The Experts below are selected from a list of 19689 Experts worldwide ranked by ideXlab platform
Irena Pletikosa - One of the best experts on this subject based on the ideXlab platform.
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towards simulating Criminal Offender movement based on insights from human dynamics and location based social networks
Social Informatics, 2017Co-Authors: Raquel Roses Brungger, Robin Bader, Cristina Kadar, Irena PletikosaAbstract:Interest in data-driven crime simulations has been growing in recent years, confirming its potential to advance crime prevention and prediction. Especially, the use of new data sources in crime simulation models can contribute towards safer and smarter cities. Previous work on agent-based models for crime simulations have intended to simulate Offender behavior in a geographical environment, relying exclusively on a small sample of Offender homes and crime locations. The complex dynamics of crime and the lack of information on Criminal Offender’s movement patterns challenge the design of Offender movement in simulations. At the same time, the availability of big, GPS-based user data samples (mobile data, social media data, etc.) already allowed researchers to determine the laws governing human mobility patterns, which, we argue, could inform Offender movement. In this paper, we explore: (1) the use of location-based venue data from Foursquare in New York City (NYC), and (2) human dynamics insights from previous studies to simulate Offender movement. We study 9 Offender mobility designs in an agent-based model, combining search distances strategies (static, uniform distributed, and Levy-flight approximation) and target selection algorithms (random intersection, random Foursquare venues, and popular Foursquare venues). The Offender behavior performance is measured using the ratio of crime locations passed vs average distance traveled by each Offender. Our initial results show that agents moving between POI perform best, while the performance of the three search distance strategies is similar. This work provides a step forward towards more realistic crime simulations.
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SocInfo (2) - Towards Simulating Criminal Offender Movement Based on Insights from Human Dynamics and Location-Based Social Networks
Lecture Notes in Computer Science, 2017Co-Authors: Raquel Roses Brungger, Robin Bader, Cristina Kadar, Irena PletikosaAbstract:Interest in data-driven crime simulations has been growing in recent years, confirming its potential to advance crime prevention and prediction. Especially, the use of new data sources in crime simulation models can contribute towards safer and smarter cities. Previous work on agent-based models for crime simulations have intended to simulate Offender behavior in a geographical environment, relying exclusively on a small sample of Offender homes and crime locations. The complex dynamics of crime and the lack of information on Criminal Offender’s movement patterns challenge the design of Offender movement in simulations. At the same time, the availability of big, GPS-based user data samples (mobile data, social media data, etc.) already allowed researchers to determine the laws governing human mobility patterns, which, we argue, could inform Offender movement. In this paper, we explore: (1) the use of location-based venue data from Foursquare in New York City (NYC), and (2) human dynamics insights from previous studies to simulate Offender movement. We study 9 Offender mobility designs in an agent-based model, combining search distances strategies (static, uniform distributed, and Levy-flight approximation) and target selection algorithms (random intersection, random Foursquare venues, and popular Foursquare venues). The Offender behavior performance is measured using the ratio of crime locations passed vs average distance traveled by each Offender. Our initial results show that agents moving between POI perform best, while the performance of the three search distance strategies is similar. This work provides a step forward towards more realistic crime simulations.
Raquel Roses Brungger - One of the best experts on this subject based on the ideXlab platform.
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towards simulating Criminal Offender movement based on insights from human dynamics and location based social networks
Social Informatics, 2017Co-Authors: Raquel Roses Brungger, Robin Bader, Cristina Kadar, Irena PletikosaAbstract:Interest in data-driven crime simulations has been growing in recent years, confirming its potential to advance crime prevention and prediction. Especially, the use of new data sources in crime simulation models can contribute towards safer and smarter cities. Previous work on agent-based models for crime simulations have intended to simulate Offender behavior in a geographical environment, relying exclusively on a small sample of Offender homes and crime locations. The complex dynamics of crime and the lack of information on Criminal Offender’s movement patterns challenge the design of Offender movement in simulations. At the same time, the availability of big, GPS-based user data samples (mobile data, social media data, etc.) already allowed researchers to determine the laws governing human mobility patterns, which, we argue, could inform Offender movement. In this paper, we explore: (1) the use of location-based venue data from Foursquare in New York City (NYC), and (2) human dynamics insights from previous studies to simulate Offender movement. We study 9 Offender mobility designs in an agent-based model, combining search distances strategies (static, uniform distributed, and Levy-flight approximation) and target selection algorithms (random intersection, random Foursquare venues, and popular Foursquare venues). The Offender behavior performance is measured using the ratio of crime locations passed vs average distance traveled by each Offender. Our initial results show that agents moving between POI perform best, while the performance of the three search distance strategies is similar. This work provides a step forward towards more realistic crime simulations.
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SocInfo (2) - Towards Simulating Criminal Offender Movement Based on Insights from Human Dynamics and Location-Based Social Networks
Lecture Notes in Computer Science, 2017Co-Authors: Raquel Roses Brungger, Robin Bader, Cristina Kadar, Irena PletikosaAbstract:Interest in data-driven crime simulations has been growing in recent years, confirming its potential to advance crime prevention and prediction. Especially, the use of new data sources in crime simulation models can contribute towards safer and smarter cities. Previous work on agent-based models for crime simulations have intended to simulate Offender behavior in a geographical environment, relying exclusively on a small sample of Offender homes and crime locations. The complex dynamics of crime and the lack of information on Criminal Offender’s movement patterns challenge the design of Offender movement in simulations. At the same time, the availability of big, GPS-based user data samples (mobile data, social media data, etc.) already allowed researchers to determine the laws governing human mobility patterns, which, we argue, could inform Offender movement. In this paper, we explore: (1) the use of location-based venue data from Foursquare in New York City (NYC), and (2) human dynamics insights from previous studies to simulate Offender movement. We study 9 Offender mobility designs in an agent-based model, combining search distances strategies (static, uniform distributed, and Levy-flight approximation) and target selection algorithms (random intersection, random Foursquare venues, and popular Foursquare venues). The Offender behavior performance is measured using the ratio of crime locations passed vs average distance traveled by each Offender. Our initial results show that agents moving between POI perform best, while the performance of the three search distance strategies is similar. This work provides a step forward towards more realistic crime simulations.
Christopher J Patrick - One of the best experts on this subject based on the ideXlab platform.
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validity of the externalizing spectrum inventory in a Criminal Offender sample relations with disinhibitory psychopathology personality and psychopathic features
Psychological Assessment, 2012Co-Authors: Noah C Venables, Christopher J PatrickAbstract:The Externalizing Spectrum Inventory (ESI; Krueger, Markon, Patrick, Benning, & Kramer, 2007) provides a self-report based method for indexing a range of correlated problem behaviors and traits in the domain of deficient impulse control. The ESI organizes lower order behaviors and traits of this kind around higher order factors encompassing general disinhibitory proneness, callous-aggression, and substance abuse. In the current study, we used data from a male prisoner sample (N = 235) to evaluate the validity of ESI total and factor scores in relation to external criterion measures consisting of externalizing disorder symptoms (including child and adult antisocial deviance and substance-related problems) assessed via diagnostic interviews, personality traits assessed with self-reports, and psychopathic features as assessed with both interviews and self-reports. Results provide evidence for the validity of the ESI measurement model and point to its potential usefulness as a referent for research on the neurobiological correlates and etiological bases of externalizing proneness.
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relations between psychopathy facets and externalizing in a Criminal Offender sample
Journal of Personality Disorders, 2005Co-Authors: Christopher J Patrick, Brian M Hicks, Robert F Krueger, Alan R LangAbstract:Antisocial behavior is a problem of enormous social importance that has been the focus of intensive psychological study. Two research traditions have been prominent in this area: (1) longitudinal, epidemiological studies examining precursors and predictors of Criminality in the population at large, and (2) experimental psychopathology studies investigating the construct of psychopathic personality in incarcerated Offender populations. The former has yielded evidence of a broad spectrum of externalizing problems, encompassing alcohol and drug abuse as well as child and adult antisociality, that arise from a common etiologic vulnerability (e.g., Krueger et al., 2002; Young, Stallings, Corley, Krauter, & Hewitt, 2000). The latter has produced evidence that there are distinct facets to psychopathy with differing external correlates (e.g., Cooke & Michie, 2001; Hall, Benning, & Patrick, 2004; Hare, 1991, 2003; Harpur, Hare, & Hakstian, 1989; Patrick, 1994; Widiger & Lynam, 1998) that may reflect separate etiologic processes (Fowles & Dindo, 2006; Patrick, 2001, in press). The current study bridges these research domains by demonstrating a link between the externalizing dimension of adult psychopathology and the social deviance factor of psychopathy, and illustrating how novel predictions can be generated through knowledge of this association.
Sabine C. Herpertz - One of the best experts on this subject based on the ideXlab platform.
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Brain volumes differ between diagnostic groups of violent Criminal Offenders
European Archives of Psychiatry and Clinical Neuroscience, 2013Co-Authors: Katja Bertsch, Michel Grothe, Kristin Prehn, Knut Vohs, Christoph Berger, Karlheinz Hauenstein, Peter Keiper, Gregor Domes, Stefan Teipel, Sabine C. HerpertzAbstract:Studies on structural abnormalities in antisocial individuals have reported inconsistent results, possibly due to inhomogeneous samples, calling for an investigation of brain alterations in psychopathologically stratified subgroups. We explored structural differences between antisocial Offenders with either borderline personality disorder (ASPD-BPD) or high psychopathic traits (ASPD-PP) and healthy controls (CON) using region-of-interest-based and voxel-based morphometry approaches. Besides common distinct clusters of reduced gray matter volumes within the frontal pole and occipital cortex, there was remarkably little overlap in the regional distribution of brain abnormalities in ASPD-BPD and ASPD-PP, when compared to CON. Specific alterations of ASPD-BPD were detected in orbitofrontal and ventromedial prefrontal cortex regions subserving emotion regulation and reactive aggression and the temporal pole, which is involved in the interpretation of other peoples’ motives. Volumetric reductions in ASPD-PP were most significant in midline cortical areas involved in the processing of self-referential information and self-reflection (i.e., dorsomedial prefrontal cortex, posterior cingulate/precuneus) and recognizing emotions of others (postcentral gyrus) and could reflect neural correlates of the psychopathic core features of callousness and poor moral judgment. The findings of this first exploratory study therefore may reflect correlates of prominent psychopathological differences between the two Criminal Offender groups, which have to be replicated in larger samples.
Cristina Kadar - One of the best experts on this subject based on the ideXlab platform.
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towards simulating Criminal Offender movement based on insights from human dynamics and location based social networks
Social Informatics, 2017Co-Authors: Raquel Roses Brungger, Robin Bader, Cristina Kadar, Irena PletikosaAbstract:Interest in data-driven crime simulations has been growing in recent years, confirming its potential to advance crime prevention and prediction. Especially, the use of new data sources in crime simulation models can contribute towards safer and smarter cities. Previous work on agent-based models for crime simulations have intended to simulate Offender behavior in a geographical environment, relying exclusively on a small sample of Offender homes and crime locations. The complex dynamics of crime and the lack of information on Criminal Offender’s movement patterns challenge the design of Offender movement in simulations. At the same time, the availability of big, GPS-based user data samples (mobile data, social media data, etc.) already allowed researchers to determine the laws governing human mobility patterns, which, we argue, could inform Offender movement. In this paper, we explore: (1) the use of location-based venue data from Foursquare in New York City (NYC), and (2) human dynamics insights from previous studies to simulate Offender movement. We study 9 Offender mobility designs in an agent-based model, combining search distances strategies (static, uniform distributed, and Levy-flight approximation) and target selection algorithms (random intersection, random Foursquare venues, and popular Foursquare venues). The Offender behavior performance is measured using the ratio of crime locations passed vs average distance traveled by each Offender. Our initial results show that agents moving between POI perform best, while the performance of the three search distance strategies is similar. This work provides a step forward towards more realistic crime simulations.
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SocInfo (2) - Towards Simulating Criminal Offender Movement Based on Insights from Human Dynamics and Location-Based Social Networks
Lecture Notes in Computer Science, 2017Co-Authors: Raquel Roses Brungger, Robin Bader, Cristina Kadar, Irena PletikosaAbstract:Interest in data-driven crime simulations has been growing in recent years, confirming its potential to advance crime prevention and prediction. Especially, the use of new data sources in crime simulation models can contribute towards safer and smarter cities. Previous work on agent-based models for crime simulations have intended to simulate Offender behavior in a geographical environment, relying exclusively on a small sample of Offender homes and crime locations. The complex dynamics of crime and the lack of information on Criminal Offender’s movement patterns challenge the design of Offender movement in simulations. At the same time, the availability of big, GPS-based user data samples (mobile data, social media data, etc.) already allowed researchers to determine the laws governing human mobility patterns, which, we argue, could inform Offender movement. In this paper, we explore: (1) the use of location-based venue data from Foursquare in New York City (NYC), and (2) human dynamics insights from previous studies to simulate Offender movement. We study 9 Offender mobility designs in an agent-based model, combining search distances strategies (static, uniform distributed, and Levy-flight approximation) and target selection algorithms (random intersection, random Foursquare venues, and popular Foursquare venues). The Offender behavior performance is measured using the ratio of crime locations passed vs average distance traveled by each Offender. Our initial results show that agents moving between POI perform best, while the performance of the three search distance strategies is similar. This work provides a step forward towards more realistic crime simulations.
