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	Criminal Trajectory Clustering	Age-Crime Curve Modeling
Campo	Criminology	Criminology
Famiglia	Regression model	Regression model
Anno di origine	2010	1983
Ideatore	Daniel S. Nagin; Christophe Genolini & Bruno Falissard (KmL)	Travis Hirschi & Michael Gottfredson; David Farrington
Tipo	Algorithmic clustering of longitudinal offending trajectories	Nonlinear regression modeling of the age distribution of offending
Fonte seminale	Nagin, D. S. (2005). Group-Based Modeling of Development. Harvard University Press. ISBN: 9780674016866	Hirschi, T., & Gottfredson, M. (1983). Age and the explanation of crime. American Journal of Sociology, 89(3), 552–584.
Alias	Offending Trajectory Clustering, Longitudinal Offending Cluster Analysis, Trajectory Shape Clustering, Crime-Curve Clustering	Age-Crime Relationship Modeling, Age-Offending Curve, Aggregate Age-Crime Distribution, Crime-Age Profile Modeling
Correlati	4	4
Sintesi	Criminal 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.	Age-crime curve modeling fits statistical functions to the well-known relationship between age and offending: crime rises sharply in adolescence, peaks in the late teens or early twenties, and declines through adulthood. Brought to prominence by Hirschi and Gottfredson's 1983 claim that this curve is invariant, and elaborated by Farrington, the modeling task is to capture its characteristic skewed, single-peaked shape and to debate what it implies about the causes of crime.