Love thy neighbour? Associations of social capital and crime with physical activity amongst women

Using a multilevel study design, this study examined the associations between social characteristics of individuals and neighbourhoods and physical activity among women. Women (n = 1405) recruited from 45 Melbourne (Australia) neighbourhoods of varying socioeconomic disadvantage provided data on social factors and leisure-time: physical activity; walking; and walking in one’s own neighbourhood. Individual level social factors were number of neighbours known and social participation. Neighbourhood-level social characteristics (interpersonal trust, norms of reciprocity, social cohesion) were derived by aggregating survey data on these constructs within neighbourhoods. Objective data on crimes within neighbourhoods were obtained from Victoria Police. In bivariable regression models, all social variables at both the individual and neighbourhood level were positively associated with odds of physical activity, walking, and walking in one’s own neighbourhood. Associations with individual social participation (associated with all three physical activity variables) and neighbourhood interpersonal trust (associated with overall physical activity only) remained significant in multivariable models. Neither neighbourhood crime against the person nor incivilities were associated with any form of physical activity. These results demonstrate that women who participated in local groups or events and, less consistently, women living in neighbourhoods where residents trusted one another, were more likely to participate in leisure-time physical activity. While redressing macro-level social and economic policies that contribute to neighbourhood inequalities remains a priority, public health initiatives aimed at promoting physical activity could consider focusing on fostering social interactions targeting both individuals and communities. Further investigation of causal mechanisms underlying these associations is required.

Crime risks of three-dimensional virtual environments

Three-dimensional virtual environments (3dves) are the new generation of digital multi-user social networking platforms. Their immersive character allows users to create a digital humanised representation or avatar, enabling a degree of virtual interaction not possible through conventional text-based internet technologies. As recent international experience demonstrates, in addition to the conventional range of cybercrimes (including economic fraud, the dissemination of child pornography and copyright violations), the 'virtual-reality' promoted by 3dves is the source of great speculation and concern over a range of specific and emerging forms of crime and harm to users. This paper provides some examples of the types of harm currently emerging in 3dves and suggests internal regulation by user groups, terms of service, or end-user licensing agreements, possibly linked to real-world criminological principles. This paper also provides some directions for future research aimed at understanding the role of Australian criminal law and the justice system more broadly in this emerging field.

Australian police responses to transnational crime and terrorism

This chapter outlines the Australian federal policing and allied agency responses to international drug trafficking activity and terrorism. The chapter is situated alongside a broader analysis of the structure of each key agency and the various interagency enforcement mechanisms established domestically and internationally to improve enforcement performance.

Mining multi-modal crime patterns at different levels of granularity using hierarchical clustering

The appearance of patterns could be found in different modalities of a domain, where the different modalities refer to the data sources that constitute different aspects of a domain. Particularly, the domain of our discussion refers to crime and the different modalities refer to the different data sources such as offender data, weapon data, etc. in crime domain. In addition, patterns also exist in different levels of granularity for each modality. In order to have a thorough understanding a domain, it is important to reveal the hidden patterns through the data explorations at different levels of granularity and for each modality. Therefore, this paper presents a new model for identifying patterns that exist in different levels of granularity for different modes of crime data. A hierarchical clustering approach - growing self organising maps (GSOM) has been deployed. Furthermore, the model is enhanced with experiments that exhibit the significance of exploring data at different granularities.

What is crime? Who is the criminal?

The term "crime" is often "taken-for-granted" and poorly defined in contemporary Australian and International research. There is also considerable debate amongst scholars working in different theoretical tranditions about the appropriate definition of crime. This reflects broader public division about the types of behaviour or people that are classed as criminal.