A Bayesian Parametric Statistical Anomaly Detection Method for Finding Trends and Patterns in Criminal Behavior

Author

Holst, Anders ; Bjurling, Bjorn

Author_Institution

SICS Swedish ICT AB, Kista, Sweden

fYear

2013

fDate

12-14 Aug. 2013

Firstpage

83

Lastpage

88

Abstract

In this paper we describe how Bayesian Principal Anomaly Detection (BPAD) can be used for detecting long and short term trends and anomalies in geographically tagged alarm data. We elaborate on how the detection of such deviations can be used for high-lighting suspected criminal behavior and activities. BPAD has previously been successively deployed and evaluated in several similar domains, including Maritime Domain Awareness, Train Fleet Maintenance, and Alarm filtering. Similar as for those applications, we argue in the paper that the deployment of BPAD in area of crime monitoring potentially can improve the situation awareness of criminal activities, by providing automatic detection of suspicious behaviors, and uncovering large scale patterns.

Keywords

Bayes methods; behavioural sciences computing; police data processing; BPAD; Bayesian parametric statistical anomaly detection method; crime monitoring; criminal behavior pattern; criminal behavior trends; geographically tagged alarm data; situation awareness improvement; suspected criminal activities; suspected criminal behavior; suspicious behavior automatic detection; Bayes methods; Data analysis; Data collection; Data mining; Image color analysis; Market research; Monitoring; Anomaly detection; Bayesian statistics; Criminal behaviour; Situation awareness;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligence and Security Informatics Conference (EISIC), 2013 European

Conference_Location

Uppsala

Type

conf

DOI

10.1109/EISIC.2013.19

Filename

6657129