| 书目名称 | Social Network Analysis in Predictive Policing |
| 副标题 | Concepts, Models and |
| 编辑 | Mohammad A. Tayebi,Uwe Glässer |
| 视频video | |
| 概述 | Presents a computational method and a thorough experimental evaluation for each problem, along with a discussion of the results.Contains formal representation and algorithmic solutions of predictive p |
| 丛书名称 | Lecture Notes in Social Networks |
| 图书封面 |  |
| 描述 | This book focuses on applications of social network analysis in predictivepolicing. Data science is used to identify potential criminal activity byanalyzing the relationships between offenders to fully understand criminalcollaboration patterns. Co-offending networks—networks of offenders who havecommitted crimes together—have long been recognized by law enforcement andintelligence agencies as a major factor in the design of crime prevention andintervention strategies. Despite the importance of co-offending network analysisfor public safety, computational methods for analyzing large-scale criminalnetworks are rather premature. This book extensively and systematically studiesco-offending network analysis as effective tool for predictive policing. Theformal representation of criminological concepts presented here allow computerscientists to think about algorithmic and computational solutions to problemslong discussed in the criminology literature. For each ofthe studied problems,we start with well-founded concepts and theories in criminology, then propose acomputational method and finally provide a thorough experimental evaluation,along with a discussion of the results. In this way, |
| 出版日期 | Book 2016 |
| 关键词 | criminal network analysis; predictive policing; computational criminology; co-offending network; co-offe |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-41492-8 |
| isbn_softcover | 978-3-319-82368-3 |
| isbn_ebook | 978-3-319-41492-8Series ISSN 2190-5428 Series E-ISSN 2190-5436 |
| issn_series | 2190-5428 |
| copyright | Springer International Publishing Switzerland 2016 |
发表于 2025-3-21 16:35:46
