Résumés
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
Parties annexes
Références
- Abdaoui, A., Azé, J., Bringay, S. et Poncelet, P. (2015). Collaborative content-based method for estimating user reputation in online forums. International Conference on Web Information Systems Engineering, 292-299.
- Aghababaei, S. et Makrehchi, M. (2016). Mining social media content for crime prediction. 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 526-531. https://doi.org/10.1109/WI.2016.0089
- Andresen, M. A. (2011). The ambient population and crime analysis. The Professional Geographer, 63(2), 193-212.
- Andresen, M. A. et Malleson, N. (2011). Testing the stability of crime patterns : implications for theory and policy. Journal of Research in Crime and Delinquency, 48(1), 58-82.
- Anselin, L. (1988). Spatial econometrics : Methods and models. Dordrecht, Pays-Bas : Kluwer Academic.
- Ashby, M. P. et Bowers, K. J. (2013). A comparison of methods for temporal analysis of aoristic crime. Crime Science, 2(1), 1-16.
- Awan, I. (2014). Islamophobia and Twitter : A typology of online hate against Muslims on social media. Policy & Internet, 6(2), 133-150.
- Bendler, J., Brandt, T., Wagner, S. et Neumann, D. (2014). Investigating crime-to-Twitter relationships in urban environments-facilitating a virtual neighborhood watch. Proceedings of the European Conference on Information Systems (ECIS).
- Bendler, J., Ratku, A. et Neumann, D. (2014). Crime mapping through geo-spatial social media activity. Proceedings of 14th International Conference on Information Systems (ICIS14).
- Bermingham, A. et Smeaton, A. (2011). On using Twitter to monitor political sentiment and predict election results. Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011), 2-10.
- Boivin, R. (2013). On the use of crime rates. Canadian Journal of Criminology and Criminal Justice, 55(2), 263-277.
- Boivin, R. et D’Elia, M. (2017). A network of neighborhoods : Predicting crime trips in a large Canadian city. Journal of research in crime and delinquency, 54(6), 824-846.
- Boivin, R. et Ouellet, F. (2011). La dynamique de la criminalité à Montréal : l’écologie criminelle revisitée. Canadian Journal of Criminology and Criminal Justice, 53(2), 189-216.
- Bollen, J., Mao, H. et Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.
- Braga, A., Papachristos, A. et Hureau, D. (2012). Hot spots policing effects on crime. Campbell Systematic Reviews, 8(8), 1-96.
- Braga, A. A., Papachristos, A. V. et Hureau, D. M. (2010). The concentration and stability of gun violence at micro places in Boston, 1980–2008. Journal of Quantitative Criminology, 26(1), 33-53.
- Budd, T. (2001). Burglary : Practice messages from the British crime survey. Great Britain Home Office, Policing and Reducing Crime Unit.
- Burnap, P., Rana, O. F., Avis, N., Williams, M., Housley, W., Edwards, A.,… Sloan, L. (2015). Detecting tension in online communities with computational Twitter analysis. Technological Forecasting and Social Change, 95, 96-108.
- Burnap, P. et Williams, M. L. (2015). Cyber hate speech on Twitter : An application of machine classification and statistical modeling for policy and decision making. Policy & Internet, 7(2), 223-242.
- Chen, X., Cho, Y. et Jang, S. Y. (2015). Crime prediction using Twitter sentiment and weather. Systems and Information Engineering Design Symposium (SIEDS), 63-68.
- Curman, A. S., Andresen, M. A. et Brantingham, P. J. (2015). Crime and place : A longitudinal examination of street segment patterns in Vancouver, BC. Journal of Quantitative Criminology, 31(1), 127-147.
- Eck, J. E. et Spelman, W. (1987). Problem-solving : Problem-oriented policing in Newport News. Washington, DC : National Institute of Justice.
- Eck, J. E., Chainey, S., Cameron, J. G., Leitner, M. et Wilson, R. E. (2005). Mapping crime : Understanding hot spots. Washington, DC : National Institute of Justice.
- Eisenstein, J. (2013). What to do about bad language on the internet. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies, Association for Computational Linguistics, 359-369.
- Felson, M. et Boivin, R. (2015). Daily crime flows within a city. Crime Science, 4(1).
- Gerber, M. S. (2014). Predicting crime using Twitter and kernel density estimation. Decision Support Systems, 61, 115-125. https://doi.org/10.1016/j.dss.2014.02.003
- Gu, Y., Qian, Z. S. et Chen, F. (2016). From Twitter to detector : Real-time traffic incident detection using social media data. Transportation Research Part C : Emerging Technologies, 67, 321-342.
- Haberman, C. P. et Ratcliffe, J. H. (2015). Testing for temporally differentiated relationships among potentially criminogenic places and census block street robbery counts. Criminology, 53(3), 457-483.
- Haberman, C. P. (2017). Overlapping Hot Spots ? Criminology & Public Policy, 16(2), 633-660.
- Haberman, C. P., Sorg, E. T. et Ratcliffe, J. H. (2017). Assessing the validity of the law of crime concentration across different temporal scales. Journal of Quantitative Criminology, 33(3), 547-567.
- Kim, J., Cha, M. et Sandholm, T. (2014). SocRoutes : Safe routes based on tweet sentiments. Proceedings of the 23rd International Conference on World Wide Web, 179-182.
- Koper, C. S. (1995). Just enough police presence : Reducing crime and disorderly behavior by optimizing patrol time in crime hot spots. Justice Quarterly, 12(4), 649-672.
- Malleson, N. et Andresen, M. A. (2015a). Spatio-temporal crime hotspots and the ambient population. Crime science, 4(1), 10-17.
- Malleson, N. et Andresen, M. A. (2015b). The impact of using social media data in crime rate calculations : Shifting hot spots and changing spatial patterns. Cartography and Geographic Information Science, 42(2), 112-121.
- Malleson, N. et Andresen, M. A. (2016). Exploring the impact of ambient population measures on London crime hotspots. Journal of Criminal Justice, 46, 52-63.
- Mellon, J. et Prosser, C. (2017). Twitter and Facebook are not representative of the general population : Political attitudes and demographics of British social media users. Research & Politics, 4(3), 2053168017720008.
- Mohammad, S. M. et Turney, P. D. (2013). Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29, 436-465.
- Osgood, D. W. (2000). Poisson-based regression analysis of aggregate crime rates. Journal of Quantitative Criminology, 16(1), 21-43.
- Poblete, B., Garcia, R., Mendoza, M. et Jaimes, A. (2011,). Do all birds tweet the same ? : characterizing twitter around the world. Proceedings of the 20th ACM international conference on Information and knowledge management, 1025-1030. ACM.
- Prathap, B. R. et Ramesha, K. (2018). Twitter sentiment for analyzing different types of crimes. 2018 International Conference on Communication, Computing and Internet of Things (IC3IoT), 483-488.
- Ratcliffe, J. H. (2000). Aoristic analysis : The spatial interpretation of unspecific temporal events. International Journal of Geographical Information Science, 14(7), 669-679.
- Ratcliffe, J. H. (2002). Aoristic signatures and the spatio-temporal analysis of high volume crime patterns. Journal of Quantitative Criminology, 18(1), 23-43.
- Ratcliffe, J. H. (2004). The hotspot matrix : A framework for the spatio-temporal targeting of crime reduction. Police practice and research, 5(1), 5-23.
- Ratcliffe, J., Groff, E., Haberman, C. et Sorg, E. (2012). Smart policing initiative final report. Philadelphie, PA : Temple University Center for Security and Crime Science.
- Rosser, G., Davies, T., Bowers, K. J., Johnson, S. D. et Cheng, T. (2017). Predictive crime mapping : Arbitrary grids or street networks ? Journal of Quantitative Criminology, 33(3), 569-594.
- Shaw, C. R. et McKay H. D. (1942). Juvenile delinquency and urban areas : A study of rates of delinquents in relation to differential characteristics of local communities in American cities. Chicago, IL : University of Chicago Press.
- Sherman, L. W., Gartin, P. R. et Buerger, M. E. (1989). Hot spots of predatory crime : Routine activities and the criminology of place. Criminology, 27(1), 27-56.
- Sherman, L. W. et Weisburd, D. (1995). General deterrent effects of police patrol in crime “hot spots” : A randomized, controlled trial. Justice quarterly, 12(4), 625-648.
- Sloan, L., Morgan, J., Burnap, P. et Williams, M. (2015). Who tweets ? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data. PloS one, 10(3), e0115545.
- Statistique Canada. (2018). Secteur de recensement : définition en langage simple. Repéré à https://www150.statcan.gc.ca/n1/pub/92-195-x/2011001/geo/ct-sr/ct-sr-fra.htm
- Steiger, E., Westerholt, R., Resch, B. et Zipf, A. (2015). Twitter as an indicator for whereabouts of people ? Correlating Twitter with UK census data. Computers, Environment and Urban Systems, 54, 255-265.
- Sui, D. et M. Goodchild. (2011). The convergence of GIS and social media : Challenges for GIScience. International Journal of Geographical Information Science 25(11), 1737-1748.
- Wang, X., Gerber, M. S. et Brown, D. E. (2012). Automatic crime prediction using events extracted from Twitter posts. Dans S. J. Yang, A. M. Greenberg et M. Endsley (dir.), Social Computing, Behavioral-Cultural Modeling and Prediction (vol. 7227, p. 231-238). https://doi.org/10.1007/978-3-642-29047-3_28
- Weaver, S. (2013). A rhetorical discourse analysis of online anti-muslim and anti-semitic jokes. Ethnic and Racial Studies, 36(3), 483-499.
- Weisburd, D. (2015). The law of crime concentration and the criminology of place. Criminology, 53(2), 133-157.
- Weisburd, D., Groff, E. R. et Yang, S. M. (2012). The criminology of place : Street segments and our understanding of the crime problem. Oxford University Press.
- Weisburd, D. et Telep, C. W. (2014). Hot spots policing : What we know and what we need to know. Journal of Contemporary Criminal Justice, 30(2), 200-220.
- Wheeler, A. et Haberman, C. (2018). Modeling the spatial patterns of intra-day crime trends. https://dx.doi.org/10.2139/ssrn.3136030
- Williams, M. L., Burnap, P. et Sloan, L. (2016). Crime sensing with big data : The affordances and limitations of using open source communications to estimate crime patterns. British Journal of Criminology, 57(2), 320-340. http://doi.org/10.1093/bjc/azw031