Spatial crime distribution and prediction for sporting events using social media

References

• Adepeju, M., Rosser, G., and Cheng, T., 2016. Novel evaluation metrics for sparse spatio-temporal point process hotspot predictions-a crime case study. International Journal of Geographical Information Science, 30 (11), 2133–2154. doi:10.1080/13658816.2016.1159684
   Web of Science ®Google Scholar
• Al Boni, M. and Gerber, M.S., 2016a. Area-specific crime prediction models. 2016 15th IEEE International Conference On Machine Learning And Applications (ICMLA), Anaheim, CA, USA, 671–676.
   Google Scholar
• Al Boni, M. and Gerber, M.S., 2016b. Automatic optimization of localized kernel density estimation for hotspot policing. 2016 15th IEEE international conference on Machine Learning and Applications (ICMLA), Anaheim, CA, 32–38.
   Google Scholar
• Al Boni, M. and Gerber, M.S., 2016c. Predicting crime with routine activity patterns inferred from social media. IEEE international conference on Systems, Man, and Cybernetics (SMC), 9 Oct 2016 Budapest, Hungary, 001233–001238.
   Google Scholar
• Alqhtani, S.M., Luo, S., and Regan, B., 2015. Fusing text and image for event detection in twitter. arXiv Preprint arXiv:1503.03920, 7 (1), 27–35.
   Google Scholar
• Alruily, M., 2012. Using text mining to identify crime patterns from arabic crime news report corpus. PhD (Doctor of Philosophy). DeMontfort University. doi:10.1094/PDIS-11-11-0999-PDN
   Google Scholar
• Andresen, M.A., 2011. The ambient population and crime analysis. The Professional Geographer, 63 (2), 193–212. doi:10.1080/00330124.2010.547151
   Web of Science ®Google Scholar
• Andresen, M.A. and Linning, S.J., 2012. The (in) appropriateness of aggregating across crime types. Applied Geography, 35 (1), 275–282. doi:10.1016/j.apgeog.2012.07.007
   Web of Science ®Google Scholar
• Andresen, M.A. and Tong, W., 2012. The impact of the 2010 winter olympic games on crime in vancouver 1. Canadian Journal of Criminology and Criminal Justice, 54 (3), 333–361. doi:10.3138/cjccj.2011.E44
   Web of Science ®Google Scholar
• Anselin, L., 1995. Local indicators of spatial association—LISA. Geographical Analysis, 27 (2), 93–115. doi:10.1111/j.1538-4632.1995.tb00338.x
   Web of Science ®Google Scholar
• Anselin, L. and Kelejian, H.H., 1997. Testing for spatial error autocorrelation in the presence of endogenous regressors. International Regional Science Review, 20 (1–2), 153–182. doi:10.1177/016001769702000109
   Web of Science ®Google Scholar
• Anselin, L., Syabri, I., and Kho, Y., 2006. GeoDa: an introduction to spatial data analysis. Geographical Analysis, 38 (1), 5–22. doi:10.1111/gean.2006.38.issue-1
   Web of Science ®Google Scholar
• Bendler, J., et al., 2014a. Investigating crime-to-twitter relationships in urban environments-facilitating a virtual neighborhood watch. 22nd European Conference on Information Systems (ECIS), Tel Aviv, Israel.
   Google Scholar
• Bendler, J., Ratku, A., and Neumann, D., 2014b. Crime mapping through geo-spatial social media activity. International conference on information systems, 12–15. 2014b International Conference On Information Systems. doi:10.1080/08998280.2014.11929037
   Google Scholar
• Block, R., 2000. Gang activity and overall levels of crime: a new mapping tool for defining areas of gang activity using police records. Journal of Quantitative Criminology, 16 (3), 369–383. doi:10.1023/A:1007579007011
   Web of Science ®Google Scholar
• Bogomolov, A., et al., 2014. Once upon a crime: towards crime prediction from demographics and mobile data. Proceedings of the 16th international conference on multimodal interaction, Istanbul, Turkey, 427–434.
   Google Scholar
• Botta, F., Moat, H.S., and Preis, T., 2015. Quantifying crowd size with mobile phone and twitter data. Royal Society Open Science, 2 (5), 150162. doi:10.1098/rsos.150162
   PubMed Web of Science ®Google Scholar
• Braga, A.A., 2005. Hot spots policing and crime prevention: A systematic review of randomized controlled trials. Journal of Experimental Criminology, 1 (3), 317–342. doi:10.1007/s11292-005-8133-z
   Google Scholar
• Braga, A.A. and Bond, B.J., 2008. Policing crime and disorder hot spots: A randomized controlled trial*. Criminology, 46 (3), 577–607. doi:10.1111/crim.2008.46.issue-3
   Web of Science ®Google Scholar
• Braga, A.A., Papachristos, A.V., and Hureau, D.M., 2014. The effects of hot spots policing on crime: an updated systematic review and meta-analysis. Justice Quarterly, 31 (4), 633–663. doi:10.1080/07418825.2012.673632
   Web of Science ®Google Scholar
• Brantingham, P. and Brantingham, P., 1995. Criminality of place. European Journal on Criminal Policy and Research, 3 (3), 5–26. doi:10.1007/BF02242925
   Google Scholar
• Brantingham, P.J. and Brantingham, P.L., 1981. Environmental criminology. Hills, CA: Sage Publications Beverly.
   Google Scholar
• Brantingham, P.L. and Brantingham, P.J., 1993. Environment, routine and situation: toward a pattern theory of crime. Advances in Criminological Theory, 5 (2), 259–294.
   Google Scholar
• Bright, E.A., Rose, A.N., and Urban, M.L., 2016. Landscan 2015 high-resolution global population data set. Oak Ridge, TN (United States): Oak Ridge National Lab. (ORNL).
   Google Scholar
• Brimicombe, A. and Cafe, R., 2012. Beware, win or lose: domestic violence and the world cup. Significance, 9 (5), 32–35. doi:10.1111/j.1740-9713.2012.00606.x
   Google Scholar
• Burnap, P. and 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. doi:10.1002/poi3.85
   Web of Science ®Google Scholar
• Caplan, J.M., Kennedy, L.W., and Miller, J., 2011. Risk terrain modeling: brokering criminological theory and GIS methods for crime forecasting. Justice Quarterly, 28 (2), 360–381. doi:10.1080/07418825.2010.486037
   Web of Science ®Google Scholar
• Caruso, R. and Di Domizio, M., 2013. International hostility and aggressiveness on the soccer pitch: evidence from European championships and world cups for the period 2000–2012. International Area Studies Review, 16 (3), 262–273. doi:10.1177/2233865913499267
   Google Scholar
• Center for Spatial Data Science, 2018. GeoDa data and lab. Chicago, IL: The University of Chicago.
   Google Scholar
• Chainey, S., 2012. Repeat victimisation. JDiBrief series. London: UCL Jill Dando Institute of Security and Crime Science.
   Google Scholar
• Chainey, S., Tompson, L., and Uhlig, S., 2008. The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21 (1), 4–28. doi:10.1057/palgrave.sj.8350066
   Web of Science ®Google Scholar
• Cheng, Z. and Smyth, R., 2015. Crime victimization, neighbourhood safety and happiness in China. Economic Modelling, 51, 424–435. doi:10.1016/j.econmod.2015.08.027
   Web of Science ®Google Scholar
• City of Chicago, 2018. Chicago data portal.
   Google Scholar
• Clarke, R., 2002. Thefts of and from cars in parking facilities. Washington, DC: US Department of Justice, Office of Community Oriented Policing Services.
   Google Scholar
• Cohen, L.E. and Felson, M., 1979. Social change and crime rate trends: a routine activity approach. American Sociological Review, 44 (4), 588–608.
   Web of Science ®Google Scholar
• Copus, R. and Laqueur, H., 2014. Entertainment as crime prevention: evidence from Chicago sports games. Journal of Sports Economics, 20 (3), 344–370. doi:10.1177/1527002518762551
   Google Scholar
• Corney, D., Martin, C., and Göker, A., 2014. Spot the ball: detecting sports events on twitter. Advances in Information Retrieval. Springer, 449–454.
   Google Scholar
• Curtis, A.J., Mills, J.W., and Leitner, M., 2006. Spatial confidentiality and GIS: re-engineering mortality locations from published maps about Hurricane Katrina. International Journal of Health Geographics, 5 (1), 44. doi:10.1186/1476-072X-5-44
   PubMedGoogle Scholar
• Davidson, T., et al., 2017. Automated hate speech detection and the problem of offensive language. 11th international AAAI conference on web and social media, 15-18 May Montreal, Quebec, Canada.
   Google Scholar
• Dunning, E., Murphy, P.J., and Williams, J., 2014. The roots of football hooliganism (RLE sports studies): an historical and sociological study. London, UK: Routledge.
   Google Scholar
• ESPN, 2018. NBA attendance report [online]. Available from: http://dynamic.espn.com/nba/attendance?year=2014&sort=allAvg [Accessed 30 Aug 2018].
   Google Scholar
• ESRI, 2018. ArcGis online: summarize within [online]. Available from: https://doc.arcgis.com/en/arcgis-online/analyze/summarize-within.htm [Accessed 15 September 2018].
   Google Scholar
• Fan, R.-E., et al., 2008. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9 (Aug), 1871–1874.
   Google Scholar
• Fawcett, T., 2006. An introduction to ROC analysis. Pattern Recognition Letters, 27 (8), 861–874. doi:10.1016/j.patrec.2005.10.010
   Web of Science ®Google Scholar
• Featherstone, C., 2013a. Identifying vehicle descriptions in microblogging text with the aim of reducing or predicting crime. Adaptive Science and Technology (ICAST), Pretoria, South Africa, 1–8.
   Google Scholar
• Featherstone, C., 2013b. The relevance of social media as it applies in South Africa to crime prediction. IST-Africa, Nairobi, Kenya, 1–7.
   Google Scholar
• Federal Bureau of Investigation, 2018. Crime in the United States [online]. Available from: https://ucr.fbi.gov/crime-in-the-u.s/2010/crime-in-the-u.s.-2010/property-crime/mvtheftmain [Accessed 15 August 2018].
   Google Scholar
• Founta, A.-M., et al., 2018. Large scale crowdsourcing and characterization of twitter abusive behavior. 12th international AAAI conference on web and social media, Palo Alto, California, USA.
   Google Scholar
• Fraustino, J.D., Liu, B., and Jin, Y., 2012. Social media use during disasters: a review of the knowledge base and gaps. National Consortium for the Study of Terrorism and Responses to Terrorism. 1–39.
   Google Scholar
• Gabbidon, S.L. and Greene, H.T., 2018. Race and crime. Los Angeles, CA: Sage Publications.
   Google Scholar
• Gao, L., Kuppersmith, A., and Huang, R., 2017. Recognizing explicit and implicit hate speech using a weakly supervised two-path bootstrapping approach. 8th international joint conference on natural language processing, Taipei, Taiwan, 774–782.
   Google Scholar
• Gerber, M.S., 2014. Predicting crime using Twitter and kernel density estimation. Decision Support Systems, 61, 115–125. doi:10.1016/j.dss.2014.02.003
   Web of Science ®Google Scholar
• Gracenote, 2018. Hockey reference, Chicago blackhawks [online]. Available from: https://www.hockey-reference.com/teams/CHI/ [Accessed 10 May 2017].
   Google Scholar
• Groff, E.R. and Lockwood, B., 2014. Criminogenic facilities and crime across street segments in Philadelphia: uncovering evidence about the spatial extent of facility influence. Journal of Research in Crime and Delinquency, 51 (3), 277–314. doi:10.1177/0022427813512494
   Web of Science ®Google Scholar
• Grubesic, T.H. and Pridemore, W.A., 2011. Alcohol outlets and clusters of violence. International Journal of Health Geographics, 10 (1), 30. doi:10.1186/1476-072X-10-30
   PubMed Web of Science ®Google Scholar
• Hannon, L. and DeFina, R., 2005. Violent crime in African American and white neighborhoods: is poverty’s detrimental effect race-specific? Journal of Poverty, 9 (3), 49–67. doi:10.1300/J134v09n03_03
   Google Scholar
• Hatebase, 2018. Hatebase [online]. Available from: https://hatebase.org/[Accessed 8 Aug 2018].
   Google Scholar
• HockeyDB, 2018. Chicago blackhawks yearly attendance graph [online]. Available from: http://www.hockeydb.com/nhl-attendance/att_graph.php?tmi=5218 [Accessed 30 Aug 2018].
   Google Scholar
• Hoeben, E.M., et al. 2014. The space-time budget method in criminological research. Crime Science, 3 (1), 12. doi:10.1186/s40163-014-0012-3
   Google Scholar
• Hu, Y., 2014. Event analytics on social media: challenges and solutions. (Doctor of Philosophy). Arizona State University.
   Google Scholar
• Hu, Y., et al., 2018. A spatio-temporal kernel density estimation framework for predictive crime hotspot mapping and evaluation. Applied Geography, 99, 89–97. doi:10.1016/j.apgeog.2018.08.001
   Web of Science ®Google Scholar
• Kadar, C., Brüngger, R.R., and Pletikosa, I., 2017. Measuring ambient population from location-based social networks to describe urban crime. International conference on social informatics, Oxford, UK, 521–535.
   Google Scholar
• Kadar, C., Iria, J., and Cvijikj, I.P., 2016. Exploring foursquare-derived features for crime prediction in New York City. The 5th international workshop on urban computing (UrbComp 2016), San Francisco, CA.
   Google Scholar
• Kadar, C. and Pletikosa, I., 2018. Mining large-scale human mobility data for long-term crime prediction. EPJ Data Science, 7 (1), 26. doi:10.1140/epjds/s13688-018-0150-z
   Google Scholar
• Kennedy, L.W., Caplan, J.M., and Piza, E., 2011. Risk clusters, hotspots, and spatial intelligence: risk terrain modeling as an algorithm for police resource allocation strategies. Journal of Quantitative Criminology, 27 (3), 339–362. doi:10.1007/s10940-010-9126-2
   Web of Science ®Google Scholar
• Kinney, J.B., et al. 2008. Crime attractors, generators and detractors: land use and urban crime opportunities. Built Environment, 34 (1), 62–74. doi:10.2148/benv.34.1.62
   Google Scholar
• Klein, M.W. and Maxson, C.L., 2010. Street gang patterns and policies. Oxford, UK: Oxford University Press.
   Google Scholar
• Kounadi, O., et al. 2018. Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies. Cartography and Geographic Information Science, 45 (3), 205–220. doi:10.1080/15230406.2017.1304243
   PubMed Web of Science ®Google Scholar
• Kurland, J., 2014. The ecology of football-related crime and disorder. Dissertation (Ph.D.). University College London.
   Google Scholar
• Kurland, J., Johnson, S., and Tilley, N., 2017. Hotspotting and football violence: current statistics and implications for prevention. In: Peter Sturmey, ed. The wiley handbook of violence and aggression. John Wiley & Sons, 1–15.
   Google Scholar
• Kurland, J., Tilley, N., and Johnson, S.D., 2014. The football ‘Hotspot’Matrix. In: Matt Hopkins and Treadwell James, eds. Football hooliganism, fan behaviour and crime: contemporary issues, London, UK: Palgrave McMillan, 21–48.
   Google Scholar
• Leitner, M., et al. 2011. The impact of Hurricane Katrina on reported crimes in Louisiana: a spatial and temporal analysis. The Professional Geographer, 63 (2), 244–261. doi:10.1080/00330124.2010.547156
   Web of Science ®Google Scholar
• Leitner, M. and Helbich, M., 2011. The impact of hurricanes on crime: a spatio-temporal analysis in the city of Houston, Texas. Cartography and Geographic Information Science, 38 (2), 213–221. doi:10.1559/15230406382213
   Web of Science ®Google Scholar
• Levine, N., 2008. The “Hottest” part of a hotspot: comments on “The utility of hotspot mapping for predicting spatial patterns of crime”. Security Journal, 21 (4), 295–302. doi:10.1057/sj.2008.5
   Web of Science ®Google Scholar
• Lin, Y.-R., 2015. Event-related crowd activities on social media. Social Phenomena. Springer, 235–250.
   Google Scholar
• Malleson, N. and Andresen, M.A., 2015a. 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. doi:10.1080/15230406.2014.905756
   Web of Science ®Google Scholar
• Malleson, N. and Andresen, M.A., 2015b. Spatio-temporal crime hotspots and the ambient population. Crime Science, 4 (1), 1–8. doi:10.1186/s40163-015-0023-8
   Google Scholar
• Malleson, N. and Andresen, M.A., 2016. Exploring the impact of ambient population measures on London crime hotspots. Journal of Criminal Justice, 46, 52–63. doi:10.1016/j.jcrimjus.2016.03.002
   Web of Science ®Google Scholar
• Manson, S., et al., 2018. IPUMS national historical geographic information system: version 13.0 [Database]. Minnesota, Minneapolis: IPUMS.
   Google Scholar
• Marie, O., 2016. Police and thieves in the stadium: measuring the (multiple) effects of football matches on crime. Journal of the Royal Statistical Society: Series A (Statistics in Society), 179 (1), 273–292. doi:10.1111/rssa.12113
   Web of Science ®Google Scholar
• Mohammad, S.M. and Turney, P.D., 2010. Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, Los Angeles, CA, 26–34.
   Google Scholar
• Mohammad, S.M. and Turney, P.D., 2013. Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29 (3), 436–465. doi:10.1111/j.1467-8640.2012.00460.x
   Web of Science ®Google Scholar
• Mohler, G.O., et al., 2012. Self-exciting point process modeling of crime. Journal of the American Statistical Association, 7 (5), e37455.
   Google Scholar
• Montolio, D. and Planells, S., 2016. How time shapes crime: the temporal impacts of football matches on crime. Regional Science and Urban Economics, 61, 99–113. doi:10.1016/j.regsciurbeco.2016.10.001
   Web of Science ®Google Scholar
• Montolio, D. and Planells, S., 2018. Measuring the negative externalities of a private leisure activity: hooligans and pickpockets around the stadium. Journal of Economic Geography, 19 (2), 464–504.
   Web of Science ®Google Scholar
• No swearing, 2018. No swearing [online]. Available from: https://www.noswearing.com/ [Accessed 8 August 2018].
   Google Scholar
• Ohyama, T. and Amemiya, M., 2018. Applying crime prediction techniques to Japan: a comparison between risk terrain modeling and other methods. European Journal on Criminal Policy and Research, 24 (4), 469–487. doi:10.1007/s10610-018-9378-1
   Web of Science ®Google Scholar
• Openshaw, S. and Openshaw, S., 1984. The modifiable areal unit problem.
   Google Scholar
• Perry, W.L., 2013. Predictive policing: the role of crime forecasting in law enforcement operations. Santa Monica, CA: Rand Corporation.
   Google Scholar
• Piza, E., et al. 2017. Place-based correlates of motor vehicle theft and recovery: measuring spatial influence across neighbourhood context. Urban Studies, 54 (13), 2998–3021. doi:10.1177/0042098016664299
   Web of Science ®Google Scholar
• Popescu, A.-M. and Pennacchiotti, M., 2010. Detecting controversial events from twitter. Proceedings of the 19th ACM international conference on Information and knowledge management, Toronto, ON, 1873–1876.
   Google Scholar
• Quillian, L. and Pager, D., 2001. Black neighbors, higher crime? The role of racial stereotypes in evaluations of neighborhood crime. American Journal of Sociology, 107 (3), 717–767. doi:10.1086/338938
   Web of Science ®Google Scholar
• Ristea, A., Andresen, M.A., and Leitner, M., 2018. Using tweets to understand changes in the spatial crime distribution for hockey events in Vancouver. The Canadian Geographer/Le Géographe Canadien, 62 (3), 338–351. doi:10.1111/cag.v62.3
   PubMed Web of Science ®Google Scholar
• Rumi, S.K., Deng, K., and Salim, F.D., 2018. Crime event prediction with dynamic features. EPJ Data Science, 7 (1), 43. doi:10.1140/epjds/s13688-018-0171-7
   Google Scholar
• Rummens, A., Hardyns, W., and Pauwels, L., 2017. The use of predictive analysis in spatiotemporal crime forecasting: building and testing a model in an urban context. Applied Geography, 86, 255–261. doi:10.1016/j.apgeog.2017.06.011
   Web of Science ®Google Scholar
• Schmidt, A. and Wiegand, M., 2017. A survey on hate speech detection using natural language processing. Proceedings of the fifth international workshop on natural language processing for social media, Boston, MA, 1-10.
   Google Scholar
• Smith, M.D., 1979. Towards an explanation of hockey violence: A reference other approach. Canadian Journal of Sociology/Cahiers Canadiens De Sociologie, 4.2, 105–124.
   Google Scholar
• Sportradar, 2018. Basketball reference, Chicago bulls [online]. Available from: https://www.basketball-reference.com/teams/CHI/ [Accessed 10 May 2017].
   Google Scholar
• Struse, S.P. and Montolio, D., 2014. The effect of football matches on crime patterns in Barcelona. 54th congress of the european regional science association: “regional development & globalisation: best practices”, 26-29 August, Saint Petersburg, Russia.
   Google Scholar
• Twitter Inc., 2018. Twitter API documentation [online]. Available from: https://dev.twitter.com/overview/documentation [Accessed 14 April 2014].
   Google Scholar
• United Center, 2018. United center [online]. Available from: http://www.unitedcenter.com/ [Accessed 7 May 2017].
   Google Scholar
• Vomfell, L., Härdle, W.K., and Lessmann, S., 2018. Improving crime count forecasts using twitter and taxi data. Decision Support Systems, 113, 73–85. doi:10.1016/j.dss.2018.07.003
   Web of Science ®Google Scholar
• Wang, H., et al., 2016. Crime rate inference with big data. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA, 635–644.
   Google Scholar
• Wang, M. and Gerber, M.S., 2015. Using twitter for next-place prediction, with an application to crime prediction. Computational intelligence, 2015 IEEE symposium series on, Cape Town, South Africa, 941–948.
   Google Scholar
• Wang, X. and Brown, D.E., 2011. The spatio-temporal generalized additive model for criminal incidents. Intelligence and Security Informatics (ISI), 2011 IEEE international conference on, 42–47. doi:10.1177/1753193411414628
   Google Scholar
• Wang, X., Gerber, M.S., and Brown, D.E., 2012. Automatic crime prediction using events extracted from twitter posts. In: S.J. Yang, A.M. Greenberg, and M. Endsley, eds. Social computing, behavioral-cultural modeling and prediction. Berlin, Heidelberg: Springer, 231–238.
   Google Scholar
• Weitzer, R., 2017. Theorizing racial discord over policing before and after ferguson. Justice Quarterly, 34 (7), 1129–1153. doi:10.1080/07418825.2017.1362461
   Web of Science ®Google Scholar
• Wood, S., McInnes, M.M., and Norton, D.A., 2011. The bad thing about good games: the relationship between close sporting events and game-day traffic fatalities. Journal of Consumer Research, 38 (4), 611–621. doi:10.1086/660164
   Web of Science ®Google Scholar
• Yang, D., et al., 2018. CrimeTelescope: crime hotspot prediction based on urban and social media data fusion. World Wide Web, 21 (5), 1323–1347. doi:10.1007/s11280-017-0515-4
   Google Scholar
• Yu, C.-H., et al., 2011. Crime forecasting using data mining techniques. 2011 IEEE 11th International Conference on Data Mininig Workshops (ICDMW), Vancouver, British Columbia, 779–786.
   Google Scholar
• Yu, Y., et al. 2016. Athletic contests and individual robberies: an analysis based on hourly crime data. Applied Economics, 48 (8), 723–730. doi:10.1080/00036846.2015.1085645
   Web of Science ®Google Scholar
• Zhang, H., Suresh, G., and Qiu, Y., 2012. Issues in the aggregation and spatial analysis of neighborhood crime. Annals of GIS, 18 (3), 173–183. doi:10.1080/19475683.2012.691901
   Google Scholar
• Zhang, Z., et al., 2016. Mining transportation information from social media for planned and unplanned events. Buffalo, NY United States: University at Buffalo.
   Google Scholar
• Zhao, L., et al., 2015. Spatiotemporal event forecasting in social media. SIAM, Vancouver, British Columbia, 963–971.
   Google Scholar
• Zhao, X. and Tang, J., 2017. Modeling temporal-spatial correlations for crime prediction. Proceedings of the 2017 ACM on conference on information and knowledge management, 497–506. doi:10.1142/S0218810417500563
   Google Scholar
• Zhao, X. and Tang, J., 2018. Crime in urban areas: a data mining perspective. ACM SIGKDD Explorations Newsletter, 20 (1), 1–12. doi:10.1145/3229329
   PubMedGoogle Scholar