Title :
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
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;
Conference_Titel :
Intelligence and Security Informatics Conference (EISIC), 2013 European
Conference_Location :
Uppsala
DOI :
10.1109/EISIC.2013.19
