Professor Alison Heppenstall

  • Professor of Geocomputation (Urban Studies)

Biography

My undergraduate degree was in Archaeology at Grey College, University of Durham.  I was always fascinated by the emergence of culture, interactions and movements of past populations.  It seemed inevitable that after encountering programming and Geocomputation (which I loved) during my Master’s at the University of Leeds, I fell into a PhD that created and developed agent-based models for empirical applications.  

I was lucky enough to have subsequent EPSRC and ESRC Fellowships focused on the building of Machine Learning/Artificial Intelligence approaches such as neural networks, evolutionary algorithms, agent-based modelling, microsimulation, data assimilation and uncertainty quantification etc.  

Whilst at the University of Leeds, I was involved in the Leeds Institute for Data Analytics, Consumer Data Research Centre and the Urban Analytics Programme at the Alan Turing Institute.  I held the inaugural ESRC-Turing Fellowship and was awarded the International Society of Computational Economics prize for “outstanding contribution in computational social simulation” from the Italian Research Council in 2022.  I am a member of the DSAB at the Joint BioSecurity Council and a member of the Royal Geographical Society.

I am a current Alan Turing Fellow.

Research interests

I am a methodologist - I am interested in adapting and developing AI/ML approaches to solving problems in different domains.  I have extensive experience with spatial agent-based models, microsimulation, evolutionary approaches as well as strong interests in uncertainty quantification, reinforcement learning and data assimilation.  I am also interested in the development of exascale computation for application in the social sciences.  This ties into an ongoing agenda of creating urban digital twins that I am involved with at local and national level. 

Publications

List by: Type | Date

Jump to: 2024 | 2023 | 2022 | 2021 | 2020 | 2019
Number of items: 47.

2024

Brown, H. et al. (2024) Association between individual level characteristics and take-up of a Minimum Income Guarantee for Pensioners: Panel Data Analysis using data from the British Household Panel survey 1999–2002. Social Sciences & Humanities Open, 9, 100847. (doi: 10.1016/j.ssaho.2024.100847)

2023

Wijermans, N., Scholz, G., Chappin, E., Heppenstall, A. , Filatova, T., Polhill, J. G., Semeniuk, C. and Stöppler, F. (2023) Agent decision-making: the elephant in the room: enabling the justification of decision model fit in social-environmental models. Environmental Modelling and Software, 170, 105850. (doi: 10.1016/j.envsoft.2023.105850)

Griffiths, C. et al. (2023) A complex systems approach to obesity: a transdisciplinary framework for action. Perspectives in Public Health, 143(6), pp. 305-309. (doi: 10.1177/17579139231180761) (PMID:37395317) (PMCID:PMC10683338)

Antosz, P., Birks, D., Edmonds, B., Heppenstall, A. , Meyer, R., Polhill, J. G., O’Sullivan, D. and Wijermans, N. (2023) What do you want theory for? A pragmatic analysis of the roles of “theory” in agent-based modelling. Environmental Modelling and Software, 168, 105802. (doi: 10.1016/j.envsoft.2023.105802)

Heppenstall, A. , Polhill, J. G., Batty, M., Hare, M., Salt, D. and Milton, R. (2023) Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER). In: 12th International Conference on Geographic Information Science (GIScience 2023), Leeds, UK, 12-15 Sept 2023, 38:1-38:5. ISBN 9783959772884 (doi: 10.4230/LIPIcs.GIScience.2023.38)

Feng, Z., Zhao, Q. and Heppenstall, A. (2023) Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow. In: 12th International Conference on Geographic Information Science (GIScience 2023), Leeds, UK, 14-19 Sept 2023, 29:1-29:6. ISBN 9783959772884 (doi: 10.4230/LIPIcs.GIScience.2023.29)

Höhn, A. et al. (2023) Systems science methods in public health: what can they contribute to our understanding of and response to the cost-of-living crisis? Journal of Epidemiology and Community Health, 77(9), pp. 610-616. (doi: 10.1136/jech-2023-220435) (PMID:37328262) (PMCID:PMC10423532)

An, L. et al. (2023) Modeling agent decision and behavior in the light of data science and artificial intelligence. Environmental Modelling and Software, 166, 105713. (doi: 10.1016/j.envsoft.2023.105713)

Franklin, R. S. et al. (2023) Making space in geographical analysis. Geographical Analysis, 55(2), pp. 325-341. (doi: 10.1111/gean.12325)

2022

Sucharyna Thomas, L., Wickham-Jones, C. R. and Heppenstall, A. J. (2022) Combining agent-based modelling and geographical information systems to create a new approach for modelling movement dynamics: a case study of Mesolithic Orkney. Open Archaeology, 8, pp. 987-1009. (doi: 10.1515/opar-2022-0257)

Boyd, J., Wilson, R., Elsenbroich, C. , Heppenstall, A. and Meier, P. (2022) Agent-based modelling of health inequalities following the complexity turn in public health: a systematic review. International Journal of Environmental Research and Public Health, 19(24), 16807. (doi: 10.3390/ijerph192416807) (PMID:36554687) (PMCID:PMC9779847)

Wallace, R., Franklin, R., Grant-Muller, S., Heppenstall, A. and Houlden, V. (2022) Estimating the social and spatial impacts of Covid mitigation strategies in United Kingdom regions: synthetic data and dashboards. Cambridge Journal of Regions, Economy and Society, 15(3), pp. 683-702. (doi: 10.1093/cjres/rsac019)

Ternes, P., Ward, J. A., Heppenstall, A. , Kumar, V., Kieu, L.-M. and Malleson, N. (2022) Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters. Open Research Europe, 1, 131. (doi: 10.12688/openreseurope.14144.2)

Olmez, S., Thompson, J., Marfleet, E., Suchak, K., Heppenstall, A. , Manley, E., Whipp, A. and Vidanaarachchi, R. (2022) An agent-based model of heterogeneous driver behaviour and its impact on energy consumption and costs in urban space. Energies, 15(11), 4031. (doi: 10.3390/en15114031)

Urquhart, R., Newing, A., Hood, N. and Heppenstall, A. (2022) Last-mile capacity constraints in online grocery fulfilment in Great Britain. Journal of Theoretical and Applied Electronic Commerce Research, 17(2), pp. 636-651. (doi: 10.3390/jtaer17020033)

Arnold, K. F., Gilthorpe, M. S., Alwan, N. A., Heppenstall, A. J. , Tomova, G. D., McKee, M. and Tennant, P. W.G. (2022) Estimating the effects of lockdown timing on COVID-19 cases and deaths in England: a counterfactual modelling study. PLoS ONE, 17(4), e0263432. (doi: 10.1371/journal.pone.0263432) (PMID:35421094) (PMCID:PMC9009677)

Gadd, S. C., Comber, A., Gilthorpe, M. S., Suchak, K. and Heppenstall, A. J. (2022) Simplifying the interpretation of continuous time models for spatio-temporal networks. Journal of Geographical Systems, 24(2), pp. 171-198. (doi: 10.1007/s10109-020-00345-z)

McCulloch, J., Ge, J., Ward, J. A., Heppenstall, A. , Polhill, J. G. and Malleson, N. (2022) Calibrating agent-based models using uncertainty quantification methods. Journal of Artificial Societies and Social Simulation, 25(2), 1. (doi: 10.18564/jasss.4791)

Wu, G., Heppenstall, A. , Meier, P. , Purshouse, R. and Lomax, N. (2022) A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain. Scientific Data, 9, 19. (doi: 10.1038/s41597-022-01124-9) (PMID:35058471) (PMCID:PMC8776798)

Gadd, S. C., Comber, A., Tennant, P., Gilthorpe, M. S. and Heppenstall, A. J. (2022) The utility of multilevel models for continuous-time feature selection of spatio-temporal networks. Computers, Environment and Urban Systems, 91, 101728. (doi: 10.1016/j.compenvurbsys.2021.101728)

Yang, Y., Beecham, R., Heppenstall, A. , Turner, A. and Comber, A. (2022) Understanding the impacts of public transit disruptions on bikeshare schemes and cycling behaviours using spatiotemporal and graph-based analysis: a case study of four London Tube strikes. Journal of Transport Geography, 98, 103255. (doi: 10.1016/j.jtrangeo.2021.103255)

Malleson, N., Birkin, M., Birks, D., Ge, J., Heppenstall, A. , Manley, E., McCulloch, J. and Ternes, P. (2022) Agent-based modelling for urban analytics: state of the art and challenges. AI Communications, 35(4), pp. 393-406. (doi: 10.3233/AIC-220114)

2021

An, L. et al. (2021) Challenges, tasks, and opportunities in modeling agent-based complex systems. Ecological Modelling, 457, 109685. (doi: 10.1016/j.ecolmodel.2021.109685)

Smith, D. M., Heppenstall, A. and Campbell, M. (2021) Estimating health over space and time: a review of spatial microsimulation applied to public health. J - An Open Access Journal of Multidisciplinary Science, 4(2), pp. 182-192. (doi: 10.3390/j4020015)

Olmez, S., Douglas-Mann, L., Manley, E., Suchak, K., Heppenstall, A. , Birks, D. and Whipp, A. (2021) Exploring the impact of driver adherence to speed limits and the interdependence of roadside collisions in an urban environment: an agent-based modelling approach. Applied Sciences, 11(12), 5336. (doi: 10.3390/app11125336)

Crooks, A., Heppenstall, A. , Malleson, N. and Manley, E. (2021) Agent-based modeling and the city: A gallery of applications. In: Shi, W., Goodchild, M. F., Batty, M., Kwan, M.-P. and Zhang, A. (eds.) Urban Informatics. Series: Urban book series. Springer, pp. 885-910. ISBN 9789811589836 (doi: 10.1007/978-981-15-8983-6_46)

Whipp, A., Malleson, N., Ward, J. and Heppenstall, A. (2021) Estimates of the ambient population: assessing the utility of conventional and novel data sources. ISPRS International Journal of Geo-Information, 10(3), 131. (doi: 10.3390/ijgi10030131)

Heppenstall, A. , Crooks, A., Malleson, N., Manley, E., Ge, J. and Batty, M. (2021) Future developments in geographical agent‐based models: challenges and opportunities. Geographical Analysis, 53(1), pp. 76-91. (doi: 10.1111/gean.12267) (PMID:33678813) (PMCID:PMC7898830)

Roxburgh, N., Stringer, L. C., Evans, A., GC, R. K., Malleson, N. and Heppenstall, A. J. (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)

Roxburgh, N., Evans, A., GC, R. K., Malleson, N., Heppenstall, A. and Stringer, L. (2021) An empirically informed agent-based model of a Nepalese smallholder village. MethodsX, 8, 101276. (doi: 10.1016/j.mex.2021.101276) (PMID:34434796) (PMCID:PMC8374244)

2020

Yang, Y., Heppenstall, A. , Turner, A. and Comber, A. (2020) Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems. Computers, Environment and Urban Systems, 83, 101521. (doi: 10.1016/j.compenvurbsys.2020.101521)

Hood, N., Urquhart, R., Newing, A. and Heppenstall, A. (2020) Sociodemographic and spatial disaggregation of e-commerce channel use in the grocery market in Great Britain. Journal of Retailing and Consumer Services, 55, 102076. (doi: 10.1016/j.jretconser.2020.102076)

Malleson, N., Minors, K., Kieu, L.-M., Ward, J. A., West, A. and Heppenstall, A. (2020) Simulating crowds in real time with agent-based modelling and a particle filter. Journal of Artificial Societies and Social Simulation, 23(3), p. 3. (doi: 10.18564/jasss.4266)

Olner, D., Mitchell, G., Heppenstall, A. and Pryce, G. (2020) The spatial economics of energy justice: modelling the trade impacts of increased transport costs in a low carbon transition and the implications for UK regional inequality. Energy Policy, 140, 111378. (doi: 10.1016/j.enpol.2020.111378)

Owen, A. and Heppenstall, A. (2020) Making the case for simulation: Unlocking carbon reduction through simulation of individual ‘middle actor’ behaviour. Environment and Planning B: Urban Analytics and City Science, 47(3), pp. 457-472. (doi: 10.1177/2399808318784597)

Xiang, L., Stillwell, J., Burns, L. and Heppenstall, A. (2020) Measuring and assessing regional education inequalities in China under changing policy regimes. Applied Spatial Analysis and Policy, 13(1), pp. 91-112. (doi: 10.1007/s12061-019-09293-8)

Manson, S. et al. (2020) Methodological issues of spatial agent-based models. Journal of Artificial Societies and Social Simulation, 23(1), 3. (doi: 10.18564/jasss.4174)

Kieu, L.-M., Malleson, N. and Heppenstall, A. (2020) Dealing with uncertainty in agent-based models for short-term predictions. Royal Society Open Science, 7(1), 191074. (doi: 10.1098/rsos.191074) (PMID:32218939) (PMCID:PMC7029931)

2019

Levine, S. Z., Gadd, S. C., Tennant, P. W. G., Heppenstall, A. J. , Boehnke, J. R. and Gilthorpe, M. S. (2019) Analysing trajectories of a longitudinal exposure: A causal perspective on common methods in lifecourse research. PLoS ONE, 14(12), e0225217. (doi: 10.1371/journal.pone.0225217) (PMID:31800576) (PMCID:PMC6892534)

Meier, P. et al. (2019) The SIPHER Consortium: introducing the new UK hub for systems science in public health and health economic research. Wellcome Open Research, 4, 174. (doi: 10.12688/wellcomeopenres.15534.1) (PMID:31815191) (PMCID:PMC6880277)

Yang, Y., Heppenstall, A. , Turner, A. and Comber, A. (2019) A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile. Computers, Environment and Urban Systems, 77, 101361. (doi: 10.1016/j.compenvurbsys.2019.101361)

Yang, Y., Heppenstall, A. , Turner, A. and Comber, A. (2019) Who, where, why and when? Using smart card and social media data to understand urban mobility. ISPRS International Journal of Geo-Information, 8(6), 271. (doi: 10.3390/ijgi8060271)

Arnold, K.F., Ellison, G.T.H., Gadd, S., Textor, J., Tennant, P.W.G., Heppenstall, A. and Gilthorpe, M.S. (2019) Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges. Statistical Methods in Medical Research, 28(5), pp. 1347-1364. (doi: 10.1177/0962280218756158) (PMID:29451093) (PMCID:PMC6484949)

Heppenstall, A. and Crooks, A. (2019) Guest editorial for spatial agent-based models: current practices and future trends. GeoInformatica, 23(2), pp. 163-167. (doi: 10.1007/s10707-019-00349-y)

Heppenstall, A. and Crooks, A. (Eds.) (2019) Special Issue on Spatial Agent-Based Models: Current Practices and Future Trends. Geoinformatica. 23(2) [Edited Journal]

Gulma, U. L., Evans, A., Heppenstall, A. and Malleson, N. (2019) Diversity and burglary: Do community differences matter? Transactions in GIS, 23(2), pp. 181-202. (doi: 10.1111/tgis.12511)

Alotaibi, N. I., Evans, A. J., Heppenstall, A. J. and Malleson, N. S. (2019) 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), pp. 5-32. (doi: 10.1177/1057567717709497)

This list was generated on Sat Apr 20 13:11:55 2024 BST.
Number of items: 47.

Articles

Brown, H. et al. (2024) Association between individual level characteristics and take-up of a Minimum Income Guarantee for Pensioners: Panel Data Analysis using data from the British Household Panel survey 1999–2002. Social Sciences & Humanities Open, 9, 100847. (doi: 10.1016/j.ssaho.2024.100847)

Wijermans, N., Scholz, G., Chappin, E., Heppenstall, A. , Filatova, T., Polhill, J. G., Semeniuk, C. and Stöppler, F. (2023) Agent decision-making: the elephant in the room: enabling the justification of decision model fit in social-environmental models. Environmental Modelling and Software, 170, 105850. (doi: 10.1016/j.envsoft.2023.105850)

Griffiths, C. et al. (2023) A complex systems approach to obesity: a transdisciplinary framework for action. Perspectives in Public Health, 143(6), pp. 305-309. (doi: 10.1177/17579139231180761) (PMID:37395317) (PMCID:PMC10683338)

Antosz, P., Birks, D., Edmonds, B., Heppenstall, A. , Meyer, R., Polhill, J. G., O’Sullivan, D. and Wijermans, N. (2023) What do you want theory for? A pragmatic analysis of the roles of “theory” in agent-based modelling. Environmental Modelling and Software, 168, 105802. (doi: 10.1016/j.envsoft.2023.105802)

Höhn, A. et al. (2023) Systems science methods in public health: what can they contribute to our understanding of and response to the cost-of-living crisis? Journal of Epidemiology and Community Health, 77(9), pp. 610-616. (doi: 10.1136/jech-2023-220435) (PMID:37328262) (PMCID:PMC10423532)

An, L. et al. (2023) Modeling agent decision and behavior in the light of data science and artificial intelligence. Environmental Modelling and Software, 166, 105713. (doi: 10.1016/j.envsoft.2023.105713)

Franklin, R. S. et al. (2023) Making space in geographical analysis. Geographical Analysis, 55(2), pp. 325-341. (doi: 10.1111/gean.12325)

Sucharyna Thomas, L., Wickham-Jones, C. R. and Heppenstall, A. J. (2022) Combining agent-based modelling and geographical information systems to create a new approach for modelling movement dynamics: a case study of Mesolithic Orkney. Open Archaeology, 8, pp. 987-1009. (doi: 10.1515/opar-2022-0257)

Boyd, J., Wilson, R., Elsenbroich, C. , Heppenstall, A. and Meier, P. (2022) Agent-based modelling of health inequalities following the complexity turn in public health: a systematic review. International Journal of Environmental Research and Public Health, 19(24), 16807. (doi: 10.3390/ijerph192416807) (PMID:36554687) (PMCID:PMC9779847)

Wallace, R., Franklin, R., Grant-Muller, S., Heppenstall, A. and Houlden, V. (2022) Estimating the social and spatial impacts of Covid mitigation strategies in United Kingdom regions: synthetic data and dashboards. Cambridge Journal of Regions, Economy and Society, 15(3), pp. 683-702. (doi: 10.1093/cjres/rsac019)

Ternes, P., Ward, J. A., Heppenstall, A. , Kumar, V., Kieu, L.-M. and Malleson, N. (2022) Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters. Open Research Europe, 1, 131. (doi: 10.12688/openreseurope.14144.2)

Olmez, S., Thompson, J., Marfleet, E., Suchak, K., Heppenstall, A. , Manley, E., Whipp, A. and Vidanaarachchi, R. (2022) An agent-based model of heterogeneous driver behaviour and its impact on energy consumption and costs in urban space. Energies, 15(11), 4031. (doi: 10.3390/en15114031)

Urquhart, R., Newing, A., Hood, N. and Heppenstall, A. (2022) Last-mile capacity constraints in online grocery fulfilment in Great Britain. Journal of Theoretical and Applied Electronic Commerce Research, 17(2), pp. 636-651. (doi: 10.3390/jtaer17020033)

Arnold, K. F., Gilthorpe, M. S., Alwan, N. A., Heppenstall, A. J. , Tomova, G. D., McKee, M. and Tennant, P. W.G. (2022) Estimating the effects of lockdown timing on COVID-19 cases and deaths in England: a counterfactual modelling study. PLoS ONE, 17(4), e0263432. (doi: 10.1371/journal.pone.0263432) (PMID:35421094) (PMCID:PMC9009677)

Gadd, S. C., Comber, A., Gilthorpe, M. S., Suchak, K. and Heppenstall, A. J. (2022) Simplifying the interpretation of continuous time models for spatio-temporal networks. Journal of Geographical Systems, 24(2), pp. 171-198. (doi: 10.1007/s10109-020-00345-z)

McCulloch, J., Ge, J., Ward, J. A., Heppenstall, A. , Polhill, J. G. and Malleson, N. (2022) Calibrating agent-based models using uncertainty quantification methods. Journal of Artificial Societies and Social Simulation, 25(2), 1. (doi: 10.18564/jasss.4791)

Wu, G., Heppenstall, A. , Meier, P. , Purshouse, R. and Lomax, N. (2022) A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain. Scientific Data, 9, 19. (doi: 10.1038/s41597-022-01124-9) (PMID:35058471) (PMCID:PMC8776798)

Gadd, S. C., Comber, A., Tennant, P., Gilthorpe, M. S. and Heppenstall, A. J. (2022) The utility of multilevel models for continuous-time feature selection of spatio-temporal networks. Computers, Environment and Urban Systems, 91, 101728. (doi: 10.1016/j.compenvurbsys.2021.101728)

Yang, Y., Beecham, R., Heppenstall, A. , Turner, A. and Comber, A. (2022) Understanding the impacts of public transit disruptions on bikeshare schemes and cycling behaviours using spatiotemporal and graph-based analysis: a case study of four London Tube strikes. Journal of Transport Geography, 98, 103255. (doi: 10.1016/j.jtrangeo.2021.103255)

Malleson, N., Birkin, M., Birks, D., Ge, J., Heppenstall, A. , Manley, E., McCulloch, J. and Ternes, P. (2022) Agent-based modelling for urban analytics: state of the art and challenges. AI Communications, 35(4), pp. 393-406. (doi: 10.3233/AIC-220114)

An, L. et al. (2021) Challenges, tasks, and opportunities in modeling agent-based complex systems. Ecological Modelling, 457, 109685. (doi: 10.1016/j.ecolmodel.2021.109685)

Smith, D. M., Heppenstall, A. and Campbell, M. (2021) Estimating health over space and time: a review of spatial microsimulation applied to public health. J - An Open Access Journal of Multidisciplinary Science, 4(2), pp. 182-192. (doi: 10.3390/j4020015)

Olmez, S., Douglas-Mann, L., Manley, E., Suchak, K., Heppenstall, A. , Birks, D. and Whipp, A. (2021) Exploring the impact of driver adherence to speed limits and the interdependence of roadside collisions in an urban environment: an agent-based modelling approach. Applied Sciences, 11(12), 5336. (doi: 10.3390/app11125336)

Whipp, A., Malleson, N., Ward, J. and Heppenstall, A. (2021) Estimates of the ambient population: assessing the utility of conventional and novel data sources. ISPRS International Journal of Geo-Information, 10(3), 131. (doi: 10.3390/ijgi10030131)

Heppenstall, A. , Crooks, A., Malleson, N., Manley, E., Ge, J. and Batty, M. (2021) Future developments in geographical agent‐based models: challenges and opportunities. Geographical Analysis, 53(1), pp. 76-91. (doi: 10.1111/gean.12267) (PMID:33678813) (PMCID:PMC7898830)

Roxburgh, N., Stringer, L. C., Evans, A., GC, R. K., Malleson, N. and Heppenstall, A. J. (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)

Roxburgh, N., Evans, A., GC, R. K., Malleson, N., Heppenstall, A. and Stringer, L. (2021) An empirically informed agent-based model of a Nepalese smallholder village. MethodsX, 8, 101276. (doi: 10.1016/j.mex.2021.101276) (PMID:34434796) (PMCID:PMC8374244)

Yang, Y., Heppenstall, A. , Turner, A. and Comber, A. (2020) Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems. Computers, Environment and Urban Systems, 83, 101521. (doi: 10.1016/j.compenvurbsys.2020.101521)

Hood, N., Urquhart, R., Newing, A. and Heppenstall, A. (2020) Sociodemographic and spatial disaggregation of e-commerce channel use in the grocery market in Great Britain. Journal of Retailing and Consumer Services, 55, 102076. (doi: 10.1016/j.jretconser.2020.102076)

Malleson, N., Minors, K., Kieu, L.-M., Ward, J. A., West, A. and Heppenstall, A. (2020) Simulating crowds in real time with agent-based modelling and a particle filter. Journal of Artificial Societies and Social Simulation, 23(3), p. 3. (doi: 10.18564/jasss.4266)

Olner, D., Mitchell, G., Heppenstall, A. and Pryce, G. (2020) The spatial economics of energy justice: modelling the trade impacts of increased transport costs in a low carbon transition and the implications for UK regional inequality. Energy Policy, 140, 111378. (doi: 10.1016/j.enpol.2020.111378)

Owen, A. and Heppenstall, A. (2020) Making the case for simulation: Unlocking carbon reduction through simulation of individual ‘middle actor’ behaviour. Environment and Planning B: Urban Analytics and City Science, 47(3), pp. 457-472. (doi: 10.1177/2399808318784597)

Xiang, L., Stillwell, J., Burns, L. and Heppenstall, A. (2020) Measuring and assessing regional education inequalities in China under changing policy regimes. Applied Spatial Analysis and Policy, 13(1), pp. 91-112. (doi: 10.1007/s12061-019-09293-8)

Manson, S. et al. (2020) Methodological issues of spatial agent-based models. Journal of Artificial Societies and Social Simulation, 23(1), 3. (doi: 10.18564/jasss.4174)

Kieu, L.-M., Malleson, N. and Heppenstall, A. (2020) Dealing with uncertainty in agent-based models for short-term predictions. Royal Society Open Science, 7(1), 191074. (doi: 10.1098/rsos.191074) (PMID:32218939) (PMCID:PMC7029931)

Levine, S. Z., Gadd, S. C., Tennant, P. W. G., Heppenstall, A. J. , Boehnke, J. R. and Gilthorpe, M. S. (2019) Analysing trajectories of a longitudinal exposure: A causal perspective on common methods in lifecourse research. PLoS ONE, 14(12), e0225217. (doi: 10.1371/journal.pone.0225217) (PMID:31800576) (PMCID:PMC6892534)

Meier, P. et al. (2019) The SIPHER Consortium: introducing the new UK hub for systems science in public health and health economic research. Wellcome Open Research, 4, 174. (doi: 10.12688/wellcomeopenres.15534.1) (PMID:31815191) (PMCID:PMC6880277)

Yang, Y., Heppenstall, A. , Turner, A. and Comber, A. (2019) A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile. Computers, Environment and Urban Systems, 77, 101361. (doi: 10.1016/j.compenvurbsys.2019.101361)

Yang, Y., Heppenstall, A. , Turner, A. and Comber, A. (2019) Who, where, why and when? Using smart card and social media data to understand urban mobility. ISPRS International Journal of Geo-Information, 8(6), 271. (doi: 10.3390/ijgi8060271)

Arnold, K.F., Ellison, G.T.H., Gadd, S., Textor, J., Tennant, P.W.G., Heppenstall, A. and Gilthorpe, M.S. (2019) Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges. Statistical Methods in Medical Research, 28(5), pp. 1347-1364. (doi: 10.1177/0962280218756158) (PMID:29451093) (PMCID:PMC6484949)

Heppenstall, A. and Crooks, A. (2019) Guest editorial for spatial agent-based models: current practices and future trends. GeoInformatica, 23(2), pp. 163-167. (doi: 10.1007/s10707-019-00349-y)

Gulma, U. L., Evans, A., Heppenstall, A. and Malleson, N. (2019) Diversity and burglary: Do community differences matter? Transactions in GIS, 23(2), pp. 181-202. (doi: 10.1111/tgis.12511)

Alotaibi, N. I., Evans, A. J., Heppenstall, A. J. and Malleson, N. S. (2019) 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), pp. 5-32. (doi: 10.1177/1057567717709497)

Book Sections

Crooks, A., Heppenstall, A. , Malleson, N. and Manley, E. (2021) Agent-based modeling and the city: A gallery of applications. In: Shi, W., Goodchild, M. F., Batty, M., Kwan, M.-P. and Zhang, A. (eds.) Urban Informatics. Series: Urban book series. Springer, pp. 885-910. ISBN 9789811589836 (doi: 10.1007/978-981-15-8983-6_46)

Edited Journals

Heppenstall, A. and Crooks, A. (Eds.) (2019) Special Issue on Spatial Agent-Based Models: Current Practices and Future Trends. Geoinformatica. 23(2) [Edited Journal]

Conference Proceedings

Heppenstall, A. , Polhill, J. G., Batty, M., Hare, M., Salt, D. and Milton, R. (2023) Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER). In: 12th International Conference on Geographic Information Science (GIScience 2023), Leeds, UK, 12-15 Sept 2023, 38:1-38:5. ISBN 9783959772884 (doi: 10.4230/LIPIcs.GIScience.2023.38)

Feng, Z., Zhao, Q. and Heppenstall, A. (2023) Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow. In: 12th International Conference on Geographic Information Science (GIScience 2023), Leeds, UK, 14-19 Sept 2023, 29:1-29:6. ISBN 9783959772884 (doi: 10.4230/LIPIcs.GIScience.2023.29)

This list was generated on Sat Apr 20 13:11:55 2024 BST.

Grants

AI for Collective Intelligence (AI4CI) - AI Hub led from the University of Bristol. Funded by EPSRC for 5 years. Start: 01/02/24.
 
I am leading the Smart Cities theme in conjunction with CASA @UCL. 

The AI4CI Hub combines AI expertise with excellence in key application domains in a strongly integrated national consortium of leading UK universities working with dozens of non-academic partners from across government, industry and civil society.

 
Exascale computing exploits GPU (Graphical Processing Unit) technology to generate calculation speeds of 1018 floating point operations per second. A laptop does about 109. Agent-based modelling is a form of computer simulation that gained prominence during the Covid crisis to evaluate policy options. However, it is still not typically making use of high-performance computing resources. So, ExAMPLER will:
  • Explore the state-of-the-art in high-end computing use by the empirical agent-based modelling community;
  • Benchmark that against other disciplines;
  • Co-construct visions of future agent-based modelling underpinned by exascale computing;
  • Develop a roadmap to realize those visions.
 
The HealthMod Cluster: Enhancing Policy Modelling Capabilities to Tackle the Economic Determinants of Health and Health Inequality  Funded by MRC. 5 years commencing April 2024.
 
HealthMod is a large research programme that brings together scientists, policymakers, and charities interested in using economic policies to improve people's health. We particularly want to improve the health of people who experience disadvantage or discrimination in their lives, as they tend to spend much more of their lives in poor health and also die younger, and the situation has got worse over the past decade.

Within this cluster, I am working on the computational modelling aspect, in particular the synthetic population and dynamic modelling for policy appraisal.  

 

Systems Science in Public Health and Health Economics Research (SIPHER) 

SIPHER vision is a shift from health policy to health public policy.  Along with Dr Nik Lomax, I am responsible for the data management and micro-modelling work streams of this 5 year UKPRP consortium

 

Previous projects (within last 3 years)

Behavioural, ecological and socio-economic tools for modelling agricultural policy (BESTMAP - H2020):  My role in this project is to devise ways to scale up ABMs from local to national levels.

Consumer Data Research Centre (ESRC):The CDRC seeks to develop new approaches to social science research which are needed to exploit new sources of consumer data. I hold the post of Director of Innovation.

Understanding and Quantifying Uncertainty in Agent-Based Models for Smart City Forecasts: (Turing) Developing methods that can be used to better understand uncertainty in individual-level models of cities

Capturing relationships between individuals: Integrating Causal Inference and Agent-based modelling: (Turing). This project will connect ongoing work in casual inference modelling to agent-based simulations to robustly capture and simulate causal relationships between individuals.

Forecasting the future of policing (Turing): This project is in conjunction with UCL and The Met to explore the potential of ABM as a tool for forecasting demands in policing.  The PI is Dr Dan Birks (University of Leeds).

Quantifying Utility and Preserving Privacy in Synthetic Data (QUIPP):  This is a joint project with the Turing that is aims to generate synthetic versions of sensitive data sets that contain all the relationships and preserve individual privacy.

 

Supervision

  • Feng, Zixin
    Exploring the Electric Vehicle Driver Behaviours for the Sustainable Future of Charging Infrastructure

Research datasets

Jump to: 2023
Number of items: 1.

2023

Boyd, J., Hjelmskog, A. , Elsenbroich, C. , Heppenstall, A. , Toney, J. and Meier, P. (2023) Climate mitigation and adaptation action in the UK and devolved nations - A typology. [Data Collection]

This list was generated on Sun Apr 21 08:01:41 2024 BST.