Detecting Emerging Space-Time Crime Patterns by Prospective STSS

Detecting crime patterns as they emerge in both space and time can enhance situational awareness amongst security agents and prevent epidemics of crimes in potential problematic areas (Neill and Gorr, 2007). Amongst others, space-time scan statistics (STSS) (Kulldorff et al. 2005) and space -time kernel (Nakaya and Yano, 2010) have been widely used in crime analysis. Stemmed on strong statistical theories, the STSS could provide the significance of the purported crime clusters, and this continues to gain huge popularities for crime hotspot analysis (LeBeau, 2000; Neill and Gorr, 2007; Uittenbogaard and Ceccato , 2011; Cheng and Williams, 2012; Gao et al. 2012). All these works applied STSS to crime clusters detection in a retrospective manner where all clusters within certain frame of time are detected. The approach was found to be very effective for historic analysis of crime outbreaks and near-repeat victimization. However, most STSS-based hotspot analyses were conducted at either region-wide and/or at monthly temporal granularity. This is not appropriate for city-based policing, which requires detailed spatial (local or micro) and temporal (daily) analysis. Few studies have actually attempted prospective detection of clusters with the aim of capturing their growth (emergence) in both space and time simultaneously so as to facilitate early prevention of the phenomenon in question. This was however seen only in epidemiology where outbreaks of diseases were detected employing this approach using the over-the-counter drug sales in Allegheny County from 2/13/04 2/12/05 (Neill et al., 2005). However, there is no quantitative evaluation of the significance of the emerging patterns and the rapidness of their emergence. Therefore, the aim of this research is to explore a prospective detection of emerging crime patterns at detailed spatial and temporal scales so as to facilitate proactive policing. In particular, we use the permutation STSS for the detailed crime emerging pattern detection and evaluate their significance as well as the rapidness of detection, by comparing the results with that of retrospective analysis.