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Much attention has focused on the motivation for robbery and robbery in different contexts, but an understanding of young robbery careers in an urban setting, and the pathways connected to it, have remained absent from the literature. In this paper, I give four case studies of street robbery careers highlighting key turning points and pathways into and out of street robbery and gangs. I will show how robbery, gang affiliation and participation in ‘street culture’ have implications for progression through robbery careers into other criminal activities.

In Hong Kong, robberies occurring in the elevators in high-rise buildings were identified as a particular problem and the purpose of this paper is to address a dedicated programme aimed at reducing such offences.

A comparison of elevator-based robbery statistics over a ten-year policy-implication period, in two police districts, one with a high rate of public ownership and a traditionally high rate of elevator robberies, the second with a higher rate of private ownership and a traditionally lower rate of such robberies.

The evidence suggests that the programme was successful, with such robberies declining significantly and with no evidence of displacement.

This research is based on police statistics. There is a need for more research, for example, investigating residents’ involvement with the system and the extent to which it encouraged shared ownership of the initiative.

What is clear is that, where in the West the Newman legacy led to the demonisation of high-rise public sector housing, in Hong Kong, where there is no viable alternative, the use of CCTV helped transform such areas into safer communities.

The rate of robbery, most notably elevator-based robbery, fell dramatically, improving community safety.

Though there has been a considerable amount of research on the impact of CCTV on crime, almost all of this has focussed on Western industrial societies and little of it has addressed robbery. To the best of authors’ knowledge, this research is the first in Hong Kong, and the first to evaluate the impact of CCTV on robbery.

The purpose of this study is to identify potential changes in crime generators and attractors based on monthly models in a high-tourist destination.

A risk terrain modeling approach was used to assess spatial relationships between 27 crime generator and attractor types in Atlantic City, New Jersey with robbery occurrence for the 2015 calendar year. In total, 12 separate monthly models were run to identify changes in risk factors based on the month of the year.

Results indicated unique significant risk factors based on the month of the year. Over the warmer and summer months, there was a shift in environmental risk factors that falls in line with more of a change in routine activities for residents and tourists and related situational contexts for the crime.

The analytical approach used in the current study could be used by police departments and jurisdictions to understand types of crime generators and attractors influencing local crime occurrence. Subsequent analyses were used by Atlantic City Police Department to direct place-based policing efforts.

With growing crime and place research that accounts for temporal scales, the authors advance these endeavors by focusing on a tourist destination, Atlantic City, New Jersey.

The purpose of this paper is to analyze the design and implementation of the police crackdown strategy employed in Mexico City and to discuss its limitations toward a medium-to-long-term reduction of crime rates for six types of robberies.

The present work employs generalized autoregressive conditional heteroscedasticity (GARCH) models to estimate the effect of police operations on the volatility of the rates of six types of robberies in Mexico City, as well as their persistence over time.

Results suggest that the concentration of policing in certain high-criminality spaces reduces crime rates in the immediate term; however, its permanence is contingent on policing design and behavioral characteristics of the targeted crime. Specifically, the Mexico City police crackdown strategy seems to be better suited for combating crimes of a “non-static” nature than those of a “static” nature.

Due to the nature of the data used for this research, the performed analysis does not enable a precise determination of whether the crime rates respond to temporal or spatial displacement.

Considering the obtained results, a re-design of Mexico City’s police crackdown strategy is suggested for the sustained reduction of the number of reported cases of robberies of a static nature.

Despite their importance, few studies have measured the impact of police crackdowns on city-level crime rates and whether their effect is temporary or permanent. The present study proposes the use of GARCH models in order to integrate the study of this phenomenon into criminal time series models.

The purpose of this paper is to explore how young people who offend with others define delinquent and criminal groups and consider the social risk factors associated with gang membership and criminal exploitation.

The sample consisted of 15 young people who were purposively sampled from a group of 14- to 17-year-old males who had been identified as at risk of gang involvement and referred to a community-based programme. Using a social identity framework, a thematic analysis was undertaken to investigate how the participants viewed their role in offending as part of a group.

The participants identified peer groups, street gangs and the involvement of adult criminals as distinct categories of offending groups. Unlike prior models for gang involvement, some members of the sample were involved in multiple groups to perform different categories of crime. Importantly, participants displayed an awareness of exploitation and described successful exit strategies from criminal groups.

Understanding how young people who are involved in delinquent behaviour and offending define gang and group offending.

The implications for gang and group offending prevention and intervention programmes are discussed.

The literature on child criminal exploitation and UK drug markets is in its infancy. This paper offers further evidence for the processes of joining and leaving delinquent and criminal groups.

The purpose of this paper is to develop a methodology for knowledge discovery in emergency response service databases based on police occurrence reports, generating information to help law enforcement agencies plan actions to investigate and combat criminal activities.

The developed model employs a methodology for knowledge discovery involving text mining techniques and uses latent Dirichlet allocation (LDA) with collapsed Gibbs sampling to obtain topics related to crime.

The method used in this study enabled identification of the most common crimes that occurred in the period from 1 January to 31 December of 2016. An analysis of the identified topics reaffirmed that crimes do not occur in a linear manner in a given locality. In this study, 40 per cent of the crimes identified in integrated public safety area 5, or AISP 5 (the historic centre of the city of RJ), had no correlation with AISP 19 (Copacabana – RJ), and 33 per cent of the crimes in AISP 19 were not identified in AISP 5.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary manner to the identification of criminal practices and their characteristics based on police occurrence reports stored in emergency response databases. The generated knowledge enables law enforcement experts to assess, reformulate and construct differentiated strategies for combating crimes in a given locality.

The production of knowledge from the emergency service database contributes to the government integrating information with other databases, thus enabling the improvement of strategies to combat local crime. The proposed model contributes to research on big data, on the innovation aspect and on decision support, for it breaks with a paradigm of analysis of criminal information.

The originality of the study lies in the integration of text mining techniques and LDA to detect crimes in a given locality on the basis of the criminal occurrence reports stored in emergency response service databases.

The purpose of this paper is to develop a method for the discovery of knowledge in emergency response databases based on police incident reports, generating information that identifies local criminal demands that allow the selection of the appropriate policing strategies portfolio to solve the problem.

The developed model uses a methodology for the discovery of knowledge involving text mining techniques using Latent Dirichlet Allocation (LDA) integrated with the ELECTRE I multicriteria method.

The developed method allowed the identification of the most common criminal demands that occurred from January 1 to December 31, 2016, in the policing areas studied. One of the crimes does not occur homogeneously in a particular locality. In this study, it was initially observed that 40 per cent of the crimes identified in the Integrated Public Safety Area 5, or AISP-5, (historical city center of RJ) had no correlation with AISP-19 (Copacabana - RJ), and 33 per cent of crimes crimes in AISP-19 were not identified in AISP-5. This finding guided the second part of the method that sought to identify which portfolio of policing strategies would be most appropriate for the identified demands. In this sense, using the ELECTRE I method, eight different scenarios were constructed where it can be identified that for each specific criminal demand set there is a set of policing strategies to be applied.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary way to the identification of criminal practices and their characteristics based on reports of police occurrences stored in emergency response databases. The knowledge generated through the identification of criminal demands allows law enforcement decision makers to evaluate and choose among the available policing strategies, which best suit the reality they study, and produce the reduction of criminal indices.

It is possible to infer that by choosing appropriate strategies to combat local crime, the proposed model will increase the population’s sense of safety through an effective reduction in crime.

The originality of the study lies in the integration of text mining techniques, LDA and the ELECTRE I method for detecting crime in a given location based on crime reports stored in emergency response databases, enabling identification and choice, from customized policing strategies to particular criminal demands.

Ethnography has been an important research method that has given insight into ‘dangerous’ and ‘problematic’ populations. Yet, ethnographic methods with such populations are increasingly rare as the governance of social science research takes on an ever more intensified ‘risk assessment’ approach. Based on projects that made use of ethnographic methods undertaken from 2004 to 2008, this paper will try to offer some methodological reflections on working with ‘dangerous’ and ‘problematic’ populations such as mentally ill adults, those with antisocial behaviour orders (ASBOs), crack cocaine users and gangs. It will call for greater consideration to be given to the use of ethnographic methods with such populations to inform policy and practice.

Intelligence-led policing (ILP) involves the analysis of data to inform the development and implementation of strategic actions aimed at more efficiently reducing crime. The purpose of this paper is to examine how chronic acquisitive offenders – a focus of ILP – respond to police patrol, and how this knowledge can be turned into actionable strategies to reduce crime.

Interviews were conducted with 137 chronic offenders who had multiple convictions for burglary, robbery and/or vehicle crime. The interviews involved the collection of both qualitative and quantitative data, including responses to situational crime vignettes.

When encountering police patrols, criminals were initially more likely to displace (e.g. committing crime elsewhere and/or later in the day) than to desist from offending. Some of the conditions under which police patrol was most effective were identified, including offenders’ fear of being recognized by officers. Repeated thwarted crime attempts appeared to be most impactful, with even the most chronic offenders becoming “worn down.”

The profiles of top offenders should be systematically disseminated to front line officers to augment the effectiveness of police patrol and minimize the possibility of crime displacement.

Offender interviews are a valuable source of information but they have been underutilized within an ILP framework. This research illustrates how offender interview research can inform and support the role of police in preventing crime.

A growing body of literature focuses on crime hotspots; however, less is known about the spatial distribution of crime at mass transit systems, and even less is known about trajectory patterns of hotspots in non-English-speaking countries.

The spatiotemporal behaviour of 1,494 crimes reported to the Hong Kong’s Railway Police District across a two-year period was examined in this study. Crime harm weights were then applied to offences to estimate the distribution of crime severity across the transit system. Descriptive statistics are used to understand the temporal and spatial trends, and k-means longitudinal clustering are used to examine the developmental trajectories of crime in train stations over time.

Analyses suggest that 15.2% and 8.8% of stations accounted for 50% of all counted crime and crime harm scores, respectively, indicating the predictability of crime and harm to occur at certain stations but not others. Offending persists consistently, with low, moderate and high counts and harm stations remaining the same over time.

These findings suggest that more localised crime control initiatives are required to target crime effectively.

This is one of the only studies focusing on hotspots and harmspots in the mass transit system.