Comparar métodos

	Offender-Based Transition Matrix	Criminal Trajectory Clustering
Campo	Criminology	Criminology
Familia	Process / pipeline	Regression model
Año de origen	1988	2010
Autor original	Alfred Blumstein, Jacqueline Cohen, Somnath Das & Soumyo D. Moitra	Daniel S. Nagin; Christophe Genolini & Bruno Falissard (KmL)
Tipo	Markov-style transition-matrix description of crime-type switching	Algorithmic clustering of longitudinal offending trajectories
Fuente seminal	Blumstein, A., Cohen, J., Das, S., & Moitra, S. D. (1988). Specialization and seriousness during adult criminal careers. Journal of Quantitative Criminology, 4(4), 303–345.	Nagin, D. S. (2005). Group-Based Modeling of Development. Harvard University Press. ISBN: 9780674016866
Alias	Crime-Switch Matrix, Offense-Type Transition Matrix, Specialization Transition Matrix, Markov Crime-Switching Analysis	Offending Trajectory Clustering, Longitudinal Offending Cluster Analysis, Trajectory Shape Clustering, Crime-Curve Clustering
Relacionados	3	4
Resumen	An offender-based transition matrix describes the probability that an offender's next offense is of a particular crime type given the type of the current offense. Introduced to criminology by Blumstein, Cohen, Das, and Moitra in 1988, it treats each individual's ordered sequence of offenses as a Markov-style process and asks the central question of the specialization-versus-versatility debate: do offenders tend to repeat the same kind of crime, or do they switch freely across crime types?	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.