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Agent-Based Models of Geographical Systems

Heppenstall, Alison J., Crooks, Andrew T., See, Linda M. and Batty, Michael (eds.)
Springer-Verlag: Berlin, 2012
ISBN 9789048189267 (pb)

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Reviewed by José Manuel Galán
University of Burgos

Cover of book This book is a collection of papers that offer a comprehensive state-of-the-art of agent-based modelling (ABM) in geography. It is structured around two sections: the first on methodological aspects of computational modelling and ABM, the second on applications in the field.

The first part of the first section provides an overview of spatial models in human systems, including spatial econometric models, system dynamics models and land-use transportation models. Specific chapters emphasize microsimulation models, cellular automata and differences and similarities between such techniques and ABM. The second part deals with the principles and concepts of ABM. The first two chapters analyse general modelling issues, with the first identifying conditions that make ABM more appropriate than other modelling paradigms and the second situating ABM in the context of complexity theory (respectively by David O' Sullivan and colleagues, Steven N. Manson and colleagues). The following three chapters deal with more practical aspects of ABM, ranging from the general design of the model (by Mohamed Abdou, Lynne Hamill and Nigel Gilbert) to how to model human behaviour (by William G. Kennedy) or the ever-challenging issues of ABM calibration, verification and validation (by The An Ngo and Linda See). Two of the more popular ABMs are used here, such as Sugarscape (Epstein and Axtell 1996) and Schelling's residential segregation model (Schelling 1978). This second part closes with an additional chapter discussing the relation between ABM and social network analysis (by Shah Jamal Amal and Armando Geller). Here, it is suggested that better integration with social network analysis is important for two reasons: (i) it allows us to relax the unrealistic and inappropriate, but widely followed, assumption that agent interaction networks are simple, regular or trivial (e.g., random networks) and (ii), at the same time, it helps us to realise that social mechanisms and processes tend often to give rise to complex interaction topologies, which should be seriously considered.

The third and final part of the first section includes several chapters that elaborate on methods, techniques and tools to design ABM. A review of software platforms is presented that explains how spatial entities and their capabilities can be represented in ABM integrating geographic information systems (GIS) (by Andrew T. Crooks and Christian G. E. Castle). Secondly, there is a review of approaches to model scaling in large ABM, including parallel computing and an example of usage of an environment-parallel approach (by Hazel R. Parry and Mike Bithell). Other topics discussed are as follows: uncertainty and error, complementarities between ABM and microsimulation, models of crowding and an introduction to the Overview, Design concepts, Details (ODD) protocol (recently updated by (Grimm et al. 2010 and (Polhill 2010), a standard framework for formulating and communicating ABM, and Pattern-Oriented Modelling (POM), a set of strategies which include patterns for model structure, selecting sub-models and parameterisation to help to look at all relevant aspects of the targeted system (Grimm et al. 2005).

The second section is devoted to applications and is divided in parts 4 and 5. Part 4 presents nine examples of fine-scale, micro-applications of spatial agent-based models. It covers a variety of topics that illustrate many of the concepts depicted in the methodological section of the book, such as crime, urban geosimulation, pedestrian modelling, business applications, education, health, housing choice, residential mobility, and land-use change. The final part (5) provides interesting examples of macro models linking spatial ABM to aggregate applications, including peripherization growth processes in Latin American cities, urban growth in Australia, epidemiological models to analyse the spread of mumps, shifting cultivation, traffic flow, and land cover of agriculture in Brazil.

Then, three chapters follow that illustrate sociophysics approaches in spatial contexts. The first one proposes an adaptive agent framework to explain the city size distributions in France, Russia and the USA (by Timothy R. Gulden and Ross A. Hammond). The second one analyses differences and similarities between an entropy-maximising model and an ABM of urban retail, both applied to a metropolitan county of South Yorkshire in UK (by Joel Dearden and Alan Wilson). The third one presents the SIMPOM framework aimed at reconstructing the evolution of urban settlements in geographical space and time (by Denise Pumain). Finally, the book ends with a chapter where the editors discuss current and future challenges of modelling geographical systems.

To sum up, this book is an essential reference for any researcher in the field of ABM and geographical systems. Although a more than 700 pages book can scare everyone, the admirably collective effort to synthesize and provide an up-to-date overview of the most relevant methodological and applied works in the field is worth the challenge. Furthermore, it must be said that it can also be recommended to any reader interested in ABM in general, even if initially unconcerned about geographical applications. Indeed, the first book section covers most of the relevant topics to be considered as a primer in ABM, regardless of the context of application, especially the second ("Principles and Concepts of Agent-Based Modelling") and many chapters of the third part ("Methods, Techniques and Tools for the Design and Construction of Agent-Based Models").

* References

EPSTEIN, JM and Axtell RL (1996) Growing Artificial Societies. Social Science From the Bottom Up. Cambridge, MA: Brookings Institution Press-MIT Press

GRIMM, V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke HH, Weiner J, Wiegand T, and DeAngelis DL (2005) Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310(5750), pp. 987-991

GRIMM, V, Berger U, DeAngelis DL, Polhill JG, Giske J, and Railsback SF (2010) The ODD protocol: A review and first update. Ecological Modelling, 221(23), pp. 2760-2768

POLHILL, JG (2010) ODD Updated. Journal of Artificial Societies and Social Simulation, 13(4): http://jasss.soc.surrey.ac.uk/13/4/9.html

SCHELLING, TC (1978) Micromotives and macrobehavior. New York: Norton


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