2024

Olmez, S., Birks, D., Heppenstall, A. and Ge, J. (2024) Learning the rational choice perspective: a reinforcement learning approach to simulating offender behaviours in criminological agent-based models. Computers, Environment and Urban Systems, (Accepted for Publication)

Heppenstall, A. , Wang, M. , Demsar, U., Lemmens, R. and Yao, J. (2024) Preface. AGILE: GIScience Series, 5, 1. (doi: 10.5194/agile-giss-5-1-2024)

Kopasker, D. et al. (2024) Evaluating the influence of taxation and social security policies on psychological distress: a microsimulation study of the UK during the COVID-19 economic crisis. Social Science and Medicine, (doi: 10.1016/j.socscimed.2024.116953) (In Press)

Heppenstall, A. , Wang, M. , Demsar, U., Lemmens, R. and Yao, J. (Eds.) (2024) 27th AGILE Conference on Geographic Information Science “Geographic Information Science for a Sustainable Future”. AGILE: GIScience Series. 5 [Edited Journal]

Wolf, L. J., Harris, R. and Heppenstall, A. (Eds.) (2024) A Research Agenda for Spatial Analysis. Series: Elgar Research Agendas. Edward Elgar. ISBN 9781802203226

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)

Articles

Olmez, S., Birks, D., Heppenstall, A. and Ge, J. (2024) Learning the rational choice perspective: a reinforcement learning approach to simulating offender behaviours in criminological agent-based models. Computers, Environment and Urban Systems, (Accepted for Publication)

Heppenstall, A. , Wang, M. , Demsar, U., Lemmens, R. and Yao, J. (2024) Preface. AGILE: GIScience Series, 5, 1. (doi: 10.5194/agile-giss-5-1-2024)

Kopasker, D. et al. (2024) Evaluating the influence of taxation and social security policies on psychological distress: a microsimulation study of the UK during the COVID-19 economic crisis. Social Science and Medicine, (doi: 10.1016/j.socscimed.2024.116953) (In Press)

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 Books

Wolf, L. J., Harris, R. and Heppenstall, A. (Eds.) (2024) A Research Agenda for Spatial Analysis. Series: Elgar Research Agendas. Edward Elgar. ISBN 9781802203226

Edited Journals

Heppenstall, A. , Wang, M. , Demsar, U., Lemmens, R. and Yao, J. (Eds.) (2024) 27th AGILE Conference on Geographic Information Science “Geographic Information Science for a Sustainable Future”. AGILE: GIScience Series. 5 [Edited Journal]

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)