69,499 research outputs found

    Spatio-temporal crime HotSpot detection and prediction: a systematic literature review

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    The primary objective of this study is to accumulate, summarize, and evaluate the state-of-the-art for spatio-temporal crime hotspot detection and prediction techniques by conducting a systematic literature review (SLR). The authors were unable to find a comprehensive study on crime hotspot detection and prediction while conducting this SLR. Therefore, to the best of author's knowledge, this study is the premier attempt to critically analyze the existing literature along with presenting potential challenges faced by current crime hotspot detection and prediction systems. The SLR is conducted by thoroughly consulting top five scientific databases (such as IEEE, Science Direct, Springer, Scopus, and ACM), and synthesized 49 different studies on crime hotspot detection and prediction after critical review. This study unfolds the following major aspects: 1) the impact of data mining and machine learning approaches, especially clustering techniques in crime hotspot detection; 2) the utility of time series analysis techniques and deep learning techniques in crime trend prediction; 3) the inclusion of spatial and temporal information in crime datasets making the crime prediction systems more accurate and reliable; 4) the potential challenges faced by the state-of-the-art techniques and the future research directions. Moreover, the SLR aims to provide a core foundation for the research on spatio-temporal crime prediction applications while highlighting several challenges related to the accuracy of crime hotspot detection and prediction applications

    Mining large-scale human mobility data for long-term crime prediction

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    Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. We deep-dive into the main crime categories, and find that the predictive gain of the human dynamics features varies across crime types: such features bring the biggest boost in case of grand larcenies, whereas assaults are already well predicted by the census features. Furthermore, we identify and discuss top predictive features for the main crime categories. These results offer valuable insights for those responsible for urban policy or law enforcement

    Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

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    Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ‘A.I. neuroprediction,’ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed

    Can Neuroscience Help Predict Future Antisocial Behavior?

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    Part I of this Article reviews the tools currently available to predict antisocial behavior. Part II discusses legal precedent regarding the use of, and challenges to, various prediction methods. Part III introduces recent neuroscience work in this area and reviews two studies that have successfully used neuroimaging techniques to predict recidivism. Part IV discusses some criticisms that are commonly levied against the various prediction methods and highlights the disparity between the attitudes of the scientific and legal communities toward risk assessment generally and neuroscience specifically. Lastly, Part V explains why neuroscience methods will likely continue to help inform and, ideally, improve the tools we use to help assess, understand, and predict human behavior

    Foreword

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    This Foreword provides an overview of Criminal Behavior and the Brain: When Law and Neuroscience Collide, a symposium hosted by the Fordham Law Review and cosponsored by the Fordham Law School Neuroscience and Law Center. While the field of neuroscience is vast—generally constituting “the branch of the life sciences that studies the brain and nervous system”— this symposium focused on the cutting-edge ties between neuroscience evidence and the different facets of criminal law. Such an intersection invited commentary from an expert group on a wide span of topics, ranging from the historical underpinnings between law and neuroscience to the treatment of young adults to the different roles of neuroscience in the context of sentencing, expert testimony, defenses, prediction, punishment, and rehabilitation, as well as the civil and criminal divide. These diverse subjects have an overarching theme in common: each pertains in some way to the criminal justice system’s effort to punish or rehabilitate more fairly and effectively

    Standard of proof, unpredictable behaviour and the High Court of Australia's verdict on preventive detention laws

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    Preventive detention laws authorize courts to order the continued detention in prison of a person who has served their allocated term of imprisonment, but who are thought to be at risk of re-offending if released. They raise fundamental issues about the separation of powers, the purpose of incarceration, and the standard of proof which is/should be required to authorize detention. They assume that it is possible to predict, with a satisfactory rate of success, whether or not a past offender would if released commit further offences. Recently, a majority of the High Court of Australia validated such legislation. The author in this article explains his reasons for disagreeing with the verdict of the Court in this matter
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