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Criminal Trajectory Clustering×Criminal Career Paradigm×
OborCriminologyCriminology
RodinaRegression modelProcess / pipeline
Rok vzniku20101986
TvůrceDaniel S. Nagin; Christophe Genolini & Bruno Falissard (KmL)Alfred Blumstein, Jacqueline Cohen, Jeffrey Roth & Christy Visher
TypAlgorithmic clustering of longitudinal offending trajectoriesConceptual framework for decomposing offending over the life course
Původní zdrojNagin, D. S. (2005). Group-Based Modeling of Development. Harvard University Press. ISBN: 9780674016866Blumstein, A., Cohen, J., Roth, J. A., & Visher, C. A. (Eds.). (1986). Criminal Careers and 'Career Criminals' (Vols. 1–2). National Academy Press. ISBN: 9780309036887
Další názvyOffending Trajectory Clustering, Longitudinal Offending Cluster Analysis, Trajectory Shape Clustering, Crime-Curve ClusteringCriminal Careers Framework, Career Criminal Paradigm, Offending Career Approach, Blumstein Criminal Career Model
Příbuzné44
ShrnutíCriminal trajectory clustering is the broad family of methods that group individuals by the shape of their longitudinal offending curves. Rather than committing to a single statistical model, it spans algorithmic approaches — k-means for longitudinal data, distance-based clustering of trajectory shapes, and likelihood-based latent class growth — and treats the choice of clustering method itself as a modeling decision validated by fit and stability criteria.The criminal career paradigm is a framework for studying offending as a longitudinal sequence in an individual's life rather than as undifferentiated aggregate crime. Codified by Blumstein, Cohen, Roth, and Visher in the 1986 National Academy of Sciences report, it decomposes crime into distinct dimensions — whether someone offends (participation), how often active offenders offend (frequency, λ), and the onset, seriousness, and duration of the career — each potentially with different causes.
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ScholarGatePorovnat metody: Criminal Trajectory Clustering · Criminal Career Paradigm. Získáno 2026-06-25 z https://scholargate.app/cs/compare