Spatial Optimization
Spatial optimization is a powerful method to explore the potentials of a given area to improve the spatial coherence of land use functions. Within land use planning linear optimization methods are often not applicable because of the qualitative character of the relations and large number of variables and/or relations to be optimized.
Heuristic methods as Genetic Algorithms hardly have any restrictions regarding the formulation of the variables and their relations. An additional advantage of the Genetic Algorithms is that slightly different configurations can be found with almost the same characteristics in a relatively short time, which is an interesting feature for policy-makers. Policy makers may evaluate alternative land use configurations, each with their specific socio-economic impacts, while still achieving optimal results.
In order to restore and conserve the biodiversity, plans to create networks and enlarge existing nature areas have been initiated in Europe and more specifically in the Netherlands. Thousands of hectares of former agricultural land are purchased and converted into nature areas. These new nature areas have to be situated within designated areas, due to many constraints. The spatial coherence of the governmental plans for new nature are compared to the spatial coherence of the results of the spatial optimization. In this study the spatial coherence is defined as the total sum of boundary lengths of all nature areas. The goal of the optimization is therefore to minimize this total sum of boundary lengths, while increasing the total surface area. Theresults were compared in terms of boundary length and surface area in areas larger than 5000 hectares. Optimizing the allocation resulted in a large decline in boundary-length compared to the original plans and yielded more coherent areas. An interesting feature ofthe method used is its ability to optimize more than half a million variables, i.e. potential nature cells.
Currently no agenda available.