Space–Time Dynamics of Crime in Transport Nodes

Abstract

Our current understanding of the role of the social environment in crime causation is at best rudimentary. Guided by the theoretical framework of Situational Action Theory, and using data from the ESRC financed Peterborough Adolescent and Young Adult Development Study (PADS?), this paper aims to propose how we can better theorise and study the role of the social environment, particularly the person and place interaction, in crime causation. We will introduce, and illustrate the usefulness of, a space-time budget methodology as a means of capturing people's exposure to settings and describing their activity fields. We will suggest and demonstrate that, combined with a small area community survey and psychometric measures of individual characteristics, a space-time budget is a powerful tool for advancing our knowledge about the role of the social environment, and its interaction with people's crime propensity, in crime causation. Our unique data allows us to study the convergence in time and space of crime propensity, criminogenic exposure and crime events. As far as we are aware, such an analysis has never before been carried out. The findings show that there are (a) clear associations between young people's activity fields and their exposure to criminogenic settings, (b) clear associations between their exposure to criminogenic settings and their crime involvement, and, crucially, (c) that the influence of criminogenic exposure depends on a person's crime propensity. Having a crime-averse morality and strong ability to exercise self-control appears to make young people practically situationally immune to the influences of criminogenic settings, while having a crime-prone moralityThe Peterborough Adolescent and Young Adult Development Study (PADS?) is funded by a large grant from the UK Economic and Social Research Council (ESRC). For further information about PADS? and its research see www.pads.ac.uk.

Intra-week spatial-temporal patterns of crime

“…Though this is generally an expected result, this is not always the case. For example, Ceccato and Uittenbogaard (2014) report that police statistics in Stockholm show the expected seasonal pattern with increases in criminal events in the summer months, Stockholm Public Transportation data reveal that violent crimes were greatest in the winter months. This was explained by the need of passengers to be indoors at stations in the winter because of low temperatures as well as weather making it more likely for people to leave their cars at home.…”

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

“…Friday night to Sunday morning) and are spatially connected with the proximity to bars or nightclubs (Andresen & Malleson, 2013, 2015; Caplan & Kennedy, 2011). Robberies and assaults present temporal similarities and peak periods that are found to be in the early morning hours on weekends and between 00:00 and 03:00 am on Saturdays and Sundays (Ceccato & Uittenbogaard, 2014). …”

Spatiotemporal Interaction of Urban Crime in Nairobi, Kenya

“…Voluminous literature exists on the seasonal variability of crime (e.g., JOHNSON ET AL. 2007, NAKAYA & YANO 2010, ANDRESEN & MALLESON 2013, CECCATO & UITTENBOGAARD 2014, and reviews of such literature (e.g. BAUMER & WRIGHT 1996), but elementary questions still exist.…”