Competitive Learning Approach to GIS Based Land Use Suitability Analysis |
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| Authors: | TELLEZ Ricardo Delgado WANG Shaohua ZHONG Ershun CAI Wenwen LONG Liang |
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| Institution: | 1. University of Chinese Academy of Sciences, Beijing 100049, China; 2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences; Beijing 100101, China; 3. SuperMap Software Co. Ltd., Beijing 100015, China |
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| Abstract: | This paper uses the expected utility under risk hypothesis to develop a new approach to GIS modeling for land use suitability analysis with competitive learning algorithms (CLG-LUSA). It uses Kohonen’s Self Organized Maps (SOM) and Linear Vector Quantization (LVQ) among other tools to create comprehensive ordering of high number of options. The model uses decision makers preferred locations and environmental data to construct a manifold of the decision’s attribute space. Then, decision and uncertainty maps are derived from this manifold. An application example is provided using the selection of suitable environments for coconut development in a municipality of Cuba. CLG-LUSA model was able to provide accurate visual feedback of key aspects of the decision process, making the methodology suitable for personal or group decision making. |
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| Keywords: | GIS land use suitability analysis linear vector quantization self organized maps |
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