• Medientyp: E-Artikel
  • Titel: Spatio-Temporal Mixed Membership Models for Criminal Activity
  • Beteiligte: Virtanen, Seppo; Girolami, Mark
  • Erschienen: Oxford University Press (OUP), 2021
  • Erschienen in: Journal of the Royal Statistical Society Series A: Statistics in Society, 184 (2021) 4, Seite 1220-1244
  • Sprache: Englisch
  • DOI: 10.1111/rssa.12642
  • ISSN: 0964-1998; 1467-985X
  • Schlagwörter: Statistics, Probability and Uncertainty ; Economics and Econometrics ; Social Sciences (miscellaneous) ; Statistics and Probability
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  • Beschreibung: Abstract We suggest a probabilistic approach to study crime data in London and highlight the benefits of defining a statistical joint crime distribution model which provides insights into urban criminal activity. This is achieved by developing a hierarchical mixture model for observations, crime occurrences over a geographical study area, that are grouped according to multiple time stamps and crime categories. The mixture components correspond to spatial crime distributions over the study area and the goal is to infer, based on the observations, how and to what degree the latent distributions are shared across the groups.