Andromachi Tseloni – författare

This work provides clear application of a new statistical modeling technique that can be used to recognize patterns in victimization and prevent repeat victimization. The history of crime prevention techniques range from offender-based, to environment/situation-based, to victim-based. The authors of this work have found more accurate ways to predict and prevent victimization using a statistical modeling, based around crime concentration and sub-group profiling with regard to crime vulnerability levels, to predict areas and individuals vulnerable to crime. Following from this prediction, they propose policing strategies to improve crime prevention based on these predictions. With a combination of immediate actions and longer-term research recommendations, this work will be of interest to researchers and policy makers in focused on crime prevention, police studies, victimology and statistical applications.

This work provides clear application of a new statistical modeling technique that can be used to recognize patterns in victimization and prevent repeat victimization. The history of crime prevention techniques range from offender-based, to environment/situation-based, to victim-based. The authors of this work have found more accurate ways to predict and prevent victimization using a statistical modeling, based around crime concentration and sub-group profiling with regard to crime vulnerability levels, to predict areas and individuals vulnerable to crime. Following from this prediction, they propose policing strategies to improve crime prevention based on these predictions. With a combination of immediate actions and longer-term research recommendations, this work will be of interest to researchers and policy makers in focused on crime prevention, police studies, victimology and statistical applications.

Domestic burglary has fallen significantly over the past 20 years in many countries, but still remains a high volume crime. On top of substantial financial loss and property damage, burglary also leads to high levels of anxiety and fear of crime. The research presented in this book represents the first systematic study of what actually works in security interventions against burglary, with cross-sectional data on different regions and socio-economic population groups. This work provides an overview of the scope of the problem and what can be done about it, drawing on extensive research evidence from projects funded by the Economic and Social Research Council (ESRC) Secondary Data Analysis Initiative (SDAI), and other sources. It reports detailed findings about which interventions are most effective for different population groups and how these measures can be implemented. It includes burglary prevention advice for homeowners, law enforcement and other public agencies, and makes recommendations for future research.In addition to being relevant to concerned citizens, police, policy-makers and crime prevention practitioners, this book will also be of interest to researchers in criminology and criminal justice, particularly those working on security and crime prevention, as well as urban planning and public policy.