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Xie, Yao [Verfasser:in] ; Short, Martin (Organisation) [Sonstige Person, Familie und Körperschaft]; Kolokolnikov, Theodore (Organisation) [Sonstige Person, Familie und Körperschaft]; Lloyd, David (Organisation) [Sonstige Person, Familie und Körperschaft]

Network Hawkes processes for crime events Change detection, text, and generative model

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  • Medientyp: E-Book; Video
  • Titel: Network Hawkes processes for crime events Change detection, text, and generative model
  • Beteiligte: Xie, Yao [Verfasser:in]; Short, Martin (Organisation) [Sonstige Person, Familie und Körperschaft]; Kolokolnikov, Theodore (Organisation) [Sonstige Person, Familie und Körperschaft]; Lloyd, David (Organisation) [Sonstige Person, Familie und Körperschaft]
  • Erschienen: [Erscheinungsort nicht ermittelbar]: Banff International Research Station (BIRS) for Mathematical Innovation and Discovery, 2019
  • Erschienen in: Mathematical Criminology and Security (19w5101) ; (Jan. 2019)
  • Umfang: 1 Online-Ressource (92 MB, 00:31:10:05)
  • Sprache: Englisch
  • DOI: 10.5446/56605
  • Identifikator:
  • Entstehung:
  • Anmerkungen: Audiovisuelles Material
  • Beschreibung: Crimes emerge out of complex interactions of behaviors and situations; thus there are complex linkages between crime incidents. Solving the puzzle of crime linkage is a highly challenging task because we often only have limited information from indirect observations such as records, text descriptions, and associated time and locations. We propose a new modeling and learning framework for detecting linkage between crime events using spatio-temporal-textual data, which are highly prevalent in the form of police reports. We capture the notion of modus operandi (M.O.), by introducing a multivariate marked point process and handling the complex text jointly with the time and location. The model is able to discover the latent space that links the crime series. The model fitting is achieved by a computationally efficient Expectation-Maximization (EM) algorithm. In addition, we explicitly reduce the bias in the text documents in our algorithm. Our numerical results using real data from the Atlanta Police show that our method has competitive performance relative to the state-of-the-art. Our results, including variable selection, are highly interpretable and may bring insights into M.O. extraction. This is a joint work with Shixiang Zhu at Georgia Tech
  • Zugangsstatus: Freier Zugang
  • Rechte-/Nutzungshinweise: Namensnennung - Nicht kommerziell (CC BY-NC)