Abstract
The use of social media data for the spatial analysis of crime patterns during social events has proven to be instructive. This study analyzes the geography of crime considering hockey game days, criminal behaviour, and Twitter activity. Specifically, we consider the relationship between geolocated crime‐related Twitter activity and crime. We analyze six property crime types that are aggregated to the dissemination area base unit in Vancouver, for two hockey seasons through a game and non‐game temporal resolution. Using the same method, geolocated Twitter messages and environmental variables are aggregated to dissemination areas. We employ spatial clustering, dictionary‐based mining for tweets, spatial autocorrelation, and global and local regression models (spatial lag and geographically weighted regression). Findings show an important influence of Twitter data for theft‐from‐vehicle and mischief, mostly on hockey game days. Relationships from the geographically weighted regression models indicate that tweets are a valuable independent variable that can be used in explaining and understanding crime patterns.
Highlights
Spatial patterning of crime research most often involves routine activity theory, social disorganization theory, and the geometry of crime (Andresen 2006, 2011)
It should be noted that not all crime types occur with greater frequency in the immediate vicinity of Rogers Arena on game days, but there is an increase in the degree of clustering
No relationships were found between crime densities around the stadium; patterns were changing across the city in the four temporal frames
Summary
Spatial patterning of crime research most often involves routine activity theory, social disorganization theory, and the geometry of crime (Andresen 2006, 2011). Non-routine activities may be able to explain short-term changes in those spatial patterns Such non-routine activities must be significant enough to change the spatial distribution of motivated offenders, suitable targets, and capable guardians, albeit for a short period of time. Investigations into these possible changes need to have crime data, and other explanatory data, that are spatially and temporally available at a relatively fine resolution. The geometry of crime (Brantingham and Brantingham 1981) focuses on the possible intersection between the activity spaces of an offender and a victim This intersection could occur at different activity nodes (e.g., pubs, fast food restaurants, alcohol outlets) or along their pathways. Several researchers have noted that stadiums can be both a crime generator and attractor for distinct place- and time-specific events (Ratcliffe 2004; Kurland et al 2014; Brantingham et al 2017)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
