Journal of Geographical Sciences ›› 2012, Vol. 22 ›› Issue (1): 179-191.doi: 10.1007/s11442-012-0920-7

• Land Use Change • Previous Articles    

Optimal land use allocation of urban fringe in Guangzhou

GONG Jianzhou1,2, LIU Yansui1, CHEN Wenli3   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China;
    3. Guangdong Party Institute of CCP, Guangdong Administration Institute, Guangzhou 510053, China
  • Received:2011-06-09 Revised:2011-08-25 Online:2012-02-15 Published:2011-12-26
  • Supported by:

    National Natural Science Foundation of China, No.41130748; No.41171070; China Postdoctoral Science Foundation, No.200902132; No.20080440511; The Humanities and Social Sciences Project of Ministry of Education, PRC, No.10YJCZH031


In response to the strong drive for social and economic development, local governments have implemented urban master plans, providing measures and timeframes to address the continuous demand for land and to alleviate urban problems. In this paper, a multi-objective model was constructed to discuss the problem, including economic benefits and ecological effectiveness, in terms of land use optimization. A genetic algorithm was then adopted to solve the model, and a performance evaluation and sensitivity analysis were conducted using Pareto optimality. Results showed that a set of tradeoffs could be acquired by the allocation of land use. In addition, the Pareto solutions proved the model to be efficient; for example, a limit of 13,500 ha of urban area conformed to plan recommendations. The reduction in crop land, orchard land, grassland, and unused land provided further efficiencies. These results implied that further potential regional land resources remain and that the urban master plan is able to support sustainable local development in the years to come, as well as verified that it is feasible to use land use allocation multi-objective modeling and genetic algorithms.

Key words: optimal allocation, land use, multi-objective modeling, genetic algorithms, fringe area