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SpaceTimeLab

SpaceTimeLab’s mission is to generate actionable insights from geo-located and time-stamped data for government, business and society.

About 

Working with the private and public sectors, SpaceTimeLab uses integrated space-time thinking and a multi-disciplinary approach to develop theories, algorithms and platforms to gain insight from geo-located and time-stamped data, in order to engineer solutions to improve the mobility, security, health and resilience of urban living. 

Find out more about our research topics and data sources by looking at our leaflets: 

 

 

Please note, links on this page may direct to external sites.


People 

Academic Staff
  • Professor Tao Cheng, Director
  • Dr James Haworth
Research Staff

Research staff are current post doctoral research associates working on a range of projects in the SpaceTimeLab 

  • Chris Gale
  • Sarah Wise
  • Khaled Taalab
  • Gabriel Rosser
PhD Students 

Current research students:

  • Dawn Rollocks
  • Huanfa Chen
  • Juntao Lai
  • Nilufer Sari Aslam
  • Yang Zhang
  • Davide Di Gioia
  • Mohamed Ibrahim
  • Tongxin Chen
  • Thanos Bantis
Academic Collaborators 

The following staff have close ties with SpaceTimeLab, and work in either CEGE or associated UCL Departments. Many co-supervise our PhD students, or they are Co-Investigators in our projects.

  • Professor Kate Bowers
  • Dr Toby Davies
  • Dr Claire Ellul
  • Professor Muki Haklay
  • Professor Benjamin Heydecker
  • Dr Andrew Hudson-Smith
  • Professor Paul Longley
  • Professor Alan Penn
  • Professor John Shawe-Taylor
Advisors 

Advisors offer support, supervision and advice to students and researchers working in the SpaceTimeLab

Honorary Members 

Honorary members comprise former PhD students and post-doctoral research associates who now maintain close links with the SpaceTimeLab.

Visiting Researchers 

Every year the UCL SpaceTimeLab is happy to host a small number of outstanding visiting researchers and doctoral students. We especially welcome those who closely share our research interests of big-data analytics and network complexity. If you are appointed, you will  work closely with members of the SpaceTimeLab to develop an research plan which will be mutually beneficial. In order to make the most effective use of your time with us, we recommend that plans are made well before your visit, typically several months in advance. 

For more information, please see the UCL website: https://www.ucl.ac.uk/multidisciplinary-and-intercultural-inquiry/affiliate-academics or contact Prof Tao Cheng (tao.cheng@ucl.ac.uk

Prospective visiting research students should vist the UCL International Students website for more information. 

Previous visiting researchers/doctoral students

  • Prof Tomoki Nakaya (Ritsumeikan University)  
  • Prof Xinhai Lu (Huazhong University of Science and Technology) 
  • Dr Igor Ivan (Technical University of Ostrava) 
  • Prof Ge Chen (Ocean Remote Sensing Institute) 
  • Dr Yibin Ren 

Current visiting researchers/doctoral students

  • Dr Di Zhu
Completed PhDs 
  • Dr Artemis Skarlatidou 
  • Dr Ed Manley 
  • Dr Berk Anbaroglu 
  • Dr Adel Bolbol
  • Dr Garavig Tanaksaranond 
  • Dr James Haworth 
  • Dr Monsuru Adepeju 
  • Dr Jianan Shen 

Research 

SpaceTimeLab has a varied portfolio of research that spans the following four key research themes:

Transport & Mobility 

The transportation system is the lifeblood of the city. At SpaceTimeLab, we work to ensure the health of the city by improving the mobility of its citizens and the function of its transportation system. SpaceTimeLab is working on a number of projects (e.g. STANDARD) using large, spatio-temporal transportation datasets to categorise, cluster and profile people and places. 

Examples of our work include:

  • Prediction, simulation and visualisation of urban traffic flows
  • Identifying flexible travellers and groups using smart card data
  • Modelling the impact of engineering work and incidents on London tube lines
  • Developing mobility solutions for cyclists using cycle hire and tracking app data
  • Detecting hybrid travel modes from sparse GPS data
Security and Policing 

The safety and security of the public relies on police agencies maximising their use of the diverse geo-temporal data sources available to them. Collaborating with the Metropolitan Police Service, the Crime, Policing and Citizenship (CPC) project analyses and models the relationship between police activities, crime and public attitudes towards policing. CPC focuses on:

  • Street network based crime prediction
  • Police patrol behavioural analysis
  • Optimal patrol routing in real-time
  • Agent-based simulation for strategic planning
  • Spatio-temporal analysis of public confidence
  • Supply and demand of police patrol coverage
Business Intelligence 

Many facets of business and commerce can be rationalised and improved through an understanding of the spatio-temporal patterns underlying consumer behaviour (e.g. CDRC). Examples include:

  • Insurance fraud
  • Footfall and catchment area estimation
  • Consumer profiling
  • Fleet and logistic management
Environmental Resilience 

Advancing knowledge and understanding of natural hazards and developing novel risk assessment methods is a core part of the SpaceTimeLab research agenda. Recent work includes:

  • Hazard susceptibility mapping
  • Hazard risk assessment
  • Flood forecasting
  • Forest fire prediction

SpaceTimeLab is one of 11 partners in INFRARISK, a European Commission project that aims to test the resilience of critical infrastructure networks to a variety of natural hazards.

Current and past projects 

The Consumer Data Research Centre (2014 - present)

SpaceTimeLab is one of the partners of the Consumer Data Research Centre (CDRC). CDRC was established by the UK Economic and Social Research Council to:

  • Contribute towards ensuring the future sustainability of UK research using consumer data
  • Support consumer related organisations to maximise their innovation potential
  • Drive economic growth

We are bringing together world-class researchers from the University of Leeds, University College London, University of Liverpool and the University of Oxford to offer a range of expert services to a wide range of users.

You can learn more about the project at the CDRC website.

INFRARISK (2013 - 2016)

The achievements of the European Union targets regarding energy and socio-economic sustainability are highly dependent on the way risks and vulnerabilities of European operating infrastructure networks and critical assets are minimised against natural extreme events.  The INFRARISK project will develop reliable stress tests on European critical infrastructure using integrated modelling tools for decision-support. It will lead to higher infrastructure networks resilience to rare and low probability extreme events, known as “black swans”. INFRARISK will advance decision making approaches and lead to better protection of existing infrastructure while achieving more robust strategies for the development of new ones. INFRARISK proposes to expand existing stress test procedures and adapt them to critical land-based infrastructure which may be exposed to or threatened by natural hazards. Integrated risk mitigation scenarios and strategies will be employed, using local, national and pan-European infrastructure risk analysis methodologies. These will take into consideration multiple hazards and risks with cascading impact assessments. 

The INFRARISK approach will robustly model spatio-temporal processes with propagated dynamic uncertainties in multiple risk complexity scenarios of Known Unknowns and Unknown Unknowns.  An operational framework with cascading hazards, impacts and dependent geospatial vulnerabilities will be developed. This framework will be a central driver to practical software tools and guidelines that provide greater support to the next generation of European infrastructure managers to analyse and handle scenarios of extreme events.  The minimisation of the impact of such events by the supporting tools shall establish optimum mitigation measures and rapid response.  INFRASRISK will deliver a collaborative integrated platform where risk management professionals access and share data, information and risk scenarios results efficiently and intuitively.

Crime Policing and Citizenship (CPC) (2012 - 2016)

The UCL Crime, Policing and Citizenship (CPC) project aims to investigate the relationship between detailed patterns of police activities and the space-time pattern of recorded incidents and public perceptions of crime. The project is being carried out in collaboration with the Metropolitan Police, with support from the UK Engineering and Physical Sciences Research Council (EPSRC).

For more information on the CPC project visit our Website  https://www.ucl.ac.uk/cpc/

Spatio-Temporal Analysis of Network Data and Route Dynamics (STANDARD) (2009 -2012)

UCL together with Transport for London (TfL)  work on a 3-year EPSRC-funded research project to investigate spatio-temporal characteristics of data from transport networks. The Spatio-Temporal Analysis of Network Data and Route Dynamnics (STANDARD) project encompasses and builds upon a broad range of modelling approaches in spatio-temporal analysis, complexity science and state of the art 3D visualisation. 

For more information on the STANDARD project visit our Website http://standard.cege.ucl.ac.uk/workshops/index.htm

Modelling the Changing Surroundings of Everyday Life (2009 - 2012)

Understanding travel behaviour and travel demand is of constant importance to transportation communities and agencies in every country. Modelling this kind of activity on the aggregate scale is very important for applications like measuring time expenditures and quality of life, tourist activity and environmental issues. Nowadays, attempts have been made to automatically infer transportation modes from positional data, such as the data collected by using GPS devices so that the cost in time and budget of conventional travel diary survey could be significantly reduced. Some limitations, however, exist in the literature, in aspects of data collection (sample size selected, duration of study, granularity of data), selection of variables (or combination of variables), and method of inference (the number of transportation modes to be used in the learning). 

This project aims to fully understand these aspects in the process of inference. The work attempts to solve a classification problem of GPS data into different transportation modes (car, walk, cycle, underground, train and bus). We first study the variables that could contribute positively to this classification, and statistically quantify their discriminatory power. We then introduce a novel approach to carry out this inference using a framework based on Support Vector Machines (SVM) classification. We then apply segmentation strategies to identify significant stops that occurred along a person’s track such as home, work, etc. The classification then is subjected to a network matching process that checks whether the identified modes follow their corresponding transport networks to verify the final classification. This project is sponsored by u-blox and EPSRC. 

For more information about this project, please contact Prof Tao Cheng: tao.cheng@ucl.ac.uk

Trust in Web GIS (2007 - 2011)

Attesting to the powerful capabilities and in technology trends, many scholars envisioned the consolidation of Geographic Information Systems (GIS) into vital tools for disseminating spatial information, that are presently used to inform, advise and instruct users in several contexts and to further engage citizens in decision-making processes that can impact and sustain policy development. Interaction with these applications incorporates risk and uncertainty, which have been repeatedly identified as preconditions in nurturing trust perceptions, and which instigate a user’s decision to rely on a system and act on the provided information. 

In a four-year project carried out in collaboration with Arup and with the support from the UK Engineering and Physical Sciences Research Council (EPSRC), Artemis Skarlatidou, used a multidisciplinary research approach derived mainly from the fields of Human-Computer Interaction and Risk Communication, to identify how non-experts' trust perceptions are formed when they interact with environmental Web GIS applications, but also how information about nuclear waste should be communicated to lay people to improve public understanding and trust. The findings supported the development of the PE-Nuclear tool; a Web GIS application to inform lay people in the UK about the site selection of a nuclear waste repository. 

In a different project, also funded by the UK Engineering and Physical Sciences Research Council (EPSRC), we use the same approach to identify how non-experts' trust perceptions are formed when they interact with public crime Web GIS. One part of this research focuses on identifying the user needs and expectations when they interact with different types of crime data at different scales and for different purposes, while the other part aims at building novel crime visualisation approaches which are evaluated for their perceived trustworthiness with non-expert users.

For more information about this project, please contact Dr Artemis Skarlatidou (a.skarlatidou@ucl.ac.uk) or Prof Tao Cheng: (tao.cheng@ucl.ac.uk)

Partnerships

We have been working with many industrial companies and organisations. Our current research projects are supported by the following organisations:


Teaching 

SpaceTimeLab welcomes applications from prospective PhD students from both the UK and overseas. Before applying, please look at our publications to ensure that your research interests match those of the lab. For more information on the requirements and application process, please check the CEGE postgraduate research page and the UCL Doctoral School page.

SpaceTimeLab also has strong links to the following taught MSc programmes: 


Publications 

Journal articles by theme 

Space-time modelling
  • Cheng, T. & Wicks, T. (2014). Event Detection using Twitter: A Spatio-Temporal Approach. Plos One, 9(6), e97807
  • Cheng, T., Wang, J., Haworth, J., Heydecker, B., Chow, A. (2014). A Dynamic Spatial Weight Matrix and Localized Space–Time Autoregressive Integrated Moving Average for Network Modeling, Geographical Analysis. Geographical Analysis, 46: 75–97 DOI: 10.1111/gean.12026
  • Manley, E., Cheng, T., Penn, A., & Emmonds, A. (2014). A framework for simulating large-scale complex urban traffic dynamics through hybrid agent-based modelling. Computers, Environment and Urban Systems44, 27-36. DOI
  • Chow, A. H. F., Santacreu, A., Tsapakis, I., Tanasaranond, G., Cheng, T. (2013). Empirical assessment of urban traffic congestion. Journal of Advanced Transportation doi:10.1002/atr.1241.
  • Dong, J. -. X., Cheng, T., Xu, J., Wu, J. (2013). Quantitative assessment of urban road network hierarchy planning. Town Planning Review 84(4), 445-472
  • 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 UK: doi:10.1016/j.compenvurbsys.2012.06.001. [Accepted]
  • Cheng, T. (2012). Guest editorial: Integrated spatio-temporal analysis and data mining. GeoInformatica 16(4), 623-624 doi:10.1007/s10707-012-0167-6.
  • Cheng, T., Haworth, J., Wang, J. (2012). Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems 14(4), 389-413 doi:10.1007/s10109-011-0149-5.
  • Haworth, J., Cheng, T. (2012). Non-parametric regression for space-time forecasting under missing data. Computers, Environment and Urban Systems36(6), 538-550 doi:10.1016/j.compenvurbsys.2012.08.005.
  • Tsapakis, I., CHENG, T. (2012). Impact of weather conditions on macroscopic urban travel times. Special issue of the Journal of Transportation Geographyhttp://dx.doi.org/10.1016/j.jtrangeo.2012.11.003
  • Tsapakis, I., Turner, J., Cheng, T., Heydecker, B., Emmonds, A., Bolbol, A. (2012). Effects of Tube Strikes on Journey Times in the Transport Network of London. Transportation Research Record 2274: Journal of the Transportation Research Board (TRB) , 84-92 doi:10.3141/2274-09.
  • Tsapakis, I., Schneider, W. H., Nichols, A., Haworth, J. (2012) Alternatives in assigning short-term counts to seasonal adjustment factor groupings. Journal of Advanced Transportation doi:10.1002/atr.1186.
  • Cheng, T., Haworth, J., Wang, J. (2011). Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems doi:10.1007/s10109-011-0149-5.Publisher URL
  • Cheng, T., Wang, J. Q., Li, X. (2011). A Hybrid Framework for Space-Time Modeling of Environmental Data. GEOGR ANAL 43(2), 188-210 doi:10.1111/j.1538-4632.2011.00813.x.
  • Cheng, T., Wang, J. Q. (2009). Accommodating spatial associations in DRNN for space-time analysis. COMPUT ENVIRON URBAN 33(6), 409-418 doi:10.1016/j.compenvurbsys.2009.08.004.
  • Cheng, T., Wang, J. (2008). Integrated spatio-temporal data mining for forest fire prediction. Transactions in GIS 12(5), 591-611 doi:10.1111/j.1467-9671.2008.01117.x.
  • Li, G. Q., Deng, M., Zhu, J. J., Cheng, T. (2008). A new detection method of spatial outliers by considering the distances of spatial entities in their neighbors. Journal of Remote Sensing (in chinese) 12(5), 197-202
  • Chen, J., Liu, W., Li, Z., Zhao, R., Cheng, T. (2007). Detection of spatial conflicts between rivers and contours in digital map updating. INT J GEOGR INF SCI21(10), 1093-1114 doi:10.1080/13658810701300071.
  • Deng, M., Cheng, T., Chen, X., Li, Z. (2007). Multi-level topological relations between spatial regions based upon topological invariants. Geoinformatica 11(2), 239-267 doi:10.1007/s10707-006-0004-x.
  • Wang, J., Cheng, T. (2007). Using spatio-temporal data mining and knowledge discovery for forest fire prevention. Acta Scientiarum Naturalium Universitatis Sun Yat-sen I 46(2), 110-116
  • Chen, J., Liu, W., Li, Z., Cheng T, Z. R. (2006). The refined calculation method of topological relationships between line objects. Acta Geodaetica et Cartographica Sinica 35(3), 255-260
  • Cheng, T., Li, Z. (2006). A Multiscale Approach for Spatio-Temporal Outlier Detection. Transactions in GIS 10(2), 253-263 doi:10.1111/j.1467-9671.2006.00256.x.
  • Cheng, T., Li, Z. (2006). Effect of generalization on area features: A comparative study of two strategies. Cartographic Journal 43(2), 157-170 doi:10.1179/000870406X114649.
  • Cheng, T., Li, Z. (2006). Toward Quantitative Measures for the Semantic Quality of Polygon Generalization. Cartographica 41(2), 135-148 doi:10.3138/0172-6733-227U-8155.
  • Fisher, P., Wood, J., Cheng, T. (2004). Where is Helvellyn? Multiscale morphometry and the mountains of the English Lake District. Transactions of the Institute of British Geographers 29(1), 106-128 doi:10.1111/j.0020-2754.2004.00117.x.
  • Wang, D., Cheng, T. (2001). A spatio-temporal data model for activity-based transport demand modelling. International Journal of Geographical Information Science 15(6), 561-586 doi:10.1080/13658810110046934.Table of Contents (2 levels)
  • Li, D., Cheng, T. (1995). Toward an intelligent GIS by applying KDD. Acta Geodaetica et Cartographica Sinica 24, 37-44
Visualisation
  • 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 doi:10.1016/j.trc.2013.09.001Author URL Publisher URL
  • Koenig, A., Samarasundera, E., Cheng, T. (2011). Interactive map communication: Pilot study of the visual perceptions and preferences of public health practitioners. Public Health 125(8), 554-560 doi:10.1016/j.puhe.2011.02.011
  • Cheng, T., Tanaksaranond, G., Emmonds, A., Sonoiki, D. (2010). Multi-Scale Visualisation of Inbound and Outbound Traffic Delays in London. CARTOGR J47(4), 323-329 doi:10.1179/000870410X12911311788152
  • Zuo, X., Li, Q., Yang, B., Cheng, T. (2007). A view-dependent method for the multi-resolution representation of terrains with roads embedded. International Journal of Remote Sensing 28(2), 319-334 doi:10.1080/01431160600702657
  • Blok, C., Kobben, B., Cheng, T., Kuterema, A. (1999). Visualization of Relationships Between Spatial Patterns in Time by Cartographic Animation. Cartography and Geographic Information Science 26(2), 139-151 doi:10.1559/152304099782330716.
Agent based simulation
  • Manley, E., Cheng, T., Penn, A., & Emmonds, A. (2014). A framework for simulating large-scale complex urban traffic dynamics through hybrid agent-based modelling. Computers, Environment and Urban Systems44, 27-36. DOI
  • Manley, E., Cheng, T., Penn, A., Emmonds, A. (2014). Combining Agent-based Modelling with Macroscopic Traffic Simulation for Large-Scale Behaviourally-Realistic Representations of Urban Traffic Flow. Computers, Environment and Urban Systems. 44. 27-36 http://www.sciencedirect.com/science/article/pii/S0198971513001129
  • Crooks, A. T., Wise, S. (2013). GIS and agent-based models for humanitarian assistance. Computers, Environment and Urban Systems 41, 100-111, C doi:10.1016/j.compenvurbsys.2013.05.003.
  • Wise, S., Crooks, A. T. (2012). Agent-based modeling for community resource management: Acequia-based agriculture. Computers, Environment and Urban Systems 36(6), 562-572 doi:10.1016/j.compenvurbsys.2012.08.004.
Clustering
  • Cheng, T & Adepeju, M. (2014). Modifiable Temporal Unit Problem (MTUP) and its Effect on Space-Time Cluster Detection. PLOS ONE 9(6), e100465
  • Deng, M., Liu, Q., Cheng, T., Shi, Y. (2011). An adaptive spatial clustering algorithm based on delaunay triangulation. Computers, Environment and Urban Systems 35(4), 320-332 UK: doi:10.1016/j.compenvurbsys.2011.02.003.Publisher URL
  • Li, G., Deng, M., Liu, Q., CHENG, T. (2009). A spatial clustering method adaptive to local density change. Acta Geodaetica et Cartographica Sinica 38(3), 255-263 China:
  • Li, G. Q., Deng, M., Cheng, T., Zhu, J. J. (2008). A dual distance based spatial clustering method. Acta Geodaetica et Cartographica Sinica 37(4), 482-488 China:
Trust in GIS
  • Skarlatidou, A., Cheng, T., Haklay, M. (2013). Guidelines for trust interface design for public engagement Web GIS. International Journal of Geographical Information Science doi:10.1080/13658816.2013.766336Publisher URL
  • Skarlatidou, A., Cheng, T., Haklay, M. (2012). What Do Lay People Want to Know About the Disposal of Nuclear Waste? A Mental Model Approach to the Design and Development of an Online Risk Communication. Risk Analysis 32(9), 1496-1511 doi:10.1111/j.1539-6924.2011.01773.xPublisher URL
  • Skarlatidou, A., Haklay, M., CHENG, T. (2011). Trust in Web GIS: The role of the trustee attributes in the design of trustworthy Web GIS applications. International Journal of GIScience 25(12), 1913-1930 doi:10.1080/13658816.2011.557379.Publisher URL
Fuzziness and uncertainty 
  • Cheng, T., Deng, M., Li, Z. (2007). Representation methods of spatial objects with uncertainty and their application in GIS. Geomatics and Information Science of Wuhan University 32(5), 389-393
  • Fisher, P., Cheng, T., Wood, J. (2007). Higher order vagueness in geographical information: Empirical geographical population of type n fuzzy sets. Geoinformatica 11(3), 311-330 doi:10.1007/s10707-006-0009-5.
  • Fisher, P., Wood, J., Cheng, T. (2007). Higher Order Vagueness in a Dynamic Landscape: Multi-Resolution Morphometric Analysis of a Coastal Dunefield. Journal of Environmental Informatics 9(1), 56-70 doi:10.3808/jei.200700087
  • Deng, M., Li, Z., Cheng, T. (2006). Rough-set representation of GIS data uncertainties with multiple granularities. Acta Geodaetica et Cartographica Sinica35(1), 64-70
  • Cheng, T., Fisher, P., Li, Z. (2004). Double vagueness: uncertainty in multi-scale fuzzy assignment of duneness. Geo-Spatial Information Science 7(1), 58-66 doi:10.1007/BF02826677.
  • Cheng, T. (2002). Fuzzy objects: their change and uncertainties. Photogrammetric Engineering and Remote Sensing 68(1), 41-49 Publisher URL
  • Cheng, T., Lin, H. (2001). Concept, model and application of fuzzy objects. Journal of Remote Sensing 5, 248-253
  • Cheng, T., Molenaar, M., Lin, H. (2001). Formalizing fuzzy objects from uncertain classification results. International Journal of Geographical Information Science15(1), 27-42 doi:10.1080/13658810010004689.
  • Molenaar, M., Cheng, T. (2000). Fuzzy spatial objects and their dynamics. ISPRS Journal of Photogrammetry and Remote Sensing 55(3), 164-175 doi:10.1016/S0924-2716(00)00017-4.
  • Cheng, T., Molenaar, M. (1999). Diachronic Analysis of Fuzzy Objects. GeoInformatica 3(4), 337-356 doi:10.1023/A:1009884730822.
  • Cheng, T., Molenaar, M. (1999). Objects with fuzzy spatial extent. Photogrammetric Engineering and Remote Sensing 65(7), 797-801
  • Cheng, T., Li, D., Shu, N. (1992). An expert system prototype for remote sensing image interpretation. Journal of Wuhan Technical University of Surveying and Mapping 17, 155-166
Overview
  • Cheng, T., Haworth, J., Manley, E. (2012). Advances in geocomputation (1996-2011). Computers, Environment and Urban Systemsdoi:10.1016/j.compenvurbsys.2012.10.002.
  • Samarasundera, E., CHENG, T., Walsh, T., Koenig, A., Jattansingh, K., Dawe, A., Soljak, M. (2012). Methods and tools for geographical mapping and analysis in primary health care. Primary Health Care Research and Development 13(1), 10-21 doi:10.1017/S1463423611000417Publisher URL
  • Deng, M., Liu, Y., CHENG, T., Chen, J. (2008). An approach for evaluation of semantic quality in map generalization. Geography and Geo-Information Science25(5), 20-51
  • Van Zuidam, R., Farifteh, J., Eleveld, M., Cheng, T. (1998). Developments in remote sensing, dynamic modelling and GIS applications for integrated coastal zone management. Journal of Coastal Conservation 4(2), 191-202 doi:10.1007/BF02806511.

Journal articles by application 

Transport
  • Cheng, T. & Wicks, T. (2014). Event Detection using Twitter: A Spatio-Temporal Approach. Plos One, 9(6), e97807
  • Manley, E., Cheng, T., Penn, A., & Emmonds, A. (2014). A framework for simulating large-scale complex urban traffic dynamics through hybrid agent-based modelling. Computers, Environment and Urban Systems44, 27-36. DOI
  • Cheng, T., Wang, J., Haworth, J., Heydecker, B., Chow, A. (2014). A Dynamic Spatial Weight Matrix and Localized Space–Time Autoregressive Integrated Moving Average for Network Modeling, Geographical Analysis. Geographical Analysis, 46: 75–97 DOI: 10.1111/gean.12026
  • Manley, E., Cheng, T., Penn, A., Emmonds, A. (2014). Combining Agent-based Modelling with Macroscopic Traffic Simulation for Large-Scale Behaviourally-Realistic Representations of Urban Traffic Flow. Computers, Environment and Urban Systems. 44. 27-36 http://www.sciencedirect.com/science/article/pii/S0198971513001129
  • 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 doi:10.1016/j.trc.2013.09.001Author URL Publisher URL
  • Chow, A. H. F., Santacreu, A., Tsapakis, I., Tanasaranond, G., Cheng, T. (2013). Empirical assessment of urban traffic congestion. Journal of Advanced Transportation doi:10.1002/atr.1241.
  • Dong, J. -. X., Cheng, T., Xu, J., Wu, J. (2013). Quantitative assessment of urban road network hierarchy planning. Town Planning Review 84(4), 445-472
  • 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 UK: doi:10.1016/j.compenvurbsys.2012.06.001. [Accepted]
  • CHENG, T., Haworth, J., Wang, J. (2012). Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems 14(4), 389-413 doi:10.1007/s10109-011-0149-5.
  • Haworth, J., Cheng, T. (2012). Non-parametric regression for space-time forecasting under missing data. Computers, Environment and Urban Systems36(6), 538-550 doi:10.1016/j.compenvurbsys.2012.08.005.
  • Tsapakis, I., CHENG, T. (2012). Impact of weather conditions on macroscopic urban travel times. Special issue of the Journal of Transportation Geography[Submitted]
  • Tsapakis, I., Turner, J., Cheng, T., Heydecker, B., Emmonds, A., Bolbol, A. (2012). Effects of Tube Strikes on Journey Times in the Transport Network of London. Transportation Research Record 2274: Journal of the Transportation Research Board (TRB) , 84-92 doi:10.3141/2274-09.
  • Tsapakis, I., Schneider, W. H., Nichols, A., Haworth, J. (2012) Alternatives in assigning short-term counts to seasonal adjustment factor groupings. Journal of Advanced Transportation doi:10.1002/atr.1186.
  • Cheng, T., Haworth, J., Wang, J. (2011). Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems doi:10.1007/s10109-011-0149-5.Publisher URL
  • Cheng, T., Tanaksaranond, G., Emmonds, A., Sonoiki, D. (2010). Multi-Scale Visualisation of Inbound and Outbound Traffic Delays in London. CARTOGR J47(4), 323-329 doi:10.1179/000870410X12911311788152.
  • Wang, D., Cheng, T. (2001). A spatio-temporal data model for activity-based transport demand modelling. International Journal of Geographical Information Science 15(6), 561-586 doi:10.1080/13658810110046934.
  • Blok, C., Kobben, B., Cheng, T., Kuterema, A. (1999). Visualization of Relationships Between Spatial Patterns in Time by Cartographic Animation. Cartography and Geographic Information Science 26(2), 139-151 doi:10.1559/152304099782330716.
Environment
  • Crooks, A. T., Wise, S. (2013). GIS and agent-based models for humanitarian assistance. Computers, Environment and Urban Systems 41, 100-111, C doi:10.1016/j.compenvurbsys.2013.05.003.
  • Skarlatidou, A., Cheng, T., Haklay, M. (2012). What Do Lay People Want to Know About the Disposal of Nuclear Waste? A Mental Model Approach to the Design and Development of an Online Risk Communication. Risk Analysis 32(9), 1496-1511 doi:10.1111/j.1539-6924.2011.01773.xPublisher URL
  • Wise, S., Crooks, A. T. (2012). Agent-based modeling for community resource management: Acequia-based agriculture. Computers, Environment and Urban Systems 36(6), 562-572 doi:10.1016/j.compenvurbsys.2012.08.004.
  • Cheng, T., Wang, J. Q., Li, X. (2011). A Hybrid Framework for Space-Time Modeling of Environmental Data. GEOGR ANAL 43(2), 188-210 doi:10.1111/j.1538-4632.2011.00813.x.
  • Deng, M., Liu, Q., Cheng, T., Shi, Y. (2011). An adaptive spatial clustering algorithm based on delaunay triangulation. Computers, Environment and Urban Systems 35(4), 320-332 UK: doi:10.1016/j.compenvurbsys.2011.02.003.Publisher URL
  • Cheng, T., Wang, J. Q. (2009). Accommodating spatial associations in DRNN for space-time analysis. COMPUT ENVIRON URBAN 33(6), 409-418 doi:10.1016/j.compenvurbsys.2009.08.004.
  • Cheng, T., Wang, J. (2008). Integrated spatio-temporal data mining for forest fire prediction. Transactions in GIS 12(5), 591-611 doi:10.1111/j.1467-9671.2008.01117.x.
  • Chen, J., Liu, W., Li, Z., Zhao, R., Cheng, T. (2007). Detection of spatial conflicts between rivers and contours in digital map updating. INT J GEOGR INF SCI21(10), 1093-1114 doi:10.1080/13658810701300071.
  • Cheng, T., Deng, M., Li, Z. (2007). Representation methods of spatial objects with uncertainty and their application in GIS. Geomatics and Information Science of Wuhan University 32(5), 389-393
  • Deng, M., Cheng, T., Chen, X., Li, Z. (2007). Multi-level topological relations between spatial regions based upon topological invariants. Geoinformatica 11(2), 239-267 doi:10.1007/s10707-006-0004-x.
  • Fisher, P., Cheng, T., Wood, J. (2007). Higher order vagueness in geographical information: Empirical geographical population of type n fuzzy sets. Geoinformatica 11(3), 311-330 doi:10.1007/s10707-006-0009-5.
  • Fisher, P., Wood, J., Cheng, T. (2007). Higher Order Vagueness in a Dynamic Landscape: Multi-Resolution Morphometric Analysis of a Coastal Dunefield. Journal of Environmental Informatics 9(1), 56-70 doi:10.3808/jei.200700087
  • Wang, J., Cheng, T. (2007). Using spatio-temporal data mining and knowledge discovery for forest fire prevention. Acta Scientiarum Naturalium Universitatis Sun Yat-sen I 46(2), 110-116
  • Zuo, X., Li, Q., Yang, B., Cheng, T. (2007). A view-dependent method for the multi-resolution representation of terrains with roads embedded. International Journal of Remote Sensing 28(2), 319-334 doi:10.1080/01431160600702657.
  • Chen, J., Liu, W., Li, Z., Cheng T, Z. R. (2006). The refined calculation method of topological relationships between line objects. Acta Geodaetica et Cartographica Sinica 35(3), 255-260
  • Cheng, T., Li, Z. (2006). A Multiscale Approach for Spatio-Temporal Outlier Detection. Transactions in GIS 10(2), 253-263 doi:10.1111/j.1467-9671.2006.00256.x.
  • Cheng, T., Li, Z. (2006). Effect of generalization on area features: A comparative study of two strategies. Cartographic Journal 43(2), 157-170 doi:10.1179/000870406X114649.
  • Cheng, T., Li, Z. (2006). Toward Quantitative Measures for the Semantic Quality of Polygon Generalization. Cartographica 41(2), 135-148 doi:10.3138/0172-6733-227U-8155.
  • Deng, M., Li, Z., Cheng, T. (2006). Rough-set representation of GIS data uncertainties with multiple granularities. Acta Geodaetica et Cartographica Sinica35(1), 64-70
  • Cheng, T., Fisher, P., Li, Z. (2004). Double vagueness: uncertainty in multi-scale fuzzy assignment of duneness. Geo-Spatial Information Science 7(1), 58-66 doi:10.1007/BF02826677.
  • Fisher, P., Wood, J., Cheng, T. (2004). Where is Helvellyn? Multiscale morphometry and the mountains of the English Lake District. Transactions of the Institute of British Geographers 29(1), 106-128 doi:10.1111/j.0020-2754.2004.00117.x.
  • Cheng, T. (2002). Fuzzy objects: their change and uncertainties. Photogrammetric Engineering and Remote Sensing 68(1), 41-49 Publisher URL
  • Cheng, T., Lin, H. (2001). Concept, model and application of fuzzy objects. Journal of Remote Sensing 5, 248-253
  • Cheng, T., Molenaar, M., Lin, H. (2001). Formalizing fuzzy objects from uncertain classification results. International Journal of Geographical Information Science15(1), 27-42 doi:10.1080/13658810010004689.
  • Molenaar, M., Cheng, T. (2000). Fuzzy spatial objects and their dynamics. ISPRS Journal of Photogrammetry and Remote Sensing 55(3), 164-175 doi:10.1016/S0924-2716(00)00017-4.
  • Cheng, T., Molenaar, M. (1999). Diachronic Analysis of Fuzzy Objects. GeoInformatica 3(4), 337-356 doi:10.1023/A:1009884730822.
  • Cheng, T., Molenaar, M. (1999). Objects with fuzzy spatial extent. Photogrammetric Engineering and Remote Sensing 65(7), 797-801
  • Van Zuidam, R., Farifteh, J., Eleveld, M., Cheng, T. (1998). Developments in remote sensing, dynamic modelling and GIS applications for integrated coastal zone management. Journal of Coastal Conservation 4(2), 191-202 doi:10.1007/BF02806511.
  • Cheng, T., Li, D., Shu, N. (1992). An expert system prototype for remote sensing image interpretation. Journal of Wuhan Technical University of Surveying and Mapping 17, 155-166
Health
  • Samarasundera, E., CHENG, T., Walsh, T., Koenig, A., Jattansingh, K., Dawe, A., Soljak, M. (2012). Methods and tools for geographical mapping and analysis in primary health care. Primary Health Care Research and Development 13(1), 10-21 doi:10.1017/S1463423611000417Publisher URL
  • Koenig, A., Samarasundera, E., Cheng, T. (2011). Interactive map communication: Pilot study of the visual perceptions and preferences of public health practitioners. Public Health 125(8), 554-560 doi:10.1016/j.puhe.2011.02.011.
Crime and policing
Social Media
  • Cheng, T., & Wicks, T. (2014). Event Detection using Twitter: A Spatio-Temporal Approach. PloS one9(6), e97807.

 

Conference papers
  • Haworth, J., Cheng, T., Manley, E. Improving forecasting under missing data on sparse spatial networks. Author URL Publisher URL
  • Haworth, J., Cheng, T., Manley, E. Improving forecasting under missing data on sparse spatial networks. International Conference of GeoComputation2013Author URLPublisher URL
  • Manley, E., Cheng, T., Haworth, J. Markov Chain Topological Route Selection.International Conference of GeoComputation2013Author URLPublisher URL
  • Skarlatidou, A., Haklay, M., Cheng, T., Bolbol, A. Investigating non-experts trust perceptions in Spatial Decision Support Systems for public use. Association of American Geographers Annual Meeting
  • Cheng, T., Adepejue, M. (2013). Detecting Emerging Space-Time Crime Patterns by Prospective STSS. International Conference of GeoComputationAuthor URLPublisher URL
  • Bolbol, A., CHENG, T., Tsapakis, I. (2012). Inferring the travel mode from sparse GPS data using SVM classification: Case study of Greater London. Conference of Transportation Research Arena. [Submitted]
  • Bolbol, A., Tsapakis, I., Cheng, T., Chow, A. H. F. (2012). Sample Size Calculation for Studying Transportation Modes from GPS Data. Transport Research Arena. [Submitted]
  • Chow, A. H. F., Santecreu, A., Tsapakis, I., Tanaksaranond, G., CHENG, T. (2012). Nature and causes of urban traffic congestion - a case study of London. Transportation Research Board 91st Annual Meeting. [Accepted]
  • Tsapakis, I., Turner, J. T., CHENG, T., Heydecker, B., Emmonds, A. E., Bolbol, A. B. (2012). Effects of tube strikes on journey times in transport network of London. Transportation Research Board 91st Annual Meeting. [Submitted]
  • Tsapakis, I., Turner, J., Heydecker, B., CHENG, T. (2012). Understanding the effects of tube strikes on macroscopic and link travel times. Conference of Transportation Arena. [Submitted]
  • Anbaroglu, B., CHENG, T. (2011). Where and when does the traffic congestion begin and end? A spatio-temporal clustering approach to detect congestion. Proceedings of the international symposium on spatial-temporal analysis and data mining. ( pp.11-13). London, UK: University College London.
  • Bolbol, A., CHENG, T., Haworth, J. (2011). Using a moving window SVM classification to infer travel mode from GPS data. Proceedings of the 11th International Conference on GeoComputation. ( pp.262-270).
  • CHENG, T., Haworth, J., Shawe-Taylor, J. (2011). Kernel regression for traffic prediction under missing data. Proceedings of the 11th International Conference on GeoComputation. ( pp.280-284). London, UK: University College London.
  • CHENG, T., Skarlatidou, A., Wardlaw, J., Haklay, M. (2011). Understanding the influence of specific Web GIS attributes in the formation of non-experts’ trust perceptions. Proceedings of the 25th International Cartographic Conference.
  • CHENG, T., Wang, J., Haworth, J., Heydecker, B., Chow, A. (2011). Modelling dynamic space-time autocorrelations of urban transport network. Proceedings of the 11th international conference on GeoComputation. ( pp.215-220). London, UK: University College London.
  • Manley, E., CHENG, T. (2011). Multi-agent simulation of drivers reactions to unexpected incidents on urban road networks. Proceedings of the 19th annual conference of GIS Research UK (GISRUK 2011).. ( pp.280-285).
  • Manley, E., CHENG, T., Emmonds, A. (2011). Understanding route choice by using agent-based simulation. Proceedings of the 11th international conference on GeoComputation. ( pp.54-58). London, UK: University College London.
  • Tanaksaranond, G., CHENG, T., Chow, A. (2011). A star-schema OLAP to support multi-dimensional traffic analysis. Proceedings of the international symposium on spatial-temporal analysis and data mining. ( pp.50-52). London, UK: University College London. [Accepted]
  • Wang, J., CHENG, T., Heydecker, B., Chow, A. (2011). Parameter calibration of STARIMA model. Proceedings of the international symposium on spatial-temporal analysis and data mining. ( pp.1-4). London, UK: University College London.
  • Bolbol, A., CHENG, T. (2010). GPS data collection setting for pedestrian activity modelling. Proceedings of the 18th Annual Conference of GIS Research UK. ( pp.337-344).
  • Bolbol, A., CHENG, T. (2010). On-line travel diary: towards automatic travel behaviour detection. ISPRS Proceedings.
  • Cao, Z., Haklay, M., Cheng, T. (2010). IDW implementation in Map calculus and commercial GIS products. Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. ( pp.141-144).
  • CHENG, T., Anbaroglu, B. (2010). Spatio-Temporal Clustering of Road Network Data. AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I. ( pp.116-123).
  • CHENG, T., Anbaroglu, T. (2010). Defining spatio-temporal neighbourhood of network data. Proceedings of the Joint International Conference on Theory, Data Handling and Modelling in GeoSpatial Information Science. ( pp.75-79). ISPRS.
  • CHENG, T., Manley, E. (2010). Understanding road congestion as an emergent property of traffic networks. Proceedings of The International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC 2010). ( pp.109-114).
  • CHENG, T., Tanaksaranond, G., Emmonds, A., Sonoiki, D. (2010). Multi-scale visualisation of inbound and outbound traffic delays in London. Proceedings of the 18th Annual Conference of GIS Research UK. ( pp.319-324).
  • Skarlatidou, A., Cheng, T., Haklay, M. (2010). Preliminary investigation of Web GIS trust: the example of the “WIYBY” website. Proceedings of the Joint International Conference on Theory, Data Handling and Modelling in GeoSpatial Information Science. ( pp.411-416). ISPRS.
  • Skarlatidou, A., Haklay, M., Cheng, T., Francis, N. (2010). Trust in web GIS: A preliminary investigation of the environment agency’s WIYBY website with non-expert users. Proceedings of the GIS Research UK 18th Annual Conference. ( pp.439-446). London: UCL.
  • Wang, J., CHENG, T., Haworth, J. (2010). Space-time kernels. Proceedings of the Joint International Conference on Theory, Data Handling and Modelling in GeoSpatial Information Science. ( pp.57-62). ISPRS.
  • Wang, J., CHENG, T., Heydecker, B., Haworth, J. (2010). STARIMA for journey time prediction in London. Proceedings of the 5th IMA (Institute of Mathematics and its Applications) Conference on Mathematics in Transport.
  • CHENG, T., Anbaroglu, B. (2009). Methods on defining spatio-temporal neighbourhoods. Proceedings of the 10th international conference on GeoComputation.
  • CHENG, T., Anbaroglu, B. (2009). Spatio-temporal outlier detection in environmental data. Spatial and temporal reasoning for ambient intelligence systems: COSIT 2009 workshop proceedings. ( pp.1-8).
  • Cheng, T., Wang, J., Li X, Z., W, (2008). A hybrid approach to model nonstationary space-time series. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. ( pp.195-202).
  • Cheng, T., Wang, J., Li, X. (2008). Space-time series forecasting by artificial neural networks. International Conference on Earth Observation Data Processing and Analysis(ICEODPA). Wuhan:
  • Cheng, T., Deng, M., Li, Z. (2007). A unified representation for indeterminate spatial objects. 7th UK Workshop on Computational Intelligence. London, UK:
  • Cheng, T., Wang, J. (2007). Application of a Dynamic Recurrent Neural Network in Spatio-Temporal Forecasting. Information Fusion and Geographic Information Systems. ( Vol. XIV pp.173-186). St Petersburg, Russia: Berlin: Springer.
  • Cheng, T., Wang, J., Li, X. (2007). Spatio-temporal data mining and forecasting by support vector machines. Proceedings of the 9th international conference on GeoComputation. ( Vol. Paper 4C3 ). Maynooth, Ireland:
  • Wang, J., Cheng, T. (2007). Nonlinear integration of spatial and temporal forecasting by support vector machines. 3rd International Conference on Natural Computation and 4th International Conference on Fuzzy Systems and Knowledge Discovery. ( pp.61-66). Haikou, China:
  • Cheng, T., Li, Z. L., Li, D. A. L., D, (2006). An integrated cloud model for measurement errors and fuzziness. Progress in Spatial Data Handling 12th International Symposium on Spatial Data Handling (SDH 2006).
  • Cheng, T., Wang, J. (2006). Applications of spatio-temporal data mining and knowledge for forest fire. Remote Sensing: From Pixels to Processes. ( pp.148-153). Enschede, Netherlands:
  • Cheng, T., Li, Z. (2005). Representing cyclic change in polar coordinate systems.22nd International Cartographic Conference. ( Vol. CD-Rom ). La Coruña, Spain:
  • Cheng, T., Li, Z., Deng, M., Xu, Z. (2005). Representing indeterminate spatial objects by cloud theory. 4th International Symposium on Spatial Data Quality. ( pp.70-77). Beijing:
  • Cheng, T., Li, Z., Gong, J. (2005). Conceptual design of an activity-based spatio-temporal data model for SARS transmission analysis. Proceedings of International Symposium on Spatio-temporal Modeling, Spatial Reasoning, Analysis, Data Mining and Data Fusion. ( pp.31-37). Beijing:
  • Fisher, P., Wood, J., Cheng, T. (2005). The uncertainty of uncertainty: higher order vagueness in geographical information. International Symposium on Spatial Data Quality. Beijing:
  • Liu, W., Chen, J., Li, Z., Zhao, R., Cheng, T. (2005). Detection of spatial conflicts between rivers and contours in topographical database updating. 4th ISPRS Workshop on Dynamic and Multi-dimensional GIS. ( pp.99-105). Pontypridd, Wales, UK:
  • Liu, W., Chen, J., Zhao, R., Cheng, T. (2005). A Refined Line-Line Spatial Relationship Model for Spatial Conflict Detection. Lecture Notes in Computer Science. ( Vol. 3770 pp.239-248). Springer-Verlag.
  • Cheng, T., Fisher, P., Li, Z. (2004). Double vagueness: effect of scale on the modelling of fuzzy spatial objects. Developments in Spatial Data Handling. ( pp.299-313).
  • Cheng, T., Li, Z. (2004). A hybrid approach to detect spatio-temporal outliers. Proceedings of the 12th International Conference on Geoinformatics Geospatial Information Research: Bridging the Pacific and Atlantic. ( pp.173-178). Gävle, Sweden:
  • Cheng, T., Li, Z. (2004). A multi-scale approach to detect spatio-temporal outliers. International Archives of Photogrammetry and Remote Sensing. ( pp.1008-1012). Istanbul, Turkey:
  • Cheng, T., Li, Z. (2003). Measure the semantic change after merging operation. International Workshop on Semantic Processing of Spatial Data. ( pp.92-101). Mexico City:
  • Cheng, T. (2001). Quality assessment of model-oriented generalization. 4th Workshop on Progress in Automated Map Generalisation,ICAAuthor URL
  • Cheng, T., Wang, D. (2001). Visualizing cyclic spatio-temporal patterns in polar coordinate system. 20th International Cartographic Conference. ( pp.2460-2467). Beijing:
  • Shan, Y., Cheng, T., and Lin, H. (2001). Spatio-temporal data mining in GIS. International Workshop on GEOINFORMATICS & DMGIS 2001. ( pp.254-257). Bangkok:
  • Cheng, T., Lin, H. (2000). Uncertainties in multi-scale generalization. Proceedings of the 9th International Symposium on Spatial Data Handling (SDH2000). ( Vol. 5 pp.18-31). Beijing:
  • Wang, D., Cheng, T. (2000). A process-oriented spatio-temporal data model for activity-based travel demand modeling. 29th International Geographic Congress. ( pp.597-598). Seoul, Korea:
  • Cheng, T., Molenaar, M. (1999). Syntactic representation of three fuzzy object models. the International Symposium on Spatial Data Quality (ISSDQ’99). ( pp.506-516). Hong Kong:
  • CHENG, T. (1998). A process-oriented spatio-temporal data model to support physical environmental modeling. the first conference of the Association of Geographic Information Laboratories in Europe (AGILE).
  • Cheng, T. (1998). Change of fuzzy objects. the first conference of Association of Geographic Information Laboratories in Europe (AGILE). Enschede, The Netherlands:
  • Cheng, T., Molenaar, M. (1998). The identification and monitoring of objects with fuzzy spatial extent. International Archives of Photogrammetry and Remote Sensing. ( Vol. 32 pp.207-212).
  • Molenaar, M., Cheng, T. (1998). Fuzzy spatial objects and their dynamics. International Archives of Photogrammetry and Remote Sensing (ISPRS). ( Vol. 32 pp.389-394).
  • Cheng, T., Molenaar, (1997). Dynamics of fuzzy objects. the International Workshop on Dynamic and Multi-dimensional GIS, Aug.. ( pp.49-63). Hong Kong:
  • Cheng, T., Molenaar, M., Bouloucos, T. (1997). Identification of Fuzzy Objects from Field Obseravtion Data. Lecture Notes in Computer Science. ( Vol. Spatial Informat pp.241-259). Berlin: Springer-Verlag.
  • Cheng, T., Kainz, W., Van Zuidam, R. A. (1996). Coupling GIS and environmental modelling: the implications for spatio-temporal data modeling. International Archives of Photogrammetry and Remote Sensing. ( pp.849-856).
  • Cheng, T. (1995). Development of a spatio-temporal GIS shell to support coastal environment modelling. Conference of Spatial Information Theory Doctoral Consortium. ( pp.7-14).
  • Cheng, T., Eleveld, M. A., Van Zuidam, R. A. (1995). Creation of a 4-D GIS working platform for coastal environmental monitoring and management. Third Thematic Coference of Remote Sensing for Marine and Coastal Environments, Technology and Applications. ( Vol. 2 pp.260-272).
  • Cheng, T., Van Zuidam, R. A., Kaniz, W. (1995). A unified spatio-temporal data model for 4-D GIS. GIS/LIS 95 Annual Conference and Exposition. ( pp.967-976). Nashville, USA:
  • Eleveld, M. A., Cheng, T., Van Zuidam, R. A. (1995). Towards a decision support system for coastal zone management by applying morphodynamic modeling with remote sensing data input in a 4-D GIS environment. Third Thematic Conference of Remote Sensing for Marine and Coastal Environments, Technology and Applications. ( Vol. 1 pp.256-265). Seattle, USA:
  • Li, D., Cheng, T. (1994). KDG - Knowledge Discovery from GIS: propositions on the use of KDD in an Intelligent GIS. The Canadian Conference on GIS. ( Vol. 2 pp.1001-1012).
  • Cheng, T., Li, D., Shu, N. (1992). An expert system of grass resource investigation by using remote sensing data. International Cooloquium on Photogrammetry, Remote Sensing and Geographic Information Systems. Wuhan, China:
Book chapters
  • Wang, J., CHENG, T., Haworth, J. (2012). Space-time kernels. In Shi, W., Goodchild, M., Lees, B. (Eds.). Advances in geospatial information science ( ). Leiden, NL: CRC Press.
  • Skarlatidou, A., Wardlaw, J., Haklay, M., CHENG, T. (2011). Understanding the influence of specific Web GIS attributes in the formation of non-experts' trust perceptions. In Ruas, A. (Ed.). Advances in cartography and GIScience ( pp.219-238). Berlin: Springer-Verlag.
  • CHENG, T., Anbaroglu, B. (2010). Spatio-temporal clustering of road network data. In Deng, H., Wang, L., Wang, F. L., Lei, J. (Eds.). Artificial Intelligence and Computational Intelligence ( pp.116-123). New York: Springer-Verlag New York Inc. Publisher URL
  • Cheng, T., Li, Z., Gong, J. (2008). An activity-based spatio-temporal data model for epidemic transmission analysis. In Tang, X., Liu, Y., Zhang, J., Kainz, W. (Eds.). Advances in Spatio-Temporal Analysis ( pp.135-146). Taylor & Francis.
  • Cheng, T., Molenaar, M., Stein, A. (2008). Fuzzy approach for integrated coastal zone management. In Yang, X. (Ed.). Remote Sensing and GIS for Coastal Ecosystem Assessment and Management: Principles and Application (1 ed. pp.67-90). Berlin, Germany: Springer-Verlag.
  • CHENG, T., Wang, J. (2007). Application of a dynamic recurrent neural network in spatio-temporal forecasting. In Popovich, V. V., Schrenk, M., Korolenko, K. V. (Eds.). Information fusion and geographic information systems ( pp.173-186). Berlin: Springer Verlag.
  • CHENG, T., Li, Z., Li, D., Li, D. (2006). An integrated cloud model for measurement errors and fuzziness. In Riedl, A., Kainz, W., Elmes, G. A. (Eds.). Progress in Spatial Data Handling ( pp.699-718). Springer Verlag.
  • Cheng, T. (2005). Modelling and visualising linear and cyclic Changes. In Fisher, P., Unwin, D. (Eds.). Re-Presenting GIS ( pp.205-213). John Wiley & Son.
  • CHENG, T., Fisher, P., Li, Z. (2005). Double vagueness: effect of scale on the modelling of fuzzy spatial objects. In Fisher, P. F. (Ed.). Developments in spatial data handling ( pp.299-313). Springer-Verlag New York Inc.
  • Fisher, P., Wood, J., Cheng, T. (2005). Fuzziness and ambiguity in multi-scale analysis of landscape morphometry. In Cobb, M., Petry, F., Robinson, V. (Eds.). Fuzzy Modeling with Spatial Information for Geographic Problems ( pp.209-232). New York: Springer.
  • Raper, J., Miller, H., Guhathakurta, S., Muetzelfeldt, R., Cheng, T. (2005). Time as well: an introduction. In Fisher, P., Unwin, D. (Eds.). Re-Presenting GIS ( pp.195-198). John Wiley & Sons.
  • CHENG, T., Molenaar, M., Bouloucos, T. (1997). Identification of fuzzy objects from field observation data. In Hirtle, S. C., Frank, A. U. (Eds.). Spatial information theory ( pp.241-259). Springer Verlag. 
Books
  • Cheng, T. et al. (2016) CPC: Crime, Policing and Citizenship – Intelligent policing and big data. SpaceTimeLab, London.
  • Cheng, T., Longley, P., Ellul, C., Chow, A. H. F. (Eds.) (2011). Proceedings of the 11th International Conference of GeoComputation, July 20-22. London, UK: University College London.
  • Cheng, T., Longley, P., Ellul, C., Chow, A. H. F. (Eds.) (2011). Proceedings of the International Symposium of Spatial-temporal Analysis and Data Mining (STDM 2011), July 18-19. London, UK: University College London.