Comparison of Two Spatial Optimization Techniques: A Framework to Solve Multiobjective Land Use Distribution Problems |
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Authors: | Burghard Christian Meyer Jean-Marie Lescot Ramon Laplana |
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Institution: | 1.Dortmund University of Technology, School of Spatial Planning,Dortmund,Germany;2.Cemagref, Unité Aménités et Dynamiques des Espaces Ruraux (ADER),Cestas Cedex,France |
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Abstract: | Two spatial optimization approaches, developed from the opposing perspectives of ecological economics and landscape planning
and aimed at the definition of new distributions of farming systems and of land use elements, are compared and integrated
into a general framework. The first approach, applied to a small river catchment in southwestern France, uses SWAT (Soil and
Water Assessment Tool) and a weighted goal programming model in combination with a geographical information system (GIS) for
the determination of optimal farming system patterns, based on selected objective functions to minimize deviations from the
goals of reducing nitrogen and maintaining income. The second approach, demonstrated in a suburban landscape near Leipzig,
Germany, defines a GIS-based predictive habitat model for the search of unfragmented regions suitable for hare populations
(Lepus europaeus), followed by compromise optimization with the aim of planning a new habitat structure distribution for the hare. The multifunctional
problem is solved by the integration of the three landscape functions (“production of cereals,” “resistance to soil erosion
by water,” and “landscape water retention”). Through the comparison, we propose a framework for the definition of optimal
land use patterns based on optimization techniques. The framework includes the main aspects to solve land use distribution
problems with the aim of finding the optimal or best land use decisions. It integrates indicators, goals of spatial developments
and stakeholders, including weighting, and model tools for the prediction of objective functions and risk assessments. Methodological
limits of the uncertainty of data and model outcomes are stressed. The framework clarifies the use of optimization techniques
in spatial planning. |
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Keywords: | Multiobjective spatial decision making Objective function Goal programming Optimization Multifunctionality |
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