Crooks, A, N. Malleson, E. Manley, A. Heppenstall (2019) Agent-Based Modelling and Geographical Information Systems. Sage.

Peer-Reviewed Journal Articles


Malleson, N., R. Franklin, D. Arribas-Bel, T. Cheng, and M. Birkin (2024). Digital Twins on Trial: Can They Actually Solve Wicked Societal Problems and Change the World for Better? Environment and Planning B: Urban Analytics and City Science (online first). DOI: 10.1177/23998083241262893

Kieu, M., R. Ozaki, P. Ternes, N. Malleson (2024). Evaluating Public Sentiment Towards Transport Policies: A causal analysis of the motorbike ban in Hanoi. Case Studies on Transport Policy. DOI: 10.1016/j.cstp.2024.101203

Y. Oswald, N. Malleson and K. Suchak (2024). An Agent-Based Model of the 2020 International Policy Diffusion in Response to the COVID-19 Pandemic with Particle Filter. Journal of Artificial Societies and Social Simulation 27(2) 3. DOI: 10.18564/jasss.5342

Seyidoglu, H., G. Farrell, A. Dixon, J. Pina-Sánchez, N Malleson (2024). Post-pandemic crime trends in England and Wales. Crime Science 13, 6. DOI: 10.1186/s40163-024-00201-1

Kieu, Minh, Alexis Comber, Hang Nguyen Thi Thuy, Thanh Bui Quang, Phe Hoang Huu, and N. Malleson (2024). An Open Dataset on Individual Perceptions of Transport Policies. Nature Scientific Data 11 (1): 104. DOI: 10.1038/s41597-024-02950-9

Kieu, M., H. Nguyen, J. A. Ward, and N. Malleson (2024). Towards Real-Time Predictions Using Emulators of Agent-Based Models. Journal of Simulation 18 (1): 29–46. DOI: 10.1080/17477778.2022.2080008


Molly Asher, Nik Lomax, Karyn Morrissey, Fiona Spooner, Nick Malleson (2023). Dynamic calibration with approximate Bayesian computation for a microsimulation of disease spread. Nature Scientific Reports 13:8637. DOI: 10.1038/s41598-023-35580-z.

Kieu, M., E. Wanjau, A. Comber, K. Bratkova, H. N. T. Thuy, T. B. Quang, P. H. Huu, N. Malleson (2023). Factors affecting perceptions in transport – A deep dive into the motorbike ban in Hanoi, Vietnam. Case Studies on Transport Policy 11: 100958. DOI: 10.1016/j.cstp.2023.100958


Jumadi, Jumadi, V.N. Fikriyah, H.Z. Hadibasyir, M.I.T. Sunariya, K.D. Priyono, N.A. Setiyadi, S.J. Carver, P.D. Norman, N. Malleson, A. Rohman, A. Lotfata (2022). Spatiotemporal Accessibility of COVID-19 Healthcare Facilities in Jakarta, Indonesia. Sustainability 14 (21): 14478. DOI: 10.3390/su142114478.

N. Malleson, Birkin, M., Birks, D., Ge, J., Heppenstall, A., Manley, E., McCulloch, J., Ternes, P. (2022) Agent-based modelling for Urban Analytics: State of the art and challenges. AI Communications 35, 393–406. DOI: 10.3233/AIC-220114. [PDF (open access)]

Comber, A., Callaghan, M., Harris, P., Lu, B., Malleson, N., Brunsdon, C. (2022) gwverse: A Template for a New Generic Geographically Weighted R Package. Geographical Analysis (online first). DOI: 10.1111/gean.12337

Tang, D. and N. Malleson (2022). Data assimilation with agent-based models using Markov chain sampling. Open Research Europe 2(70). DOI: 10.12688/openreseurope.14800.1 (open access)

Kieu, M., H. Nguyen, J.A. Ward and N. Malleson (2022). Towards real-time predictions using emulators of agent-based models. Journal of Simulation 1–18. DOI: 10.1080/17477778.2022.2080008. [PDF].

McCulloch, J., J. Ge, J.A. Ward, A. Heppenstall, J.G. Polhill, N. Malleson (2022) Calibrating agent-based models using uncertainty quantification methods. Journal of Artificial Societies and Social Simulation 25, 1. DOI: 10.18564/jasss.4791 (open access)


Spooner, F., J.F. Abrams, K. Morrissey, G. Shaddick M. Batty, R. Milton, A. Dennett, N. Lomax, N. Malleson, N. Nelissen, A. Coleman, J. Nur, Y. Jin, R. Greig, C. Shenton, M. Birkin (2021) A dynamic microsimulation model for epidemics. Social Science & Medicine 291, 114461. DOI: 10.1016/j.socscimed.2021.114461 (open access)

Ternes, P., J.A. Ward, A. Heppenstall, V. Kumar, L.-M. Kieu, N. Malleson (2021) Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters. Open Research Europe 1, 131. DOI:10.12688/openreseurope.14144.1 (open access)

An, Li, Volker Grimm, Abigail Sullivan, B.L. Turner II, N Malleson, Alison Heppenstall, Christian Vincenot, Derek Robinson, Xinyue Ye, Jianguo Liu, Emilie Lindkvist, Wenwu Tang (2021) Challenges, tasks, and opportunities in modeling agent-based complex systems, Ecological Modelling 457(1):109685. DOI: 10.1016/j.ecolmodel.2021.109685

Clay, Robert, J. A. Ward, P. Ternes, Le-Minh Kieu, N. Malleson (2021) Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter. Simulation Modelling Practice and Theory 113 (102386) DOI: 10.1016/j.simpat.2021.102386. [PDF].

Rosés, R., C. Kadar, N. Malleson (2021) A data-driven agent-based simulation to predict crime patterns in an urban environment. Computers, Environment and Urban Systems 89: 101660 DOI:10.1016/j.compenvurbsys.2021.101660. [PDF].

Cui, N., N. Malleson, V. Houlden, and A. Comber (2021) Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review. ISPRS International Journal of Geo-Information 10(7): 425. DOI: 10.3390/ijgi10070425 (open access)

Whipp, A., N. Malleson, J. Ward, A. Heppenstall (2021). Estimates of the Ambient Population: Assessing the Utility of Conventional and Novel Data Sources. International Journal of Geo-Information 10(3), 131 (open access). DOI: 10.3390/ijgi10030131 (open access)

Roxburgh, N., L. C. Stringer, A. Evans, R. K. Gc, N. Malleson, and A. J. Heppenstall (2021). Impacts of Multiple Stressors on Mountain Communities: Insights from an Agent-Based Model of a Nepalese Village. Global Environmental Change 66:102203. DOI: 10.1016/j.gloenvcha.2020.102203 (open access)

Roxburgh, N., A. Evans, R. K. Gc, N. Malleson, A. Heppenstall, and L. Stringer (2021) An Empirically Informed Agent-Based Model of a Nepalese Smallholder Village. MethodsX 101276. [DOI: 10.1016/j.mex.2021.101276].


Heppenstall, A., A. Crooks, N. Malleson, E. Manley, J. Ge, and M. Batty (2020). Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities. Geographical Analysis 53: 76-91. DOI: 10.1111/gean.12267 (open access)

Halford, E., A. Dixon, G. Farrell, N. Malleson and N. Tilley (2020) Crime and coronavirus: social distancing, lockdown, and the mobility elasticity of crime. Crime Science 9 (11). DOI: 10.1186/s40163-020-00121-w (open access)

Alotaibi, M., G. Clarke, and N. Malleson (2020). Optimal Service Planning in a Temporary City. Journal of Service Science and Management 13(05): 709–28. DOI: 10.4236/jssm.2020.135045 (open access)

Johnson, P., M. A. Andresen, and N. Malleson (2020). Cell Towers and the Ambient Population: A Spatial Analysis of Disaggregated Property Crime’. European Journal on Criminal Policy and Research (in press). DOI: 10.1007/s10610-020-09446-3. [PDF]

Malleson, N., K. Minors, Le-Minh Kieu , J. A. Ward , A. West and A. Heppenstall (2020) Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter. Journal of Artificial Societies and Social Simulation (JASSS) 23 (3). DOI: 10.18564/jasss.4266 (open access)

Jumadi, J, N. Malleson, S. Carver, and D. Quincey (2020). Estimating Spatio-Temporal Risks from Volcanic Eruptions Using an Agent-Based Model’. Journal of Artificial Societies and Social Simulation 23 (2): 2. DOI: 10.18564/jasss.4241 (open access)

Martin A. Andresen, N. Malleson, W. Steenbeek, M. Townsley and C. Vandeviver (2020). Minimum geocoding match rates: an international study of the impact of data and areal unit sizes. International Journal of Geographical Information Science 34(7) first) 10.1080/13658816.2020.1725015 (pdf)

Kieu, Le-Minh, N. Malleson, and A. Heppenstall (2019). Dealing with Uncertainty in Agent-Based Models for Short-Term Predictions’. Royal Society Open Science 7(1): 191074. DOI: 10.1098/rsos.191074 (open access)


Kieu, Le-Minh, D. Ngoduy, N Malleson, and E. Chung (2019). A Stochastic Schedule-Following Simulation Model of Bus Routes. Transportmetrica B: Transport Dynamics 7 (1): 1588–1610. DOI: 10.1080/21680566.2019.1670118. [PDF].

Malleson N, Steenbeek W, Andresen MA (2019) Identifying the appropriate spatial resolution for the analysis of crime patterns. PLoS ONE 14(6): e0218324. DOI: 10.1371/journal.pone.0218324 (open access).

Crols, T., and N. Malleson (2019) Quantifying the Ambient Population Using Hourly Population Footfall Data and an Agent-Based Model of Daily Mobility. GeoInformatica (online first). DOI: 10.1007/s10707-019-00346-1. [Open access].

Addis, N., A. Evans, and N. Malleson (2019) Exploring the Practices of Steal-to-Order Burglars: A Different Brand of Offender? Security Journal (online first). DOI: 10.1057/s41284-019-00174-w. [PDF]

Yu, Rui, A.J. Evans and N. Malleson (2019) An agent-based model for assessing grazing strategies and institutional arrangements in Zeku, China. Agricultural Systems 171: 135–142. DOI:10.1016/j.agsy.2019.02.004. [PDF].

Malleson, N (2019) Building Temporal Dynamism into Applied GIS Research. Applied Spatial Analysis and Policy (Online first). (Introduction to the 2015 GIS Research UK Special Issue). DOI: 10.1007/s12061-019-09291-w. [PDF].


Gulma, U.L., A. Evans, A. Heppenstall, N. Malleson (2018) Diversity and burglary: Do community differences matter? Transactions in GIS 23 (2): 181-202. DOI: 10.1111/tgis.12511 [PDF]

Jumadi, A. Heppenstall, N. Malleson, S. Carver, D. Quincey and V. Manville (2018). Modelling Individual Evacuation Decisions during Natural Disasters: A Case Study of Volcanic Crisis in Merapi, Indonesia. Geosciences 8(196). DOI: 10.3390/geosciences8060196.

Malleson, N., Vanky, A., Hashemian, B., Santi, P., Verma, S.K., Courtney, T.K., Ratti, C.. (2018). The characteristics of asymmetric pedestrian behavior: A preliminary study using passive smartphone location data. Transactions in GIS 22(2): 616-634. 10.1111/tgis.12336

Yu, Rui, A.J. Evans, and N. Malleson (2018). Quantifying Grazing Patterns Using a New Growth Function Based on MODIS Leaf Area Index. Remote Sensing of Environment 209: 181–94. DOI:10.1016/j.rse.2018.02.034.


Harris, Richard, David O’Sullivan, Mark Gahegan, Martin Charlton, Lex Comber, Paul Longley, Chris Brunsdon, Nick Malleson, Alison Heppenstall, Alex Singleton, Daniel Arribas-Bel, Andy Evans (2017). More bark than bytes? Reflections on 21+ years of geocomputation. Environment and Planning B: Urban Analytics and City Science 44(4) 598–617. DOI: 10.1177/2399808317710132.

Andresen, M.A., Linning, S.J., and Malleson, N. (2017). Crime at places and spatial concentrations: exploring the spatial stability of property crime in Vancouver BC, 2003-2013. Journal of Quantitative Criminology, 33(2), 255 – 275. DOI: 10.1007/s10940-016-9295-8

Alotaibi, Nawaf Ibrahim, Andrew J. Evans, Alison J. Heppenstall, and N. Malleson (2017) How Well Does Western Environmental Theory Explain Crime in the Arabian Context? The Case Study of Riyadh, Saudi Arabia. International Criminal Justice Review. 29(1) 5-32. DOI: 10.1177/1057567717709497.


Ward, J., A. Evans, N. Malleson (2016) Dynamic calibration of agent-based models using data assimilation. Royal Society Open Science. 3:150703. (Open access)

Malleson, N., and Andresen, M.A. (2016) Exploring the impact of ambient population measures on London crime hotspots. Journal of Criminal Justice 46 pp 52-63. (Open access)

Heppenstall, A., N. Malleson and A. Crooks (2016) ‘Space, the Final Frontier’: How Good are Agent-Based Models at Simulating Individuals and Space in Cities? Systems 4(1) 9


Malleson, N. and M.A. Andresen (2015a) Spatio-temporal crime hotspots and the ambient population. Crime Science 4(10). (Open access)

Andresen, M.A., and N. Malleson (2015). Intra-week spatial-temporal patterns of crime. Crime Science. 4(12). (Open access) [URL (white rose repository]

Malleson, N. and M. Andresen (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 [PDF]


Hirschfield, A., M. Birkin, C. Brunsdon, N. Malleson and A. Newton (2014). How Places Influence Crime: The Impact of Surrounding Areas on Neighbourhood Burglary Rates in a British City. Urban Studies 1(5) 1057-1072 DOI: 10.1177/0042098013492232

Andresen, M.A. and N. Malleson (2014). Police Foot Patrol and Crime Displacement: A Local Analysis. Journal of Contemporary Criminal Justice 30(2) 186–199. doi:10.1177/1043986214525076. [URL]

Jenkins, K., J. Hall, V. Glenis, C. Kilsby, M. McCarthy, C. Goodess, D. Smith, N. Malleson, M. Birkin (2014) Probabilistic spatial risk assessment of heat impacts and adaptations for London. Climatic Change 124: 105-117. doi:10.1007/s10584-014-1105-4 [URL]

Birkin, M., Harland, K., Malleson, N., Cross, P., Clarke, M. (2014) An Examination of Personal Mobility Patterns in Space and Time Using Twitter. International Journal of Agricultural and Environmental Information Systems 5, 55–72. doi:10.4018/ijaeis.2014070104 [pdf]


Birkin, M., K. Harland and N. Malleson (2013) The Classification of Space-Time Behaviour Patterns in a British City from Crowd-Sourced Data. In Murgante, B., Misra, S., Carlini, M., Torre, C., Nguyen, Hong-Quang, Taniar, D., Apduhan, B. O. and Gervasi, O. (Eds) Computational Science and Its Applications – Lecture Notes in Computer Science 7974 179-192 [URL] [pdf]

Malleson, N., A. Evans, A. Heppenstall, L. See (2013) The Leeds Burglary Simulator. Informatica e diritto special issue: Law and Computational Social Science 1 211-222

Andresen, M.A. and N. Malleson (2013). Crime seasonality and its variations across space. Applied Geography, 43 25–35. [URL]

Malleson, N., A. Heppenstall, L. See, A. Evans (2013) Using an agent-based crime simulation to predict the effects of urban regeneration on individual household burglary risk. Environment and Planning B: Planning and Design 40 405-426. doi:10.1068/b38057 [download]


Malleson, N. and M. Birkin (2012). Analysis of crime patterns through the integration of an agent-based model and a population microsimulation. Computers, Environment and Urban Systems 36(6) 551–561. [URL] [download]

Malleson, N., L. See, A. Evans, and A. Heppenstall (2012). Implementing comprehensive offender behaviour in a realistic agent-based model of burglary. SIMULATION 88(1) 50-71 . [URL] [download]


Birkin, M., N. Malleson, Hudson-Smith, A., Gray, S. Milton, R. (2011). Calibration of a spatial simulation model with volunteered geographical information. International Journal of Geographical Information Science 25(8) 1221-1239. [URL] [download]

Malleson, N. and Birkin, M. (2011). Towards victim-oriented crime modelling in a social science e-infrastructure. Philosophical Transactions of the Royal Society A 369(1949) 3353-3371. [URL] [download]

Andresen, M.A. and N. Malleson (2011). Testing the stability of crime patterns: implications for theory and policy. Journal of Research in Crime and Delinquency, 48(1) 58-82 [URL]


Malleson, N., A. Heppenstall and L. See (2010). Crime reduction through simulation: An agent-based model of burglary. Computers, Environment and Urban Systems 31(3) 236-250. [URL] [download]


Malleson, N., A. Evans and T. Jenkins (2009). An agent-based model of Burglary. Environment and Planning B: Planning and Design 36 1103-1123. [URL].

Malleson, N. and P. L. Brantigham (2009). Prototype Burglary Simulations For Crime Reduction and Forecasting. Crime Patterns and Analysis 2(1). [download]

Book Chapters

Heppenstall, A., A. Crooks, E. Manley and N. Malleson (2022) Simulating geographical systems using cellular automata and agent-based models. In Handbook of Spatial Analysis in the Social Sciences, edited by Sergio J. Rey and Rachel S.Franklin, Chapter 8. Edward Elgar Publishing.

Crooks, A.T., A. Heppenstall, N. Malleson, and Ed Manley (2021) Agent-Based Modeling and the City: A Gallery of Applications. In Urban Informatics, edited by Wenzhong Shi, Michael F. Goodchild, Michael Batty, Mei-Po Kwan, and Anshu Zhang, 885–910. The Urban Book Series. Springer. [].

Odiari, E. , M. Birkin, S. Grant-Muller and N. Malleson (2021) Spatial microsimulation models for rail travel: a West Yorkshire case study. Chapter 17 of Big Data Applications in Geography and Planning: An Essential Companion. Edited by Mark Birkin, Graham Clarke, Jonathan Corcoran and Robert Stimson. SBN: 978 1 78990 978 4

Tether, V., N. Malleson, W. Steenbeek and D. Birks (2021). Using Agent-Based Models to Investigate the Presence of Edge Effects Around Crime Generators and Attractors. In C. Gerritsen and H. Elffers (eds) Agent-Based Modelling for Criminological Theory Testing and Development. Routledge. [URL]

Crooks, A.T., Heppenstall, A. and N. Malleson (2018). Agent-based Modelling, in Huang, B. (ed), Comprehensive Geographic Information Systems, pp 218–243. Elsevier. [URL].

Malleson, N., Alison Heppenstall and Andrew Crooks (2018) Place-Based Simulation Modeling: Agent-Based Modeling and Virtual Environments. Oxford Research Encyclopedia of Criminology. Oxford University Press. DOI: 10.1093/acrefore/9780190264079.013.319

Crooks, A.T., N. Malleson, Wise, S. and Heppenstall, A. (2018) Big Data, Agents and the City, in Schintler, L.A. and Chen, Z. (eds.), Big Data for Urban and Regional Science, Routledge, New York, NY, pp. 204-213. [PDF]

Malleson, N. (2017) Spatial Crime Modelling and Analysis. In The Routledge Companion to Criminological Theory and Concepts, Avi Brisman, Eamonn Carrabine, Nigel South (Eds). Routledge.

Birkin, M. and N. Malleson (2015) Modelling and Simulation. In Halfpenny, P. and Procter, R. (Eds) Innovations in Digital Research Methods, Chapter 6. SAGE Publications Ltd. [URL]

Malleson, N. and A. Evans (2014) Agent-Based Models to Predict Crime at Places. In G. Bruinsma and D. Weisburd (Eds) Encyclopedia of Criminology and Criminal Justice pp 41-48 . Springer. [PDF]

Malleson, N. (2014) Calibration of Simulation Models. In G. Bruinsma and D. Weisburd (Eds) Encyclopedia of Criminology and Criminal Justice pp 243 – 252 . Springer. [PDF]

Malleson, N., L. See, A. Evans, A. Heppenstall (2014) Optimising an Agent-Based Model to Explore the Behaviour of Simulated Burglars. Theories and Simulations of Complex Social Systems. Volume 52 of Intelligent Systems Reference Library, pp 179-204. Springer. [URL] [PDF]

Birkin, M. and N. Malleson (2014) An investigation of the sensitivity of a dynamic microsimulation model of urban neighbourhood dynamics. In Dekkers G., Keegan, M. and O’Donoghue, C. (eds) New pathways in microsimulation. Ashgate.

Andresen, M.A. and N. Malleson (2013). Spatial heterogeneity in crime analysis. In M. Leitner (ed.) Crime Modeling and Mapping Using Geospatial Technologies. Volume 8 of Geotechnologies and the Environment. New York, NY: Springer.

N. Malleson (2012) Using Agent-Based Models to Simulate Crime. In Heppenstall, A.J.; Crooks, A.T.; See, L.M.; Batty, M. (Eds.) Agent-Based Models of Geographical Systems. Springer. [URL] [download]

Articles in Conference Proceedings

Cui, N., N. Malleson, V. Houlden and A. Comber (2022). Using social media data to understand the impact of the COVID-19 pandemic on urban green space use. Urban Forestry & Urban Greening 74 (127677). DOI: 10.1016/j.ufug.2022.127677.

Comber, A., P. Harris, K. Bratkova, H. H. Phe, M. Kieu, Q. T. Bui, T. T. H. Nguyen, E. Wanjau, and N. Malleson (2022) Handling the MAUP: Methods for Identifying Appropriate Scales of Aggregation Based on Measures on Spatial and Non-Spatial Variance. AGILE: GIScience Series 3 (2022): 30. DOI: 10.5194/agile-giss-3-30-2022.

Comber, A., N. Malleson, Hang Nguyen Thi Thuy, Thanh Bui Quang, Minh Kieu, Hoang Huu Phe and Paul Harris (2021) Multiscale Geographically Weighted Discriminant Analysis. GIScience 2021, 27-30 September, Poznan (online), Short Paper Proceedings. DOI: 10.25436/E2PP4F. PDF.

Malleson, N., Hang Nguyen Thi Thuy, Thanh Bui Quang, Minh Kieu, Phe Hoang Huu and Alexis Comber (2021) Urban Data Science for Sustainable Transport Policies in Emerging Economies. GIScience 2021, 27-30 September, Poznan (online), Short Paper Proceedings. DOI: 10.25436/E28G6D. PDF. Slides

R. Clay, Le-Minh Kieu, J. A. Ward, A. Heppenstall, N. Malleson (2020) Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter. In Demazeau Y., Holvoet T., Corchado J., Costantini S. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. PAAMS 2020. Lecture Notes in Computer Science, vol 12092. Springer. DOI: 10.1007/978-3-030-49778-1_6 Paper (pdf)

D. Birks, A. Heppenstall and N. Malleson (2020). Towards the Development of Societal Twins. 24th European Conference on Artificial Intelligence - ECAI 2020. PDF:

Heppenstall, A. and N. Malleson (2020). Building cities from slime mould, agents and quantum field theory. In Proceedings of AAMAS 2020. Abstract (pdf). Presentation. DOI: 10.5555/3398761.3398765.

Malleson, N., Jonathan A. Ward, A. Heppenstall, M. Adcock, D. Tang, J. Coello, and T. Crols. (2018). Understanding Input Data Requirements and Quantifying Uncertainty for Successfully Modelling ‘Smart’ Cities. In 3rd International Workshop on Agent-Based Modelling of Urban Systems (ABMUS), of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018). 10-15 July, Stockholm, Sweden. Full abstract (pdf). Slides (html).

Malleson, N., A. Tapper, J. Ward, A. Evans (2017). Forecasting Short-Term Urban Dynamics: Data Assimilation for Agent-Based Modelling. In proceedings of the Social Simulation Conference (SSC) - the 13th Annual Conference of the European Social Simulation Association (ESSA). 25-29 September 2017, Dublin, Ireland. [Slides] [PDF]

Malleson, N. and M. Birkin (2014) New Insights into Individual Activity Spaces using Crowd-Sourced Big Data. In: 2014 ASE BigData/SocialCom/CyberSecurity Conference, Stanford University, May 27-31 2014. Available online: [pdf] [presentation slides] [URL]

Malleson, N. and M. Birkin (2014) Generating Individual Behavioural Routines from Massive Social Data for the Simulation of Urban Dynamics. Proceedings of the European Conference on Complex Systems 2012.Springer Proceedings in Complexity 2014, pp 849-855 [pdf]

Malleson, N. and M. Birkin (2013). Estimating Individual Behaviour from Massive Social Data for An Urban Agent-Based Model. In A. Koch and P. Mandl (Eds) GeoSimulation: Modeling Social Phenomena in Spatial Context. Germany: Lit Verlag. ISBN: 978-3-643-90345-7 [download (pdf)]

Working Papers, Pre-Prints and Others

Oswald, Y., N. Malleson, K. Suchak (2023). An agent-based model of the 2020 international policy diffusion in response to the COVID-19 pandemic with particle filter. Preprint: arXiv:2302.11277 [cs.MA]

Tang, D. and N. Malleson (2022). Data assimilation with agent-based models using Markov chain sampling. Preprint: arXiv:2205.01616 [cs.MA]

A. Whipp, N. Malleson, Jonathan Ward, Alison Heppenstall (2021). Towards a Comprehensive Measure of the Ambient Population: Building Estimates Using Geographically Weighted Regression Preprint: 10.31235/

Neto-Bradley, A., N. Malleson, P. Ternes, R. Choudhary (2021) Why people in some parts of England pay far more than others to heat their homes. The Conversation. Published 29 November 2021, 3.40pm GMT. Available online:

Malleson, N., Kevin Minors, Le-Minh Kieu, Jonathan A. Ward, Andrew A. West, Alison Heppenstall (2019) Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter. Preprint: arXiv:1909.09397 [cs.MA].

Kieu, Le-Minh, N. Malleson, and A. Heppenstall (2019) Dealing with Uncertainty in Agent-Based Models for Short-Term Predictions. Preprint arXiv:1908.08288 [cs.MA].

Nik Lomax, N. Malleson, Le-Minh Kieu (2019) Point Pattern Processes and Models. Preprint arXiv:1910.00282v1 [stat.ME].

N. Malleson and A. Hepenstall (2018). How to make smart cities human again. The Conversation. Published 31 January, 2018. Available online: Also published in the Club of Amsterdam Journal.

Heppenstall, A. and N. Malleson (2015). How big data and The Sims are helping us to build the cities of the future. The Conversation. Published 22 October 2015, 2.38pm BST. Available online: Also published by the World Economic Forum.

Lovelace, R., Malleson, N., Harland, K., & Birkin, M. (2014). Geotagged tweets to inform a spatial interaction model: a case study of museums. arXiv preprint.

Birkin, M., Malleson, N., (2013) Investigating the Behaviour of Twitter Users to Construct an Individual-Level Model of Metropolitan Dynamics. National Centre for Research Methods Working Paper 05/13. University of Leeds. [pdf]

PhD Thesis

Malleson, N. (2010). Agent-Based Modelling of Burglary. School of Geography, University of Leeds, Leeds, LS2 9JT. [download] cv.