Fakultät für Mathematik und Naturwissenschaften

Verbrechen durch Mathematik verstehen

  1. C.Z. Marshak, M.P. Rombach, A.L. Bertozzi, M.R. D'Orsogna, Growth and containment of a hierarchical criminal network, Physical Review E 93(2) (2016), 022308.
  2. M.R. D'Orsogna, M. Perc, Statistical physics of crime: A review. Physics of life reviews, 12 (2015), 1-21.
  3. M.B. Short, M.R. D'orsogna, V.B. Pasour, G.E. Tita, P.J. Brantingham, A.L. Bertozzi, L.B. Chayes, A statistical model of criminal behavior, Mathematical Models and Methods in Applied Sciences, 18(supp01) (2008), 1249-1267.
  4. A.B. Barbaro, L. Chayes, M.R. D’Orsogna, Territorial developments based on graffiti: A statistical mechanics approach, Physica A: Statistical Mechanics and its Applications, 392(1) (2013), 252-270.
  5. C. Wang, Y. Zhang, A multiscale stochastic criminal behavior model under a hybrid scheme, Electronic Research Archive, 2021.
  6. L.G. Alves, H.V. Ribeiro, F.A. Rodrigues, Crime prediction through urban metrics and statistical learning, Physica A: Statistical Mechanics and its Applications 505 (2018), 435-443.
  7. I. Luna-Pla, J.R. Nicolás-Carlock, Corruption and complexity: a scientific framework for the analysis of corruption networks, Applied Network Science 5(1) (2020), 1-18.
  8. Special Issue of European Journal of Applied Mathematics on Crime Modelling, 2010.

Das Haus des Täters: Was ist Geographic Profiling?

  1. D.K. Rossmo, Geographic profiling, CRC press, 1999.
  2. D.K. Rossmo, Geographic profiling: Target patterns of serial murderers, Doctoral dissertation, School of Criminology, Simon Fraser University, 1995.
  3. D.K. Rossmo, I. Laverty, B. Moore, Geographic profiling for serial crime investigation, Geographic information systems and crime analysis (pp. 102-117). IGI Global, 2005.
  4. D.K. Rossmo, L. Velarde, Geographic profiling analysis: principles, methods and applications, Crime mapping case studies: Practice and research (2008), 35-43.
  5. D.K. Rossmo, Recent developments in geographic profiling, Policing: A Journal of Policy and Practice 6(2) (2012), 144-150.
  6. D.K. Rossmo, S. Rombouts, Geographic profiling, Environmental criminology and crime analysis (pp. 158-172), Willan, 2013.
  7. M.V. Hauge, M.D. Stevenson, D.K. Rossmo, S.C. Le Comber, Tagging Banksy: Using geographic profiling to investigate a modern art mystery, Journal of Spatial Science, 61(1) (2016), 185-190.
  8. P. Bengtsen, Hijacking Banksy, SAUC-Street Art and Urban Creativity, 2(1) (2016), 60-66.
  9. R.A. Martin, D.K. Rossmo, N. Hammerschlag, Hunting patterns and geographic profiling of white shark predation, Journal of Zoology, 279(2) (2009), 111-118.

Wie gross sind die Chancen, dass die DNA-Spur des Verdächtigen gefunden wird?

  1. J.J. Koehler, A. Chia, S. Lindsey, The random match probability in DNA evidence: Irrelevant and prejudicial?, Jurimetrics (1995), 201-219.
  2. J.J. Koehler, The base rate fallacy reconsidered: Descriptive, normative, and methodological challenges, Behavioral and brain sciences, 19(1) (1996), 1-17.
  3. A. Biedermann, F. Taroni, Bayesian networks for evaluating forensic DNA profiling evidence: a review and guide to literature, Forensic Science International: Genetics 6(2) (2012), 147-157.
  4. A. Drygajlo, D. Meuwly, A. Alexander, Statistical methods and Bayesian interpretation of evidence in forensic automatic speaker recognition, In: Eighth European Conference on Speech Communication and Technology, 2003.
  5. S. Lindsey, R. Hertwig, G. Gigerenzer, Communicating statistical DNA evidence, Jurimetrics 43 (2002), 147.
  6. C.S. Vlek, When stories and numbers meet in court: Constructing and explaining Bayesian networks for criminal cases with scenarios, 2016.
  7. C. Aitken, F. Taroni, Statistics and the evaluation of evidence for forensic scientists, John Wiley & Sons, 2004.
  8. U. Hoffrage, S. Lindsey, R. Hertwig, G. Gigerenzer, Communicating statistical information, 2000.
  9. I. Alberink, A. Bolck, S. Menges, Posterior likelihood ratios for evaluation of forensic trace evidence given a two-level model on the data, Journal of Applied Statistics 40(12) (2013), 2579-2600.

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