rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Social Network Analysis in Predictive Policing
    sub title: Concepts, Models and Methods
  name:
    name part: Tayebi, Mohammad A.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Glässer, Uwe.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XI, 133 p. 43 illus. in color. online resource.
  abstract: This book focuses on applications of social network analysis in predictive policing. Data science is used to identify potential criminal activity by analyzing the relationships between offenders to fully understand criminal collaboration patterns. Co-offending networks—networks of offenders who have committed crimes together—have long been recognized by law enforcement and intelligence agencies as a major factor in the design of crime prevention and intervention strategies. Despite the importance of co-offending network analysis for public safety, computational methods for analyzing large-scale criminal networks are rather premature. This book extensively and systematically studies co-offending network analysis as effective tool for predictive policing. The formal representation of criminological concepts presented here allow computer scientists to think about algorithmic and computational solutions to problems long discussed in the criminology literature. For each of the studied problems, we start with well-founded concepts and theories in criminology, then propose a computational method and finally provide a thorough experimental evaluation, along with a discussion of the results. In this way, the reader will be able to study the complete process of solving real-world multidisciplinary problems.
  table of contents: Introduction -- Social Network Analysis in Predictive Policing -- Structure of Co-offending Networks -- Organized Crime Group Detection -- Suspect Investigation -- Co-offence Prediction -- Personalized Crime Location Prediction -- Concluding remarks -- References.
  note: by Mohammad A. Tayebi, Uwe Glässer.
  subject:
    topic: Computer science
  subject:
    topic: Computer security
  subject:
    topic: Data mining
  subject:
    topic: Police
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Policing
  subject:
    topic: Applications of Graph Theory and Complex Networks
  subject:
    topic: Systems and Data Security
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Social Networks
  identifier: 9783319414928
  identifier: http://dx.doi.org/10.1007/978-3-319-41492-8
  record info:
    record creation date: 161011
    record change date: 20161011092503.0
    record identifier: 978-3-319-41492-8