Using crowdsourced data to analyze transport crime

Research output: Contribution to journalArticle


Anecdotal evidence suggests that harsh social conditions in the road haulage industry are having an impact on transport crime. This paper analyses transport crime, and demonstrates how to use a combination of official statistics and crowdsourced data in the process. A hierarchical regression analysis was applied to investigate the relations among different factors in order to predict transport crime threats. A secondary data set on transport crime from the Swedish Police was combined with primary crowdsourced data from volunteer observations of trucks in Sweden from both high-wage and low-wage countries. The findings imply that transportation is more vulnerable to antagonistic threats in geographical areas where the low-wage hauliers operate more frequently. For policymakers and practitioners, these findings provide useful guidance for the planning of security measures. To the authors’ knowledge, this paper is the first exploratory study of its kind that uses a combination of official statistics and crowdsourced data.


  • Henrik Sternberg
  • Björn Lantz
External organisations
  • Chalmers University of Technology
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Work Sciences


  • criminology, crowdsourcing data, social sustainability, statistical analysis, Transport crime
Original languageEnglish
Pages (from-to)133-147
JournalInternational Journal of Logistics Research and Applications
Issue number2
Early online date2018 Jan 30
Publication statusPublished - 2018 Mar 4
Publication categoryResearch