Abstracts
Résumé
La présente étude tente de déterminer l’importance d’analyser les crimes à des niveaux spatiaux et temporels de plus en plus précis. De même, une nouvelle source de données issue des médias sociaux, les messages sur Twitter, est utilisée afin de prédire la répartition des crimes à Montréal en estimant la population réelle sur le territoire, et en la caractérisant selon son humeur. Des modèles multiniveaux Poisson sont utilisés afin de prédire les crimes contre la personne et les crimes contre les biens agrégés au segment de rue selon l’heure de la journée. Les résultats montrent qu’il est primordial pour toute analyse de la criminalité à Montréal de tenir compte de la variance de la criminalité en ce qui a trait aux micro-endroits et d’y incorporer des périodes intrajournalières. La caractérisation de la population réelle de la ville a été considérée comme une avenue prometteuse pour la prédiction des crimes. Cette étude propose que l’utilisation des données de Twitter soit une avenue d’analyse concluante, mais qui reste encore à approfondir.
Mots-clés :
- Analyse de la criminalité,
- population flottante,
- analyse de l’humeur,
- réseaux sociaux,
- analyse aoristique
Abstract
This study attempts to determine the importance of analyzing crimes at increasingly precise spatial and temporal levels. Messages from the social media source Twitter are used to predict the distribution of crimes in Montreal by estimating the actual population in the area and characterizing it by mood. Multi-level Poisson models are used to predict violent and property crimes for a particular street segment according to the time of day. The results show that any crime analysis in Montreal must take into account variance in crime at the microsite level and incorporate intraday periods. Also, the characterization of a city’s actual population has been identified as a promising avenue for crime prediction. This study suggests that the analysis of Twitter data makes it possible to draw some conclusions, but it still needs to be further developed.
Keywords:
- Crime analysis,
- ambient population,
- sentiment analysis,
- social media,
- aoristic analysis
Resumen
Este estudio intenta determinar la importancia de analizar los crímenes en niveles espaciales y temporales cada vez más precisos. De este modo, una nueva fuente de datos proveniente de las redes sociales, los mensajes de Twitter, es utilizada con el fin de predecir la repartición de los crímenes en Montreal, estimando la población real en el territorio y caracterizándola según su humor. Unos modelos de multiniveles Poisson son utilizados con el fin de predecir los crímenes contra la persona y los crímenes contra los bienes y agregados al segmento de calle según la hora del día. Los resultados demuestran que para todo análisis sobre la criminalidad en Montreal, es primordial tener en cuenta la varianza de la criminalidad al nivel de los micro-sitios, e incorporar períodos intra-diarios. Igualmente, la caracterización de la población real de la ciudad fue identificada como una avenida prometedora para la predicción de los crímenes. Este estudio sugiere que la utilización de los datos de Twitter es una avenida de análisis concluyente, pero que toca todavía profundizar.
Palabras clave:
- Análisis de la criminalidad,
- población flotante,
- análisis del humor,
- redes sociales,
- análisis aoristic
Appendices
Références
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