Context. In year 2013 the number of reported residential burglariesin Sweden was 21000, where only 4-5 percent of those actuallygot solved [1]. The Swedish police is trying to improve their way ofworking to increase the number of solved burglaries, this by structuringthe data collection and analysing with computer science methods.Temporal analysis is the key to gure out when crime actually takesplace. Objectives. This thesis study ve dierent methods for analysingthe temporal data of residential burglaries. The temporal analysis isperformed on three time spans: time of day, day of the week and dayof the month. The objective is to evaluate the ve methods in thethree time spans and decide which method is the most suitable foreach of them. Methods. This study includes three experiments testing all ve methodson the three time spans. The experiments focus on comparing theobserved data against the data of burglaries with a known specictime of the crime. In order to test the performance of each method aChi-squared goodness-of-t test was used, as well as a visual comparisonof the produced plots. Results. The results showed that the Aoristic-method was the mostsuitable method to use when analysing temporal data of residentialburglars, if looking at the time of day, day of the week and day ofthe month. Using the methods we also generated plots of the threetemporal distributions, with an R script. Conclusions. We concluded that using the Aoristic-method is themost suitable method to use to generate plots from the temporal data.We also concluded that using this script with the Aoristic-method togenerate plots, would make it possible for the police to resource allocationaccording to when burglaries actually take place.

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