A Bayesian spatial shared component model for identifying crime-general and crime-specific hotspots

Abstract

The 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 crimespecific 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.

The Spatial Overlap of Police Calls Reporting Street-Level and Behind-Closed-Doors Crime: A Bayesian Modeling Approach

“…This study aimed to analyze whether there was a shared spatial distribution of police calls reporting street-level crime and IPVAW. A Bayesian joint model was performed in line with previous research that incorporates a multivariate spatial analysis to study crime outcomes [40][41][42][43]51,52]. In addition, two different Poisson regression models were conducted to assess the spatial similarity of relative risks.…”

“…In addition, police calls do not distinguish between single and multiple calls referring to the same incident and/or the same actors, which leads to a possible data dependency bias [24]. Other useful measures such as police-report records of criminal offenses [21,24,41,66], assaults resulting in hospitalizations [12], IPV-related medical records and hospitalizations [38], and self-reported IPV were not available. In addition, violence behind closed doors was measured using IPVAW; however, other IPV types, such as violence perpetrated by female partners or same-sex couples were not incorporated in this analysis, and thus the results are not generalizable to those types of IPV.…”

“…In this regard, new methodological approaches have emerged in the past years. For example, some studies have explored the common spatial patterns of different outcomes related to crime using multivariate spatial techniques [40][41][42][43]. These models aim to understand separately the potentially common spatial component and the two specific spatial patterns [43]; thus, they allow joint assessment of the spatial distribution of two or more types of crime [40,44,45].…”

The impact of crimes on house prices in LA County

“…In a recent study by Law et al (2020), they followed the approach put forward by Anselin et al (2000) and utilised Bayesian multivariate spatial models to identify general and crime specific hotspots in Ontario, Canada. Their research showed evidence demonstrating that crimes are concentrated; therefore, the majority of crime-general clusters had very strong evidence of being a hotspot.…”

Spatial Statistical Models: An Overview under the Bayesian Approach

“…It has been used to identify the impact of economic, social, and demographic factors on the spatial variability of the household share of economic distress [51]; identify the spatial structure of the calls to the Portuguese health line, accounting for the demographics, socio-economic information, and characteristics of the health systems [52]; identify clustering in severe mobility crash risk and diagnosing of active transportation safety issues [53]. Moreover, it has been employed in the analysis of spatial patterns and hotspot detection of violent and property crimes at a small spatial scale in Toronto, Canada [54]; map the main features of fertility, such as timing, pace, and scale, and to detect spatial disparity in fertility transition in Brazil [55].…”