Human-initiated land use change is the most significant factor behind the loss of agricultural and forested areas, thus global climate change. It is important to understand the reasons behind land use decisions as it is to understand their consequences. Empirical observations and controlled experimentation are not usually feasible methods for studying this change. Therefore, researchers have employed complex systems theory (or complexity theory) to help them understand and model dynamic land use change process in cities. Cellular automata (CA) theory and agent-based modeling have widely applied in land use change modelling. CA models can easily model spatial process that is changing over time, and can handle fine scale dynamics of these spatial processes. Agent-based models (ABMs) excel at relating the heterogeneous behaviour of agents with different information, different decision rules, and different situation to the macro behaviour of the overall system. While both have advantages, they have a number of challenges when applied to land use change. One of the aims of this dissertation is to develop novel modelling approaches that integrate geographic information systems (GIS), CA and ABMs with Bayesian Networks (BNs) for overcoming limitations in the modelling process by significantly reducing the tedious work in defining parameter values, transition rules and model structures. As the use of land use models in planning is not widely accepted and not trusted fully by the urban planners, the other aim is to link land use models with planning support systems (PSS), especially to use enhanced ABMs in PSS. Therefore, the proposed modelling approaches were applied to assist in understanding the patterns and controls of land use change both spatially and temporarily for the Metro Vancouver region. They were used to analyze the effects of planning decisions in accordance with the sustainable development point of view.
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