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Proceedings Paper

Integrating GIS, cellular automata, and genetic algorithm in urban spatial optimization: a case study of Lanzhou
Author(s): Xibao Xu; Jianming Zhang; Xiaojian Zhou
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Paper 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.

Paper Details

Date Published: 28 October 2006
PDF: 10 pages
Proc. SPIE 6420, Geoinformatics 2006: Geospatial Information Science, 64201U (28 October 2006); doi: 10.1117/12.712954
Show Author Affiliations
Xibao Xu, Lanzhou Univ. (China)
Nantong Univ. (China)
Jianming Zhang, Lanzhou Univ. (China)
Xiaojian Zhou, Nantong Univ. (China)

Published in SPIE Proceedings Vol. 6420:
Geoinformatics 2006: Geospatial Information Science
Jianya Gong; Jingxiong Zhang, Editor(s)

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