This study analyzes criminal career trajectories using a unique administrative dataset of 230,578 individuals arrested and over 620,000 criminal offenses recorded by Greater Manchester Police between 2008 and 2019. Moving beyond static co-offending networks, we model individual offense sequences as transitions within a multilayer directed crime network. This allows us to capture temporal dependence and structural progression across crime types and provides a scalable method to analyze criminal trajectories at the population level.

We find that the majority of potential offenders (almost 60%) are arrested for only a single recorded offense. Among repeat offenders, specialization deepens over time: the likelihood of being arrested for the same type of crime again rises steadily across consecutive offenses, most strongly for shoplifting, burglary, and fraud. Only 9.14% of persistent offenders remain within a single crime category throughout their recorded career, making diversification the norm rather than the exception among high-frequency offenders. Within these cross-category transitions, public order offenses and weapon possession consistently precede violent crime at every career stage, suggesting structured pathways that may serve as early intervention points.

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2 April 2026     Paper Number CEPDP2168

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This CEP discussion paper is published under the centre's Crime programme.

This publication comes under the following theme: Crime networks