This paper explores crime risk within and around major transit systems, specifically by investigating theft of personal property offences on the London Underground. The majority of studies to date have examined theft above ground, predominantly at transit stations, although some studies have compared this with theft in nearby surrounding areas. This study is unique and extends this analysis to theft during transit journeys below ground. The location of such offences is often unknown, only discovered by the victim sometime after the event. A new technique termed Interstitial Crime Analysis is used to better measure the location of below ground theft offences; these are compared with above ground thefts using Spearman’s Rank tests for association. Key findings are: below ground theft offences are concentrated at particular stations; risk is highest during morning and late afternoon peak travel periods; at these peak times there is an elevated risk of theft at both high risk stations and in their surrounding environs; and that this relationship is not evident during the inter-peak and late night time periods. The findings suggest offenders who operate below ground may also operate above ground on major transit systems. This has clear policy implications for policing these settings and highlights the importance of joint operations and information sharing between transit agencies and local police forces operating near major transit systems.

The routine activity approach and associated crime pattern theory emphasise how crime emerges from spatio-temporal routines. In order to understand this crime should be studied in both space and time. However, the bulk of research into crime patterns and related activities has investigated the spatial distributions of crime, neglecting the temporal dimension. Specifically, disaggregation of crime by place and by time, for example hour of day, day of week, month of year, season, or school day versus none school day, is extremely relevant to theory. Modern data make such spatio-temporal disaggregation increasingly feasible, as exemplified in this special issue. First, much larger data files allow disaggregation of crime data into temporal and spatial slices. Second, new forms of data are generated by modern technologies, allowing innovative and new forms of analyses. Crime pattern analyses and routine activity inquiries are now able to explore avenues not previously available. The unique collection of nine papers in this thematic issue specifically examine spatio-temporal patterns of crime to; demonstrate the value of this approach for advancing knowledge in the field; consider how this informs our theoretical understanding of the manifestations of crime in time and space; to consider the prevention implications of this; and to raise awareness of the need for further spatio-temporal research into crime events.