Les réseaux sociaux comme prédicteurs de la criminalité urbaine
The present study attempts to determine the importance of analyzing crime at increasingly precise spatial and temporal levels. Also, a source of data from social media, Twitter, is used to predict the distribution of crimes in Montréal by estimating the ambient population on the territory, and characterizing it according to its mood. Poisson multilevel models are used to predict violent and property crimes aggregated to the street segment by time of day. The results show that it is essential for any analysis of crime in Montreal to take into account the variance of crime at the micro-place and to incorporate intraday periods. Also, the characterization of the ambient population has been identified as a promising avenue for the prediction of crime. Finally, the study provides proof of concept of the use of Twitter for the prediction of criminality, and following a markup related to the representativity of the users of the platform, the data could be used by public safety actors.
Population flottante Lag spatial Twitter Crime analysis Spatial analysis Temporal analysis Ambient population Spatial lag Criminology of place Sentiment analysis Social media Aoristic analysis