Gale, C. (2015). Using geodemographics to profile public confidence in the Metropolitan Police Service. Geospatial and Big Data seminar, UCL, London, UK, February 2015.
Shen, J. (2015). London Police Foot Patrol Pattern Analysis. Geospatial and Big Data seminar, UCL, London, UK, February 2015.
Williams, D. (2015). Modeling public confidence in the Met Police. Geospatial and Big Data seminar, UCL, London, UK, February 2015.
Wise, S. (2015). Spatially Explicit Agent-based Modelling. Geospatial and Big Data seminar, UCL, London, UK, January 2015.
Cheng, T., & Adepeju, M. (2014). Modifiable Temporal Unit Problem (MTUP) and its effect on space-time cluster detection. PloS one, 9(6), e100465.
Davies, T. & Johnson, S.D. (2014). Examining the Relationship Between Road Structure and Burglary Risk Via Quantitative Network Analysis. Journal of Quantitative Criminology.
Adepeju, M. & Cheng, T. (2014). Prospective Space-Time Scan Statistics (STSS) for Crime Prediction. In 22nd Annual Popfest conference, London, UK, August, 2014.
Davies, T.P. (2014). Incorporating street network effects in models of crime. Computational Social Science Conference, Warwick, UK, June 2014. Invited talk.
Davies, T.P. & Bowers, K.J. (2014). Street network effects in crime and policing. International Crime Science Conference, London, UK, July 2014.
Davies, T.P. & Bowers, K.J. (2014). Quantifying the relationship between urban form, crime and police activity. American Society of Criminology Annual Meeting, San Francisco, US, November 2014. In preparation.
Rosser, G. (2014). Modelling spatiotemporal crime patterns for predictive policing: ‘crimes as earthquakes’ model. Geospatial and Big Data seminar, UCL, London, UK, October 2014.
Rosser, G. & Cheng, T. (2014). Self-exciting point process models of spatiotemporal crime patterns. International Crime Science Conference, London, UK, July 2014.
Rosser, G. & Cheng, T. (2014). Point process models for prospective crime analysis. GIScience 2014, Vienna, Austria, September 2014.
Bowers, K., and Guerette, R. (2013). Evaluation of Situational Crime Prevention. In Bruinsma, G and Weisburd, D, (eds.) Encyclopedia of Criminology and Criminal Justice. Springer Verlag
Cheng, T., & Adepeju, M. (2013). Detecting emerging space-time crime patterns by prospective STSS. In Proc of the 12th International Conference on GeoComputation, Wuhan, China, May, 2013.
Cheng, T., Tanaksaranond, G., Brunsdon, C., Haworth, J. (2013). Exploratory visualisation of congestion evolutions on urban transport networks, Transportation Research Part C: Emerging Technologies, 36, 296-306.
Chow, A. H.F., Santacreu, A., Tsapakis, I., Tanasaranond, G. and Cheng, T. (2013). Empirical assessment of urban traffic congestion. J. Adv. Transp.
Johnson, S., and Bowers, K. (2013). Near repeats and crime forecasting. In Bruinsma, G and Weisburd, D, (eds.) Encyclopedia of Criminology and Criminal Justice. Springer Verlag.
Skarlatidou, A., Cheng, T., and Haklay, M. (2013). Guidelines for trust interface design for public engagement Web GIS , International Journal of Geographical Information Science, 27(8)
Tsapakis, T., Cheng, T., Bolbol, A. (2013). Impact of weather conditions on macroscopic urban travel times, Journal of Transport Geography, 28, 204-211.
Adepeju, M. & Cheng, T. (2013). Implications of the difference between the Prospective and Retrospective Space-time scan statistics. ECTQG Dourdan, France, September, 2013.
Adepeju, M., Cheng, T., & Nakaya, T. (2013). Crime pattern detection for predictive policing. AAG Annual Meeting, Florida, United States, April, 2014.
Bolbol, A., Cheng, T., Tsapakis, I., & Haworth, J. (2012). Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification. Computers, Environment and Urban Systems.
Cheng T., Haworth J. and Manley E. (2012). Advances in Geocomputation (1996-2011). Computers, Environment and Urban Systems, in press.
Cheng T., Haworth J. and Wang J. (2012). Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems, 14, 389-413.
Haworth, J., & Cheng, T. (2012). Non-parametric regression for space–time forecasting under missing data. Computers, Environment and Urban Systems.
Johnson, S., and Bowers, K. (2012). Crime Displacement and Diffusion of Benefits; A review of situational crime prevention measures. In Welsh, BC and Farrington, DP, (eds.) The Oxford Handbook of Crime Prevention. Oxford University Press.
Johnson, S., Bowers, K., and Pease, K. (2012). Towards the Modest Predictability of Daily Burglary Counts. Policing, 6(2), 167-176.
Cheng T., Wang J., and Li X. (2011). A hybrid framework for space-time modelling of environmental data. Geographical Analysis, 43(2), 188-210.
Adnan M., Longley P.A., Singleton A.D. and Brunsdon C. (2010). Towards real-time geodemographics: clustering algorithm performance for large multidimensional spatial databases. Transactions in GIS, 14: 283-97.
Cheng T. and Anbaroglu B. (2010). Spatio-temporal clustering of road network data. International Conf. on Artificial Intelligence and Computer Intelligence, LNCS, 6319: 116-123.
Wang J., Cheng T. and Haworth J. (2010). Space-time kernels. In: Shi, W and Goodchild, M and Lees, B, (eds.) Advances in geospatial information science. CRC Press: Leiden, NL.
Cheng T. and Wang J. (2009). Accommodating spatial associations in DRNN for space–time analysis. Computer, Environmental, and Urban System. 33: 409-418.
Cheng T. and Wang, J. (2008). Integrated spatio-temporal data mining for forest fire prediction.Transactions in GIS, 12: 591-611.
Ashby D.I. and Longley P.A. (2005). Geocomputation, geodemographics and resource allocation for local policing. Transactions in GIS 9: 53-72.
Bowers K.J. and Johnson S.D. (2005). Domestic burglary repeats and space–time clusters: the dimensions of risk. European Journal of Criminology 2:67-92.
Longley PA, (2005). A renaissance of geodemographics for public service delivery. Progress in Human Geography, 29: 57-63
Shawe-Taylor J. and Cristianini N. (2004). Kernel Methods for Pattern Analysis.
Shawe-Taylor J. and Cristianini N. (2000). An Introduction to Support Vector Machines.