Professor Alison Heppenstall

  • Professor of Geocomputation (School of Social & Political Sciences)

Biography

My PhD (University of Leeds) was a mixture of spatial econometrics and artificial intelligence, specifically building agent-based models to replicate dynamics within a retail market (petrol prices).   Subsequent EPSRC and ESRC Fellowships focused on the building of Machine Learning approaches such as neural networks and evolutionary algorithms for both flood and water quality prediction.  My academic career has focused on working with individual-based approaches, both microsimulation and agent-based modelling. I am interested in the methodological developments around individual-based models.  These include uncertainty quantification, probabilistic programming, graph theory, deep learning, reinforcement learning, emulators, particle filters, neural networks 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 an ESRC-Turing Fellowship and continue to work on urban digital twins with the Turing.  I am a member of the DSAB at the Joint BioSecurity Council and a member of the Royal Geographical Society.

I work across both COSS and the MRC Unit at the University of Glasgow.

Research interests

My research interests span a wide range of ML and AI approaches including uncertainty quantification, probabilistic programming, graph theory, deep learning, reinforcement learning, emulators, particle filters, neural networks etc.  I work within health and sustainability to understand the impact of net zero policies on health inequalities.  

Publications

List by: Type | Date

Jump to: 2022
Number of items: 12.

2022

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)

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, (Accepted for Publication)

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)

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, (doi: 10.1093/cjres/rsac019) (Early Online Publication)

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)

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)

Franklin, R. S. et al. (2022) Making space in geographical analysis. Geographical Analysis, (doi: 10.1111/gean.12325) (Early Online Publication)

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)

This list was generated on Sat Dec 3 11:29:26 2022 GMT.
Jump to: Articles
Number of items: 12.

Articles

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)

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, (Accepted for Publication)

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)

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, (doi: 10.1093/cjres/rsac019) (Early Online Publication)

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)

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)

Franklin, R. S. et al. (2022) Making space in geographical analysis. Geographical Analysis, (doi: 10.1111/gean.12325) (Early Online Publication)

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)

This list was generated on Sat Dec 3 11:29:26 2022 GMT.

Grants

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

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.