Planning support systems (PSS) are information systems that support planning through problem diagnosis, data collection, mining, spatial and temporal analysis, data modeling, visualization, scenario building and projection, and collaborative decision making (Saarloos et al. 2008). PSS involve a wide range of tools that are as diverse as the steps of the planning process itself, from data selection and integration to public participation and negotiation to final policy compromises and implementation monitoring. There are a variety of models integrated within PSS:
Models that are based on geographic information systems (GIS) and spatial statistics. Some examples of GIS-based models used as PSS are: A "What-if system" (Klosterman 1999), the Planning System for Sustainable Development (PSSD) (Hansen 2001), the System for Planning and Research in Towns and Cities for Urban Sustainability (SPARTACUS) (Lautso 2002), and the Population and Land Use Model (PLUM) (WhatIf? 2011).
Models, such as cellular automata and agent-based models (ABMs), that address the spatiotemporal context of the land-use processes through complexity theory and GIS. Linking PSS with complex system models that are capable of handling the complex spatial and temporal dynamics of urban land-use change will increase their utility as tools for urban and land-use planners' decision making and policy evaluation (Torrens 2002, Zellner 2008). Complex system models, based on cellular automata or agent-based approaches, allow the generation of different land-use change scenarios and, therefore, facilitate the assessment and the implications of those scenarios. In PSS, ABMs are models that allow planners to visualize, analyze, and simulate dynamic phenomena emerging from the interaction of individual agents (Saarloos et al. 2008). One of the main advantages of ABMs is their ability to represent the complex and nonlinear interactions among individuals and actors that directly influence land-use change. PSS with ABMs make use of dynamic models that combine spatial processes and human decision making (Parker et al. 2003, Benenson 2004).
Saarloos et al. (2005) have developed a multiagent model for generating alternative land-use plans in which the agents are land-use experts who initiate the development of plan proposals and communicate with each other over time to draw up the proposals incrementally. Li and Liu (2008) have utilized an ABM as a spatial exploratory tool for generating alternative development patterns with sustainable development strategies. Kii and Doi (2005) have examined the effectiveness of policy measures aimed at achieving a compact city form by developing the MALUT agent-based model. Ligmann-Zielinska and Jankowski (2007) have applied the CommunityViz Policy Simulator to generate development scenarios to evaluate ABM's operational use in applied planning settings. Furthermore, Ligmann-Zielinska and Jankowski (2009) have experimented with a utility-based approach and an agent-based model to test different conceptions of risk-explicit decision making. The benefits and drawbacks of the integration of an agent-based model of land-use change and a groundwater flow numerical model have been explored by Zellner and Reeves (2010). They examined the multidimensional effects of land-use patterns and policy implications. Robinson and Brown (2009) have illustrated the use of a GIS-based ABM, called DEED, to produce results that describe the individual and interaction effects of minimum lot-size zoning and land-acquisition strategies on forest cover. Zellner et al. (2009) have employed an agent-based model to explore how underlying microbehaviors affect the payoffs of regional forested space and of local tax revenue obtained by two neighboring municipalities in a hypothetical exurban area. Although all these studies utilize ABMs for urban planning or urban policy evaluation, they are either theoretical (never implemented in real contexts) or they have not been fully integrated into a PSS framework. …