Sexual offences in public spaces, particularly on the London railway network, are on an upward trend as identified by research findings on victimisation rates. There are, however, relatively limited studies on offender characteristics or crime analysis identifying routine behaviours, to increase knowledge on how these crimes are committed. The known risk factors for all types of sexual offending include issues such as deviant sexual interests, intimacy and relationship deficits, difficulties in general emotional regulation and antisocial cognitions. However, it is unclear as to the extent that existing theories and typologies can be applied to those who sexually offend on the railways. In order to understand the relevance of offender characteristics and their contribution to knowledge about how sex offences on London railways are committed, script theory as a crime-specific approach has potential for creating, categorising and systematising knowledge about the crime commission process. A script theoretical framework provides an analytical approach to elicit the way that events and episodes unfold through comprehensive subjective offenders’ accounts and other sources of information. This theory incorporates different theories and concepts relating to the cognitive and procedural aspects of criminal behaviour. The benefits for this extended analysis of all stages of the crime commission sequence can assist with providing guidance for crime prevention policies. The purpose of this programme of research is to develop the existing knowledge of the characteristics of sex offenders who commit sexual offences on the railways and secondly, to develop a theoretical framework to explain the commission of these offences.
A multistage mixed methods approach will be employed to develop the evidence base in this programme of research - data collection and analysis will use both quantitative and qualitative methods to include:
AnalysisThe principles of grounded theory will be used to analyse the field notes and interview transcriptions to facilitate a progressive identification and integration of categories of meaning of the collected data, which also establishes links within the data. Parametric or non-parametric tests will be used to make comparisons between variables e.g. different types of offences, age, ethnicity etc. Multiple regression analysis will be used to identify any predictor variables.