The mongrel mob or head hunters? The association between neighbourhood-level factors on different types of gang membership in Aotearoa/New Zealand

Previous research has shown that gang members typically emerge from more socially disorganised neighbourhoods. What is less known however is whether members of different types of gangs emerge from the same types of neighbourhoods. In this study, we use the social disorganisation theory as a framework to examine the spatial risk factors associated with two different types of gangs in New Zealand: Outlaw Motorcycle Gangs and New Zealand Adult Gangs. Overall, we found some consistency in spatial risk factors associated with gang membership by type in New Zealand; however, certain variables were significantly predictive of one type of gang membership but not of the other. The overall performance of our models also differed marginally depending on the type of gang being examined. In fact, our findings suggest some non-uniformity in the extent to which the various social disorganisation factors impact gang membership rates by type. The implications of this finding are discussed in the context of an ever-changing gang landscape in the country.https://journals.sagepub.com/home/ANJhj2023Geography, Geoinformatics and Meteorolog

A framework for estimating crime location choice based on awareness space

This paper extends Crime Pattern Theory, proposing a theoretical framework which aims to explain how offenders’ previous routine activity locations influence their future offence locations. The framework draws on studies of individual level crime location choice and location choice in non-criminal contexts, to identify attributes of prior activities associated with the selection of the location for future crime. We group these attributes into two proposed mechanisms: reliability and relevance. Offenders are more likely to commit crime where they have reliable knowledge that is relevant to the particular crime. The perceived reliability of offenders’ knowledge about a potential crime location is affected by the frequency, recency and duration of their prior activities in that location. Relevance reflects knowledge of a potential crime location’s crime opportunities and is affected by the type of behaviour, type of location and timing of prior activities in that location. We apply the framework to generate testable hypotheses to guide future studies of crime location choice and suggest directions for further theoretical and empirical work. Understanding crime location choice using this framework could also help inform policing investigations and crime prevention strategies.</jats:p

A new Geographic Profiling Suspect Mapping And Ranking Technique for crime investigations: GP-SMART

This study developed and tested a new geographic profiling method for automating suspect prioritisation in crime investigations. The Geographic Profiling Suspect Mapping And Ranking Technique (GP-SMART) maps suspects' activity locations available in police records—such as home addresses, family members' home addresses, prior offence locations, locations of non-crime incidents, and other contacts with police—and ranks suspects based on both the proximity and nature of these locations, relative to an input crime. In accuracy tests using solved burglary, robbery and extra-familial sex offence cases in New Zealand (n = 4511), GP-SMART ranked the offender at or near the top of the suspect list at rates greatly exceeding chance. Highlighting the benefit of its novel inclusion and differentiation of many different types of activity location, GP-SMART also outperformed baseline methods—approximating existing algorithms—that ranked suspects using only the proximity of their activity locations, or home addresses, to the input crime

A global analysis of the impact of COVID-19 stay-at-home restrictions on crime

The implementation of COVID-19 stay-at-home policies was associated with a considerable drop in urban crime in 27 cities across 23 countries. More stringent restrictions over movement in public space were predictive of larger declines in crime. The stay-at-home restrictions to control the spread of COVID-19 led to unparalleled sudden change in daily life, but it is unclear how they affected urban crime globally. We collected data on daily counts of crime in 27 cities across 23 countries in the Americas, Europe, the Middle East and Asia. We conducted interrupted time series analyses to assess the impact of stay-at-home restrictions on different types of crime in each city. Our findings show that the stay-at-home policies were associated with a considerable drop in urban crime, but with substantial variation across cities and types of crime. Meta-regression results showed that more stringent restrictions over movement in public space were predictive of larger declines in crime.Peer reviewe

Does crime count? Investigating the association between neighbourhood-level crime and recidivism in high-risk parolees

The neighbourhood contexts in which former offenders live following their release from prison has been relatively neglected in recidivism studies. Moreover, the relationship between neighbourhood-level crime and parolee recidivism has received little scholarly attention. This oversight is of concern since high-crime neighbourhoods may influence newly-released prisoners' ability to assimilate and reintegrate effectively within society. In this study, we examine whether neighbourhood-level crime across four different categories (dishonesty, violence, property damage, and drugs and anti-social) predicts individual-level short-term recidivism. Using data from 280 high-risk male parolees returning to neighbourhoods throughout New Zealand between 2010 and 2013 we examine whether neighbourhood-level crime is associated with their reconviction. Results showed no significant associations between crime and short-term recidivism after controlling for various potential individual- and neighbourhood-level confounds. We contrast the surprising results of the research with the predominantly US-centric recidivism literature, and identify and discuss possible explanations for our non-significant findings.The Parole Project database used in this study was supported by research funding and awards to Prof Devon Polaschek from Victoria University of Wellington and the New Zealand Department of Corrections, and by a Fulbright New Zealand award.https://www.elsevier.com/locate/apgeog2020-01-01hj2019Geography, Geoinformatics and Meteorolog

Gang membership and gang crime in New Zealand : a national study identifying spatial risk factors

In this exploratory study, we identify the spatial risk factors associated with gang membership and gang crime in New Zealand using social disorganization as a theoretical framework. Gang membership data from the Gang Intelligence Center and gang crime data from New Zealand Police are included in spatial regression models to identify risk factors. Overall marginal support was found for the use of social disorganization constructs to explain gang membership and gang crime in New Zealand. Higher deprivation and higher diversity were both found to be associated with gang membership and gang crime, respectively. Some similarities and notable differences were observed between our results and the mainly U.S.-centric results of past spatial gang research. This study allows for a greater understanding of the generalizability of the social disorganization theory to explain gang membership and gang crime in areas with markedly different cultural perspectives and ethnocentricities to the United States.https://journals.sagepub.com/home/cjbhj2022Geography, Geoinformatics and Meteorolog

A new Geographic Profiling Suspect Mapping And Ranking Technique for crime investigations: GP-SMART

This study developed and tested a new geographic profiling method for automating suspect prioritisation in crime investigations. The Geographic Profiling Suspect Mapping And Ranking Technique (GP-SMART) maps suspects' activity locations available in police records—such as home addresses, family members' home addresses, prior offence locations, locations of non-crime incidents, and other contacts with police—and ranks suspects based on both the proximity and nature of these locations, relative to an input crime. In accuracy tests using solved burglary, robbery and extra-familial sex offence cases in New Zealand (n = 4511), GP-SMART ranked the offender at or near the top of the suspect list at rates greatly exceeding chance. Highlighting the benefit of its novel inclusion and differentiation of many different types of activity location, GP-SMART also outperformed baseline methods—approximating existing algorithms—that ranked suspects using only the proximity of their activity locations, or home addresses, to the input crime