Peri-urbanization and Global Environmental Change

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Integrated Modelling

Last Updated: 2009-05-15

With the advancement of GIS technology, land use models having the abilities of simulating spatial and temporal land use change were emerged. Various land use models from different realms such as equation-based models, econometric models, empirical statistical models, and spatial system models, have been widely applied to simulate land use change, but they were often focused on growth of cities and regions. Agent-based models (ABM) and cellular automata (CA) have recently become the most widely used models to simulate land use change based on the state of a pixel, the conditions in the surrounding pixels, and a set of transition rules from a bottom-up perspective. However, the evolution of urban system depends not only on its pervious and surrounding states but also on driving forces such as energy inflows and imported goods and services which affect the change of land use. Ecological models based on general system theory have also been developed to take into account energy flows as driving force of the system (Huang et al., 2007; Lee et al., 2008). This project intends to integrate ABM and ecological modelling approach for the purpose of simulating the interaction among system components within the urban ecosystem and assess the resilience of urban system due to global environmental change and land use change in peri-urban area.


The ideal of agent-based models can be traced back to the theoretical machine capable of reproduction addressed by John von Neumann. After the improvement by Stanislaw Ulam and John Conway, CA and Game of Life, the predecessor of ABM, were developed (Sun and Cheng, 2000). Craig Reynold was the first person who applied ABM to model the reality of lively biological agents. Swarm, the first general purpose ABM systems, was developed by the Santa Fe Institute in Santa Fe, New Mexico, USA during late 1980s and early 1990s. Because of the serious impacts from human decision making on land use and land cover change, agent-based models were started to receive attention for simulating the actions and interactions of autonomous individuals in modelling land use change. Agent-based models consist of dynamically interacting rules based agents and simulate their interactions through the bottom-up approach (Castel and Crooks, 2006). The models can analyze effects of human decision on land use and interactions between them which were often ignored by other models (Ojima et al., 2005). The widely applied ABM software includes RePast, Ascape, StarLogo, MASON, FEARLUS and NetLogo. On the other hand, in order to model the complex dynamic interactions of water, environmental inputs and habitants, General Ecosystem Models (GEM) was developed by Costanza et al. (1995) with algorithms of ecosystem processes. GEM is a grid-based spatial dynamic model and can integrate human and environmental systems for simulating the effects of land use scenarios on ecosystem processes. GEM has been widely used in different fields such as habitat change, watershed management, species migration, etc. (Costanza and Voinov, 2004).




1.   Castle, C. J. E. and Crooks, A.T. (2006). UCL Working Papers Series Paper 110: Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations, London: Centre for Advanced Spatial Analysis, University College London.

2.   Costanza, R., Wainger, L., Bockstael, N. (1995). Integrated ecological economic systems modeling: theoretical issues and practical applications, In Integrating Economic and Ecological Indicators, edited by J. W. Milton and J. F. Shogren, pp. 45-66, London: Praeger.

3.   Costanza, R. and Voinov, A. (2004). Landscape Simulation Modeling- A Spatially Explicit, Dynamic Approach, New York: Springer.

4.   Huang, S. ˇVL., Kao, W. ˇVC., and Lee, C. ˇVL. (2007). Energetic mechanisms and development of an urban landscape system, Ecological Modelling, 201 (3-4): 495-506.

5.   Lee, C. ˇVL., Huang, S. ˇVL., and Chan, S. ˇVL. (2008). Biophysical and system approaches for simulating land-use change, Landscape and Urban Planning, 86: 187-203.

6.   Ojima, D., Moran, E., McConnell, W., Smith, M. S., Laumann, G., Morais, J., and Young, B. (2005). Global Land Project: Science Plan and Implementation Strategy, IGBP Report No.53/ IHDP Report No. 19, Stockholm: IGBP.

7.   Sun, Y. and Cheng, L. (2000). A survey on agent-based modelling and equation-Based modeling, Proceedings of Department of Computer Science (http://www.cs.gsu.edu/~csclicx/Csc8350/model.pdf), Georgia: Georgia State University.






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