Alison Heppenstall
Alison Heppenstall
Professor in Geocomputation, University of Glasgow
Verified email at - Homepage
Cited by
Cited by
Agent-based models of geographical systems
AJ Heppenstall, AT Crooks, LM See, M Batty
Springer Science & Business Media, 2011
Introduction to agent-based modelling
AT Crooks, AJ Heppenstall
Agent-based models of geographical systems, 85-105, 2011
Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia
K Al-Ahmadi, L See, A Heppenstall, J Hogg
Ecological complexity 6 (2), 80-101, 2009
Crime reduction through simulation: An agent-based model of burglary
N Malleson, A Heppenstall, L See
Computers, environment and urban systems 34 (3), 236-250, 2010
Creating realistic synthetic populations at varying spatial scales: A comparative critique of population synthesis techniques
K Harland, A Heppenstall, D Smith, MH Birkin
Journal of Artificial Societies and Social Simulation 15 (1), 2012
Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm
A Mustafa, A Heppenstall, H Omrani, I Saadi, M Cools, J Teller
Computers, Environment and Urban Systems 67, 147-156, 2018
A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile
Y Yang, A Heppenstall, A Turner, A Comber
Computers, Environment and Urban Systems 77, 101361, 2019
“Space, the final frontier”: How good are agent-based models at simulating individuals and space in cities?
A Heppenstall, N Malleson, A Crooks
Systems 4 (1), 9, 2016
Agent-based modelling and geographical information systems: a practical primer
A Crooks, N Malleson, N Malleson, E Manley, A Heppenstall
Sage, 2018
Challenges, tasks, and opportunities in modeling agent-based complex systems
L An, V Grimm, A Sullivan, BL Turner Ii, N Malleson, A Heppenstall, ...
Ecological Modelling 457, 109685, 2021
Genetic algorithm optimisation of an agent-based model for simulating a retail market
AJ Heppenstall, AJ Evans, MH Birkin
Environment and Planning B: Planning and Design 34 (6), 1051-1070, 2007
Timing error correction procedure applied to neural network rainfall—runoff modelling
RJ Abrahart, AJ Heppenstall, LM See
Hydrological sciences journal 52 (3), 414-431, 2007
Sociodemographic and spatial disaggregation of e-commerce channel use in the grocery market in Great Britain
N Hood, R Urquhart, A Newing, A Heppenstall
Journal of Retailing and Consumer Services 55, 102076, 2020
Using hybrid agent-based systems to model spatially-influenced retail markets
A Heppenstall, A Evans, M Birkin
Journal of Artificial Societies and Social Simulation 9 (3), 2006
Using an agent-based crime simulation to predict the effects of urban regeneration on individual household burglary risk
N Malleson, A Heppenstall, L See, A Evans
Environment and Planning B: Planning and Design 40 (3), 405-426, 2013
Daily travel behaviour in Beijing, China: An analysis of workers' trip chains, and the role of socio-demographics and urban form
J Ma, G Mitchell, A Heppenstall
Habitat International 43, 263-273, 2014
A conceptual and neural network model for real-time flood forecasting of the Tiber River in Rome
G Napolitano, L See, B Calvo, F Savi, A Heppenstall
Physics and Chemistry of the Earth, Parts A/B/C 35 (3-5), 187-194, 2010
Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems
Y Yang, A Heppenstall, A Turner, A Comber
Computers, Environment and Urban Systems 83, 101521, 2020
A hybrid multi‐agent/spatial interaction model system for petrol price setting
AJ Heppenstall, AJ Evans, MH Birkin
Transactions in GIS 9 (1), 35-51, 2005
Synthesising carbon emission for mega-cities: A static spatial microsimulation of transport CO2 from urban travel in Beijing
J Ma, A Heppenstall, K Harland, G Mitchell
Computers, Environment and Urban Systems 45, 78-88, 2014
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