Be sure to also check out our Presentations page!

2015

Adepeju, M., Cheng, T., Shawe-Taylor, J., & Bowers, K. (2015) A new metric of crime hotspots for operational Policing. GISRUK 2015 Conference (Leeds, UK).

Davies, T. & Bowers, K. (2015) Quantifying the deterrent effect of police patrol via GPS analysis. GISRUK 2015 Conference (Leeds, UK).

Kowalska, K., Shawe-Taylor, J., & Longley, P. (2015) Data-driven modelling of police route choice. GISRUK 2015 Conference (Leeds, UK).

Lai, J., Cheng, T., & Lansley, G. (2015) Spatio-Temporal Patterns of Passengers’ Interests at London Tube Stations. GISRUK 2015 Conference (Leeds, UK).

Rosser, G. & Cheng, T. (2015) A self-exciting point process model for predictive policing: implementation and evaluation. GISRUK 2015 Conference (Leeds, UK).

Shen, J. & Cheng, T. (2015) Group Behaviour Analysis of London Foot Patrol Police. GISRUK 2015 Conference (Leeds, UK).

Williams, D., Haworth, J., & Cheng, T. (2015) Exploratory spatiotemporal data analysis of public confidence in the police in London. GISRUK 2015 Conference (Leeds, UK).

Wise, S. & Cheng, T. (2015) A Model Officer: An Agent-based Model of Policing. GISRUK 2015 Conference (Leeds, UK).

2014

daviesJohnson2014Burglary

Visualisation of street segments coloured according to their betweenness for (a) the entire street network of Birmingham, and (b) one smaller section. Taken from Davies & Johnson, 2014.

Adepeju, M., Cheng, T., Shawe-Taylor, J., & Bowers, K. (2014). A new metric of crime hotspots for Operational Policing. In Proc of the 23rd GIS Research UK (GISRUK) Conference (Leeds, UK).

Cheng, T., & Adepeju, M. (2014). Detecting emerging space-time crime patterns by prospective STSS. In Proc of the 12th International Conference on GeoComputation (Wuhan, China).

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.

2013

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.

Cheng et al, 2012

Studying autocorrelation between locations at various times of day. Excerpt from Cheng et al, 2012.

2012

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.

Visualisation of study area with dataset utilised in Bolbol et al, 2012.

Visualisation of study area with dataset utilised in Bolbol et al, 2012.

Older Publications

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.