Reviewed by
Andrew Crooks
George Mason University
Urban systems are constantly evolving and examining how they are affected by climate change or social restructuring is a non-trivial task. The responses can be manifested in land use change, the gentrification of neighbourhoods or that of residential segregation. Such processes are some of the core issues in understanding urban systems especially how individual decisions impact on others. Cities are in constant flux, the processes seen within them are dynamic and all take place at different spatial and temporal scales. Understanding the complex nature of such interactions is thus extremely difficult (Wilson 2000) and only through modelling can one gain insight into these processes. Modelling and Simulating Urban Processes edited by Andreas Koch and Peter Mandl brings together six papers ranging from spatial-econometric models to geostatistical techniques and multi-agent systems, to analyse and visualise patterns of social organisation, individual behaviour and spatial fabrics to explore urban change. As the preface to the edited volume states: "Modelling and (geo-)simulation techniques offer a wide range of opportunities which cannot completely or adequately be accomplished by traditional quantitative or qualitative methods". The remainder of the book follows up on this discussion in more details.
In the first chapter, entitled "Geosimulation: Expeditions to the invisible relationships of space, time and social life", Kock reflects on the theoretical, epistemological and methodological conditions that link geosimulation modelling techniques with social geographical themes (such as residential segregation). Koch introduces the reader to the ideas and principles of complexity science, frames the discussion of how one can use ideas from complexity to model geographical problems, and explores the role of models more generally. He argues that geosimulation is a diverse methodology where Cellular automata (CA), Agent-based modelling (ABM) and geographical information science (GIS) can be used to meet the challenges of representing social-temporal-spatial scaling at different levels. These notions are similar to what Benenson and Torrens (2004) put forward in their book entitled Geosimulation: Automata-Based Modelling of Urban Phenomena.
In the second chapter, Konig presents a paper entitled "An agent-based simulation to show the effects of the size of a city on the socio-spatial organisation of its population". The paper argues convincingly that the size of the urban settlement has an effect on the socio-spatial organisation and the residential segregation of its population. Konig presents a segregation model which is comprised of a graph-based spatial representation of a city which he terms "Circle City" which transforms a city's block pattern into an abstract graph-based model, whereby the streets are nodes connected to other nodes via edges. On top of this network, Konig implements an agent-based model which is a nice variation over other Schelling (1971) type models that often use a simple grid structure. The model is described in detail and uses the ODD protocol (Grimm et al. 2006) which greatly facilitates in its understanding and gives confidence in the results presented. Specifically how the effects of the built environment can impact on patterns of segregation.
Moving away from residential segregation, Giffinger and Seidl explore the process of gentrification linking both the supply and demand side of the gentrification phenomena, with a paper entitled "Micro-Modelling of gentrification: a useful tool for planning?". This chapter is well grounded as the authors discuss past modelling efforts, many of which only focus on one side or the other. They present a cell space model based on the notion of Invasion-Succession cycle which was first used by the Chicago School (e.g., Burgess 1925) for explaining residential location and residential change. Within the model presented here, the agents move based on preferences akin to Schelling's segregation model (Schelling 1971). The results from the model show characteristics of already published demand and supply types of models (e.g., Diappi and Bolchi 2008; Jackson et al. 2008). Such a paper thus adds to the growing body gentrification models.
Moving from developed world problems to developing world problems, Lindner and Hill in their chapter entitled "Simulating informal urban development in Dar es Salaam, Tanzania - a CA-based simulation model for strategic and coordinated urban planning" use a CA approach to describe and explain the spatial impact of informal urban growth in Dar es Salaam, Tanzania. Informal settlements or slums are an interesting application for urban researchers as it represents a major challenge throughout the world. This is because slums provide shelter for nearly one third of the world's urban population (approximately 900 million people), a number that is projected to increase to 2 billion by 2030 if adequate actions are not taken (UN-Habitat 2003). The authors demonstrate how geosimulation appear to be a promising approach to forecast slum evolution and growth, along with the potential of geosimulation to act as decision support tools.
Keeping with the theme of land use change, Goetzke and Judex in chapter 5 explore land use change in the Northern Rhine-Wesphalia region of Germany,. The authors explore rural to urban land use change using two modelling approaches, one based on the CLUE-s statistical model (Verburg et al. 2002) and a second based on a CA model (based on the SLEUTH model of Clarke et al. 1997). Both models were implemented in the same XULU modelling framework (Schmitz et al. 2007). What is interesting about the paper is that the authors note that: i) both models result in different land use change patterns for the same area, ii) one model cannot lead to the definitive conclusion of what might happen in the future, and iii) one model is not better than the other, as the CA model only looks at urban growth (e.g. rural to urban land use change) while the CLUE-s model looks at a variety of land use changes. The chapter also has an extensive discussion of the calibration and validation of such models and acts as a good reference to the current state of the art in the field.
In the final chapter, West and Deschermeier take a different modelling strategy, that of spatial econometrics (Anselin 1998) to ask the question "Who is living where and why?" The model is based on empirical data about value-orientation and residential satisfaction of people living in German city of Mannheim and is used to assess residential needs and trends within the city.
Overall, the book is well written and referenced which allows the reader to delve deeper into any of the topics discussed. As the papers are longer than those of typical journal articles, it allows the authors to describe the literature and the models in more detail than is normally possible and presents a good survey into the state of the art for modelling and simulating a variety of urban processes.
ReferencesBENENSON, I and Torrens, PM (2004) Geosimulation: Automata-Based Modelling of Urban Phenomena. John Wiley & Sons, London, UK
BURGESS, EW (1925) 'The Growth of the City: An Introduction to a Research Project'. In Park, RE, Burgess, EW and McKenzie, RD (eds.), The City. The University of Chicago Press, Chicago, IL, pp. 47-62
CLARKE, KC, Hoppen, S and Gaydos, LJ (1997) A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area. Environment and Planning B, 24(2), pp. 247-261
DIAPPI, L and Bolchi, P (2008) Smith's Rent gap Theory and Local Real Estate Dynamics: A Multi-agent Model. Computers, Environment and Urban Systems, 32(1), pp. 6-18
GRIMM, V, Berger, U, Bastiansen, F, Eliassen, S, Ginot, V, Giske, J, Goss-Custard, J, Grand, T, Heinz, S, Huse, G, Huth, A, Jepsen, J, Jorgensen, C, Mooij, W, Muller, B, Pe'er, G, Piou, C, Railsback, S, Robbins, A, Robbins, M, Rossmanith, E, Ruger, N, Strand, E, Souissi, S, Stillman, R, Vabo, R, Visser, U and Deangelis, D (2006) A Standard Protocol for Describing Individual-Based and Agent-Based Models. Ecological Modelling, 198(1-2), pp. 115-126
JACKSON, J, Forest, B and Sengupta, R (2008) Agent-Based Simulation of Urban Residential Dynamics and Land Rent Change in a Gentrifying Area of Boston. Transactions in GIS, 12(4), pp. 475-491
SCHELLING, TC (1971) Dynamic Models of Segregation. Journal of Mathematical Sociology, 1(1), pp. 143-186
SCHMITZ, M, Bode, T, Thamm, H-P and Cremers, AB (2007) 'XULU - A generic JAVA-based platform to simulate land use and land cover change (LUCC)'. In Oxley, L and Kulasiri, D (eds.), Proceedings of MODSIM 2007 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, pp. 2645-2649
UN-HABITAT, (2003) The Challenge of Slums. United Nations Human Settlements Programme, Sterling, VA
VERBURG, PH, Soepboer, W, Veldkamp, A, Limpiada, R, Espaldon, V and Mastura, SSA (2002) Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model. Environmental Management, 30(3), pp. 391-405
WILSON, AG (2000) Complex Spatial Systems: The Modelling Foundations of Urban and Regional Analysis. Pearson Education, Harlow, UK
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