A National Examination of the Spatial Extent and Similarity of Offenders’ Activity Spaces Using Police Data

Abstract

It is well established that offenders’ routine activity locations (nodes) shape their crime locations, but research examining the geography of offenders’ routine activity spaces has to date largely been limited to a few core nodes such as homes and prior offense locations, and to small study areas. This paper explores the utility of police data to provide novel insights into the spatial extent of, and overlap between, individual offenders’ activity spaces. It includes a wider set of activity nodes (including relatives’ homes, schools, and non-crime incidents) and broadens the geographical scale to a national level, by comparison to previous studies. Using a police dataset including n = 60,229 burglary, robbery, and extra-familial sex offenders in New Zealand, a wide range of activity nodes were present for most burglary and robbery offenders, but fewer for sex offenders, reflecting sparser histories of police contact. In a novel test of the criminal profiling assumptions of homology and differentiation in a spatial context, we find that those who offend in nearby locations tend to share more activity space than those who offend further apart. However, in finding many offenders’ activity spaces span wide geographic distances, we highlight challenges for crime location choice research and geographic profiling practice.

From centre to centres: polycentric structures in individual mobility

“…2 implicitly model an individual's activity space as a circle, and have been used in a variety of contexts. 4,5,14,[23][24][25][26][27][28][29][30][31] Yet, recent observational studies have documented polycentricity in human mobility, which an approach identifying only one centre and one spatial scale fails to capture. 2,32,34,35 To address this important limitation, the monocentric model can be extended to account for the polycentric nature of individual activity spaces.…”

“…The centre of the circle, the so-called centre of mass, and its radius, the so-called radius of gyration, are determined by the positions of the locations visited by the individual and their importance in terms of visitation probability. Following its introduction in the pioneer article by Gonzalez et al, 3 the monocentric model has been used for many applications, including characterising regularities across individuals, 4,14,[23][24][25][26] modelling the spread of epidemics, 5,27 studying patterns of crime activity, 28,29 and movements during natural disasters. 30,31 While being simple and concise, the description of individual whereabouts as a circle has recently been challenged by empirical findings revealing that mobility is organised around multiple centres.…”

A new Geographic Profiling Suspect Mapping And Ranking Technique for crime investigations: GP‐SMART

“…However, we also omitted information that if included in GP‐SMART might increase its accuracy. We only included a subset of activity nodes that most reliably indicated offenders' activity spaces, with high levels of geocoding accuracy and precision (Curtis‐Ham et al., 2021a). It is possible that including even more activity nodes would lead to higher levels of ranking accuracy, even if they were less reliable indicators of offenders' activity spaces (e.g., home addresses of past co‐offenders or other associates), or less geographically precise (e.g., traffic offences, which are recorded against stretches of road rather than specific addresses).…”

Relationships Between Offenders’ Crime Locations and Different Prior Activity Locations as Recorded in Police Data

“…The raw data included almost 5.5 million activity node records, for approximately 66,000 offenders. After removing offence and activity node records that did not reliably establish the presence of the offender (e.g., some specific frauds and offences involving publication or remote communication), or the time or location with sufficient specificity, approximately 60,000 offenders and 4.5 million nodes remained (see Curtis-Ham et al 2021a for more detail on the data cleaning steps). To ensure comparability in recency between different node types, we only included nodes dated within 5 years of the reference offence.…”