28 October 2006 Integrating GIS, cellular automata, and genetic algorithm in urban spatial optimization: a case study of Lanzhou
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Proceedings Volume 6420, Geoinformatics 2006: Geospatial Information Science; 64201U (2006) https://doi.org/10.1117/12.712954
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China
Abstract
This paper presents a model integrating GIS, cellular automata (CA) and genetic algorithm (GA) in urban spatial optimization. The model involves three objectives of the maximization of land-use efficiency, the maximization of urban spatial harmony and appropriate proportion of each land-use type. CA submodel is designed with standard Moore neighbor and three transition rules to maximize the land-use efficiency and urban spatial harmony, according to the land-use suitability and spatial harmony index. GA submodel is designed with four constraints and seven steps for the maximization of urban spatial harmony and appropriate proportion of each land-use type, including encoding, initializing, calculating fitness, selection, crossover, mutation and elitism. GIS is used to prepare for the input data sets for the model and perform spatial analysis on the results, while CA and GA are integrated to optimize urban spatial structure, programmed with Matlab 7 and coupled with GIS loosely. Lanzhou, a typical valley-basin city with fast urban development, is chosen as the case study. At the end, a detail analysis and evaluation of the spatial optimization with the model are made, and it proves to be a powerful tool in optimizing urban spatial structure and make supplement for urban planning and policy-makers.
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Xibao Xu, Xibao Xu, Jianming Zhang, Jianming Zhang, Xiaojian Zhou, Xiaojian Zhou, } "Integrating GIS, cellular automata, and genetic algorithm in urban spatial optimization: a case study of Lanzhou", Proc. SPIE 6420, Geoinformatics 2006: Geospatial Information Science, 64201U (28 October 2006); doi: 10.1117/12.712954; https://doi.org/10.1117/12.712954
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