Evaluating Temporal Approximation Methods Using Burglary Data

Lukas Oswald,Michael Leitner

doi:10.3390/ijgi9060386

Abstract

Law enforcement is very interested in knowing when a crime has happened. Unfortunately, the occurrence time of a crime is often not exactly known. In such circumstances, estimating the most likely time that a crime has happened is crucial for spatio-temporal analysis. The main purpose of this research is to introduce two novel temporal approximation methods, termed retrospective temporal analysis (RTA) and extended retrospective temporal analysis (RTAext). Both methods are compared to six existing temporal approximation methods and subsequently evaluated in order to identify the method that can most accurately estimate the occurrence time of crimes. This research is conducted with 100,000+ burglary crimes from the city of Vienna, Austria provided by the Criminal Intelligence Service Austria, from 2009–2015. The RTA method assumes that crimes in the immediate past occur at very similar times as in the present and in the future. Historical crimes with accurately known time stamps can therefore be applied to estimate when crimes occur in the present/future. The RTAext method enhances one existing temporal approximation method, aoristicext, with probability values derived from historical crime data with accurately known time stamps. The results show that the RTA method performs superiorly to all other temporal approximation methods, including the novel RTAext method, in two out of the three crime types analyzed. Additionally, the RTAext method shows very good results that are similar to the best performing existing approximation methods.

Highlights

Crime has an inherent geographical and temporal quality, because when a crime occurs it happens at a certain place and a certain time [1]
Contributing to research that attempts to derive more accurate temporal information from crime data that possesses inaccurate time stamps was the main motivation for writing this article
Using a data set of 105,578 crimes across three crime types from the city of Vienna, Austria, eight different temporal approximation methods were applied to more accurately calculate time stamps for crimes with inaccurate temporal information

Summary

Introduction

Crime has an inherent geographical and temporal quality, because when a crime occurs it happens at a certain place and a certain time [1]. It should be noted that when spatio-temporal data are investigated, GIS, as well as other crime analysis tools, have mainly focused on analyzing the geographic component [3]. The police, for example, has recognized the inherent geographical component of crime by sticking pins into maps, which are displayed on walls [16]. This technique is identical to the principle of computer-based GIS applications, where each pin represents a point (e.g., crime location). Tools were created that identify spatial patterns and concentrations of crime, or relationships between crimes and the environment where crimes occur Along with this development, the temporal aspect of crime pattern analysis has received less

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