Crooks, A, N. Malleson, E. Manley, A. Heppenstall (2019) Agent-Based Modelling and Geographical Information Systems. Sage.
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].
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].
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. http://www.mdpi.com/2076-3263/8/6/196
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) DOI: https://dx.doi.org/10.1098/rsos.150703
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 [DOI: 10.1016/j.jcrimjus.2016.03.002] (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 [DOI:10.3390/systems4010009]
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] [DOI: 10.1080/15230406.2014.905756]
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
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]
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. 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]
Crooks, A.T., Heppenstall, A. and Malleson, N. (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., Malleson, N., 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., 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.
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: http://www.ase360.org/handle/123456789/31. [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)]
N. Malleson and A. Hepenstall (2018). How to make smart cities human again. The Conversation. Published 31 January, 2018. Available online: https://theconversation.com/how-to-make-smart-cities-human-again-88453. 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: https://theconversation.com/how-big-data-and-the-sims-are-helping-us-to-build-the-cities-of-the-future-47292. 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]
Malleson, N. (2010). Agent-Based Modelling of Burglary. School of Geography, University of Leeds, Leeds, LS2 9JT. [download] cv.