于事件时间插补。另一种替代方案是进行非精确分析，其原理是将区间内每个时间单位赋予相同权重，以限定事件的不确定性边界。然而，该方法存在局限性。本文提出一种基于逻辑回归的时空模型，遵循非精确方法的核心理念，能够分析具有时间不确定性的犯罪数据。该模型从贝叶斯视角构建，可容纳观测数据中的时间不确定性。我们将此模型应用于西班牙瓦伦西亚市记录的住宅入室盗窃数据集，并将其结果与完整案例模型（即剔除时间不确定事件后的模型）进行对比分析。