Journal of Guangxi Normal University(Natural Science Edition) ›› 2020, Vol. 38 ›› Issue (1): 26-40.doi: 10.16088/j.issn.1001-6600.2020.01.004

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Optimizing Spatial Distribution of Residential Areas by Using Multi-Source Open Data

ZHAO Xin1,2,3,4 , SONG Yingqiang 1,2,3,4, HU Yueming1,2,3,4,5,6*, LIU Yilun1,2,3,4, ZHU Axing7   

  1. 1. College of Natural Resources and Environment, South China Agricultural University, Guangzhou Guangdong 510642,China;
    2. Key Laboratory of Construction Land Improvement, Ministry of Land and Resources(South China AgriculturalUniversity), Guangzhou Guangdong 510642, China;
    3. Guangdong Province Key Laboratory for Land Use and Consolidation(South China Agricultural University), Guangzhou Guangdong 510642, China;
    4. Guangdong Province Engineering ResearchCenter for Land Information Technology(South China Agricultural University), Guangzhou Guangdong 510642, China;
    5. College of Agriculture and Animal Husbandry, Qinghai University, Xining Qinghai 810016, China;
    6. School ofResources and Environment, University of Electronic Science and Technology of China, Chengdu Sichuan 610054, China;
    7. Department of Geography, University of Wisconsin Madison, Madison WI53706, USA
  • Received:2019-02-25 Online:2020-01-25 Published:2020-01-15

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Abstract: Optimizing spatial distribution of residential areas is of great significance for intensive use of land resources, improvement of the present situation of spatial distribution of residential areas, and overall planning of urban and rural development. Taking the city of Guangzhou as the research area, this study used multi-source open data (POI and population spatial data) to replace the traditional socio-economic data, and analyzed the distribution characteristics of residential areas by using the method of landscape ecology. Aiming at the suitability and compact target, an ant colony optimization (ACO) model was constructed to optimize the spatial distribution of the residential areas in Guangzhou. The results showed that: (1) The layout of some residential areas in Guangzhou have some problems, such as scattered distribution and low concentration. (2)The result of spatial layout optimization of Guangzhou residential areas based on the new data set shows that the spatial scale is finer and the planning is timely. (3)After optimization, residential areas in Guangzhou mainly located in suitable areas, general suitable areas and basic suitable areas, accounting for 18%, 64% and 17% of the total residential area, respectively. By comparing the landscape index values before and after optimization, it was found that the mean patch size (MPS ) and mean nearest-neighbor distance (MNN) of the residential area increased significantly, and the patch density (PD) decreased, which indicated that the residential area layout tend to be constrictive and orderly.

Key words: residential area, open data, spatial layout optimization, landscape analysis, principal component analysis, ant colony optimization

CLC Number: 

  • TU984.2
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