Abstract
ABSTRACTThe spatial patterning of crime hotspots provides place-based information for the design, allocation, and implementation of crime prevention policies and programmes. However, most spatial hotspot identification methods are univariate, analyse a single crime type, and do not consider if hotspots are shared amongst multiple crime types. This study applies a Bayesian spatial shared component model to identify crime-general and crime-specific hotspots for violent crime and property crime at the small-area scale. The spatial shared component model jointly analyzes both violent crime and property crime and separates the area-specific risks of each crime type into one shared component, which captures the underlying crime-general spatial pattern common to both crime types, and one type-specific component, which captures the crime-specific spatial pattern that diverges from the shared pattern. Crime-general and crime-specific hotspots are classified based on the posterior probability estimates of the shared and type-specific components, respectively. Results show that the crime-general pattern explains approximately 81% of the total variation of violent crime and 70% of the total variation of property crime. Crime-general hotspots are found to be more frequent than crime-specific hotspots, and property crime-specific hotspots are more frequent than violent crime-specific hotspots. Crime-general and crime-specific hotspots are areas that may be targeted with comprehensive initiatives designed for multiple crime types or specialized initiatives designed for a single crime type, respectively.
Highlights
Crime offences exhibit non-random spatial patterns and often concentrate at hotspot locations (Eck and Weisburd 1995; Ratcliffe and McCullagh 1999; Anselin et al 2000)
The multivariate shared component model used in this paper jointly analyzes two crime types and separates the area-specific risks of the crime types into a shared component, which captures the crime-general spatial pattern, and type-specific components, which capture the crime-specific spatial patterns that diverge from the crime-general pattern
This study found that both violent crime and property crime had similar associations with the underlying crime-general pattern, that the crime-general pattern explained the largest proportion of variation for both crime types, and that crimegeneral hotspots were more frequent than both types of crime-specific hotspots
Summary
Crime offences exhibit non-random spatial patterns and often concentrate at hotspot locations (Eck and Weisburd 1995; Ratcliffe and McCullagh 1999; Anselin et al 2000). Identifying crime hotspots is central to theoretical development, crime prevention policy, and law enforcement resource allocation. From a policy development and resource allocation perspective, hotspots are locations that may be suitable for community-based crime prevention strategies, which work to change the local social dynamics, institutions, and organizations that influence criminal behaviour (Herbert and Harries 1986; Tonry and Farrington 1995), and/or geographically focused law enforcement interventions, such as hotspot policing or problem-oriented policing, which aim to modify the places, situations, and opportunities that facilitate crime events (Braga et al 1999; Ratcliffe 2004; Chainey, Tompson, and Uhlig 2008; Wang 2012). Because univariate hotspot identification methods focus on only one outcome, they do not account for the underlying data-generating processes shared amongst multiple crime types and do not distinguish between crime-general hotspots, or areas where there are unusually high levels of two or more crime types, and crime-specific hotspots, or locations with unusually high levels of only one crime type
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
