DOI:10.1068/b31073 A review of rural land-use planning models <s> Spatial simulation models <s> This paper presents a prototype of a simulation model based on cellular automata (CA), and multicriteria evaluation (MCE) and integrated with GIS. Specifically, a method, analytical hierarchy process (AHP), of MCE is used here to derive behaviour-oriented rules of transition in CA. A ‘tight' integration strategy is adopted, which means that the modules of MCE and CA are written in the C programming language and built within ARC/INFO GIS. Designed to run on a workstation Unix SimL and fully utilizes the graphical user interface (GUI),which allows the modelto be driven by menusand automate the simulation of land conversion in the urban-rural fringe. The combination of three elements, GIS, CA, and MCE, has several advantages: visualization of decision-making, easier access to spatial information, and the more realistic definition of transition rules in CA. <s> BIB001 </s> DOI:10.1068/b31073 A review of rural land-use planning models <s> Spatial simulation models <s> This paper describes a spatial planning model combining a multi-agent simulation (MAS) approach with cellular automata (CA). The model includes individual actor behaviour according to a bottom-up modelling concept. Spatial planning intentions and related decision making of planning actors is defined by agents. CA is used to infer the knowledge needed by the agents to make decisions about the future of a spatial organisation in a certain area. The innovative item of this approach offers a framework for modelling complex land use planning process by extending CA approach with MAS. The modelling approach is demonstrated by the implementation of a pilot model using JAVA and the SWARM agent modelling toolkit. The pilot model itself is applied to a study area near the city of Nijmegen, The Netherlands. <s> BIB002 </s> DOI:10.1068/b31073 A review of rural land-use planning models <s> Spatial simulation models <s> This paper seeks to evaluate the performance of genetic algorithms (GA) as an alternative procedure for generating optimal or near-optimal solutions for location problems. The specific problems considered are the uncapacitated and capacitated fixed charge problems, the maximum covering problem, and competitive location models. We compare the performance of the GA-based heuristics developed against well-known heuristics from the literature, using a test base of publicly available data sets. <s> BIB003 </s> DOI:10.1068/b31073 A review of rural land-use planning models <s> Spatial simulation models <s> Many resource allocation issues, such as land use- or irrigation planning, require input from extensive spatial databases and involve complex decisionmaking problems. Spatial decision support systems (SDSS) are designed to make these issues more transparent and to support the design and evaluation of resource allocation alternatives. Recent developments in this field focus on the design of allocation plans that utilise mathematical optimisation techniques. These techniques, often referred to as multi-criteria decision-making (MCDM) techniques, run into numerical problems when faced with the high dimensionality encountered in spatial applications. In this paper we demonstrate how simulated annealing, a heuristic algorithm, can be used to solve high-dimensional non-linear optimisation problems for multi-site land use allocation (MLUA) problems. The optimisation model both minimises development costs and maximises spatial compactness of the land use. Compactness is achieved by adding a non-linear neighbourhood objective to the objective function. The method is successfully applied to a case study in Galicia, Spain, using an SDSS for supporting the restoration of a former mining area with new land use. <s> BIB004 </s> DOI:10.1068/b31073 A review of rural land-use planning models <s> Spatial simulation models <s> Cellular automata, principal components analysis (PCA) and geographic information system techniques can be integrated to simulate alternative urban growth patterns based on a large set of environmental constraints that could be considered in land-use planning. The simulation of actual cities usually involves multicriteria evaluation in tackling the problems of complex spatial factors. Spatial factors often exhibit a high degree of correlation which is considered to be an undesirable property for multicriteria evaluation. It is difficult to determine the weights when many spatial variables are involved. This study uses PCA to remove data redundancy among a large set of spatial variables and determine the ideal point for land development. The simulation is based on transition rules that are related to the neighborhood function and similarity between cells and the ideal point. PCA helps to deal with a large data set of spatial variables for the implementation of the cellular automata model. The model can deal with the complicated resources conditions and environmental restrictions that are often encountered in land-use planning. The model can be further developed as an extended function of GIS to be a useful tool for urban planning and environmental management. <s> BIB005 </s> DOI:10.1068/b31073 A review of rural land-use planning models <s> Spatial simulation models <s> This paper presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decisionmaking, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions. <s> BIB006 </s> DOI:10.1068/b31073 A review of rural land-use planning models <s> Spatial simulation models <s> Scale issues have significant implications for the analysis of social and biophysical processes in complex systems. These same scale implications are likewise considerations for the design and application of models of landcover change. Scale issues have wide-ranging effects from the representativeness of data used to validate models to aggregation errors introduced in the model structure. This paper presents an analysis of how scale issues affect an agent-based model (ABM) of landcover change developed for a research area in the Midwest, USA. The research presented here explores how scale factors affect the design and application of agent-based landcover change models. The ABM is composed of a series of heterogeneous agents who make landuse decisions on a portfolio of cells in a raster-based programming environment. The model is calibrated using measures of fit derived from both spatial composition and spatial pattern metrics from multi-temporal landcover data interpreted from historical aerial photography. A model calibration process is used to find a best-fit set of parameter weights assigned to agents' preferences for different landuses (agriculture, pasture, timber production, and non-harvested forest). Previous research using this model has shown how a heterogeneous set of agents with differing preferences for a portfolio of landuses produces the best fit to landcover changes observed in the study area. The scale dependence of the model is explored by varying the resolution of the input data used to calibrate the model (observed landcover), ancillary datasets that affect land suitability (topography), and the resolution of the model landscape on which agents make decisions. To explore the impact of these scale relationships the model is run with input datasets constructed at the following spatial resolutions: 60, 90, 120, 150, 240, 300 and 480 m. The results show that the distribution of landuse-preference weights differs as a function of scale. In addition, with the gradient descent model fitting method used in this analysis the model was not able to converge to an acceptable fit at the 300 and 480 m spatial resolutions. This is a product of the ratio of the input cell resolution to the average parcel size in the landscape. This paper uses these findings to identify scale considerations in the design, development, validation and application of ABMs of landcover change. <s> BIB007 </s> DOI:10.1068/b31073 A review of rural land-use planning models <s> Spatial simulation models <s> Multi-agent Systems (MAS) offer a conceptual approach to include multi-actor decision making into models of land use change. The main goal is to explore the use of MAS to simulate spatial scenarios based on modelling multi-actor decision-making within a spatial planning process. We demonstrate MAS that consists of agents representing organizations and interest groups involved in an urban allocation problem during a land use planning process. The multi-actor based decision-making is modelled by generating beliefs and preferences of actors about the location of and relation between spatial objects. This allows each agent to confront these beliefs and preferences with it's own desires and with that of other agents. The MAS loosely resembles belief, desire and intentions architecture. Based on a case study for a hypothetical land use planning situation in a study area in the Netherlands we discuss the potential and limitations of the MAS to build models that enable spatial planners to include the 'actor factor' in their analysis and design of spatial scenarios. In addition, our experiments revealed the need for further research on the representation of spatial objects and reasoning, learning and communication about allocation problems using MAS. <s> BIB008
Spatial simulation models can apply different types of algorithms to land-use allocation.  proposed two genetic algorithms for land-use optimization that were incorporated into the land allocation decision support system, LADSS  . However, most of the applications of these algorithms to regional planning have been developed in the field of location-allocation models (see BIB003  . BIB004 used an optimization procedure based on simulated annealing to solve a land-use planning model, in which an equivalence between the energy function and the development costs of the land-use plan was established. Similarly,  and  used a variation of simulated annealing to optimize land-use allocation, in which the energy function for a land use was characterized by the carrying capacity, the environmental impact, and the cost of changing land use. Other simulation models use the concept of cellular automata (CA). Engelen et al (1999) integrated a GIS and a CA in order to evaluate different land-use scenarios. The development of simulation models based on CA is more common in urban planning (see  BIB005  . However, these models view the city as a set of land uses BIB001 and can be extrapolated to rural land-use planning. When the human factor is included in the spatial decisionmaking process, the agent-based models (ABM) stand out BIB006 . ABM simulate land-use scenarios on the basis of modeling individual and group decisionmaking and human^environment interactions. These models can use any of the aforementioned landuse-allocation procedures as decisionmaking methods by individual agents, such as CA BIB002 ) and mathematical programming  , or others, such as utility maximization BIB007 or belief^desire^intention architecture BIB008 .