Metal Theft Solvability and Detection

Paul Robb*, Richard Timothy Coupe, Barak Ariel

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


This chapter identifies the solvability factors for the seven types of metal theft offences committed on railway property in England and Wales and examines how they vary geographically in different British Transport Police (BTP) regions. The analysis of BTP data for 4001 metal theft offences committed between 2009 and 2010 enables nine significant solvability factors to be identified. The most important of these are checks with scrap metal dealers, covert police activities, witnessed offences, vehicle registration information and forensic evidence. Significant solvability factors explain 64% of the variation in detection outcomes. Detection odds depend on both the number of factors present in cases and their effect sizes. The number of solvability factors was a very good measure of incident solvability, with each extra factor improving detection odds fivefold. There were marked spatial variations in incident solvability and detection outcomes, some of which reflected metal offence type differences, solvability differences and apparent variations in investigative practice. The study indicates that statistical case-screening procedures would improve metal theft detection rates cost-effectively by informing police how to prioritise the allocation of resources to the most solvable cases and avoid applying investigative effort to cases that are unlikely to be solved.

Original languageAmerican English
Title of host publicationCrime Solvability Factors
Subtitle of host publicationPolice Resources and Crime Detection
PublisherSpringer International Publishing
Number of pages20
ISBN (Electronic)9783030171605
ISBN (Print)9783030171599
StatePublished - 1 Jan 2019

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.


  • Detection
  • Investigation
  • Metal theft
  • Policing
  • Solvability factors
  • Spatial variation


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