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

Previous Articles     Next Articles

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

PDF (PC)

41

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
[1] 孙华生,黄敬峰,金艳,等. 基于GIS技术的县域居民点空间分布特征分析及其优化布局[J]. 浙江大学学报(农业与生命科学版),2007,33(3):348-354.
[2] 叶艳妹,张晓滨,林琼,等. 基于加权集覆盖模型的农村居民点空间布局优化:以流泗镇为例[J]. 经济地理,2017,37(5):140-148.
[3] 谢保鹏,朱道林,陈英,等. 基于区位条件分析的农村居民点整理模式选择[J]. 农业工程学报,2014(1):219-227.
[4] 周宁,郝晋珉,孟鹏,等. 黄淮海平原县域农村居民点布局优化及其整治策略[J]. 农业工程学报,2015,31(7):256-263.
[5] 文博,刘友兆,夏敏. 基于景观安全格局的农村居民点用地布局优化[J]. 农业工程学报,2014,30(8):181-191.
[6] 杨学龙,叶秀英,赵小敏,等. 鄱阳县农村居民点布局适宜性评价及其布局优化对策[J]. 中国农业大学学报,2015,20(1):245-255.
[7] 韦燕飞,陈燕奎,童新华,等. 基于改进PSO算法的农村居民点空间优化布局研究[J]. 地理与地理信息科学,2014,30(2):72-76.
[8] 高小永. 基于多目标蚁群算法的土地利用优化配置[D]. 武汉:武汉大学,2010.
[9] 莫致良. 基于蚁群算法的可扩展多目标土地利用优化配置[D]. 杭州:浙江大学,2017.
[10]王尧. 基于景观格局和蚁群算法的横山区农村居民点布局优化研究[D]. 西安:长安大学,2017.
[11]郑先迪,郑华伟,刘友兆. 基于GIS的农村居民点空间分布及影响因素分析:以盱眙县为例[J]. 土壤通报,2019(3):550-554.
[12]朱雪欣,王红梅,袁秀杰,等. 广东省佛冈县城乡居民点空间格局优化研究[J]. 中国土地科学,2009,23(8):51-57.
[13]孔雪松,金璐璐,郄昱,等. 基于点轴理论的农村居民点布局优化[J]. 农业工程学报,2014,30(8):192-200.
[14]杨昕,贺贤华,毛熙彦,等. 基于城乡联系的农村居民点布局优化研究:以重庆市为例[J]. 北京大学学报(自然科学版),2016,52(2):336-344.
[15]黄聪,赵小敏,郭熙,等. 基于核密度的余江县农村居民点布局优化研究[J]. 中国农业大学学报,2016,21(11):165-174.
[16]姜磊,雷国平,张健,等. 农村居民点空间布局及优化分析[J]. 水土保持研究,2013,20(1):224-229.
[17]刘仙桃. 农村居民点空间布局优化与集约用地模式研究:以北京市昌平区为例[D]. 北京:中国地质大学,2009.
[18]刘晓清,毕如田,高艳. 基于GIS的半山丘陵区农村居民点空间布局及优化分析:以山西省襄垣县为例[J]. 经济地理,2011,31(5):822-826.
[19]高文斌. 基于GIS的农村居民点布局优化研究:以靖江市马桥镇为例[D]. 南京:南京农业大学,2012.
[20]璩路路,李裕瑞,刘彦随. 基于村镇空间“物—场”模型的乡村聚落布局优化研究[J]. 经济地理,2019(4):174-181.
[21]倪冰洁. 基于引力模型的东海县农村居民点空间格局优化研究[D]. 南京:南京师范大学,2014.
[22]禹文豪,艾廷华. 核密度估计法支持下的网络空间POI点可视化与分析[J]. 测绘学报,2015(1):82-90.
[23]陈蔚珊,柳林,梁育填. 基于POI数据的广州零售商业中心热点识别与业态集聚特征分析[J]. 地理研究,2016,35(4):703-716.
[24]CHEN Y, LIU X , LI X , et al. Mapping the fine-scale spatial pattern of housing rent in the metropolitan area by using online rental listings and ensemble learning[J]. Applied Geography, 2016, 75:200-212.
[25]徐冬,黄震方,吕龙,等. 基于POI挖掘的城市休闲旅游空间特征研究:以南京为例[J]. 地理与地理信息科学,2018(1):59-64.
[26]王露露. 基于GIS空间分析的县域农村居民点布局优化研究:以平阳县为例[D]. 杭州:浙江大学,2013.
[27]刘颂,郭菲菲. 基于景观格局分析的乡村居民点布局优化研究[J]. 东北农业大学学报,2010,41(11):42-46.
[28]谭雪兰,段建南,包春红,等. 基于GIS的麻阳县农村居民点空间布局优化研究[J]. 水土保持研究,2010,17(6):177-180.
[29]徐新良. 中国人口空间分布公里网格数据集[EB/OL]. [2020-01-05].http://www.resdc.cn/DOI/DOI.aspx?DOIid=32.
[30]任若恩,王惠文. 多元统计数据分析:理论,方法,实例[M]. 北京:国防工业出版社,1997.
[31]邹亚锋. 多目标蚁群算法在农村居民点空间布局优化中的应用研究[M]. 北京:中国经济出版社,2017:83-85.
[32]LI X , CHEN Y , LIU X , et al. Concepts, methodologies, and tools of an integrated geographical simulation and optimization system[J]. International Journal of Geographical Information Science, 2011, 25(4):633-655.
[1] TANG Zhenjun. Image Hashing Algorithm Based on PCA Feature Distance [J]. Journal of Guangxi Normal University(Natural Science Edition), 2016, 34(4): 9-18.
[2] LIU Huimin, GUAN Dongjie, ZHANG Mengjie. Stress Factorsand Stress Mechanism on Subsequent Developmentof Ecological Security in the Three Gorges Reservoir Area [J]. Journal of Guangxi Normal University(Natural Science Edition), 2016, 34(3): 150-158.
[3] PU Ling, LI Hai-chao, PU Yu, JIANG Hong-xia. Principal Component Analysis of Trace Elements in 12 Commonly Used Chinese Herbal Medicines [J]. Journal of Guangxi Normal University(Natural Science Edition), 2014, 32(4): 96-100.
[4] HE Xi, LI Xu, ZHOU Wei, LI Qi-sheng. Morphologic Differentiation of Garra imberba from Different Population [J]. Journal of Guangxi Normal University(Natural Science Edition), 2013, 31(4): 128-133.
[5] YU Hong, LIU Man. Solving QoS-Aware Web Service Selection Problem with AND/OR Graph [J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(4): 45-48.
[6] WU Chao, ZHONG Yi-wen. Protein Function Prediction Using Ant Colony Optimization Algorithm [J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(3): 136-141.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] QIN Yingying, QI Guangchao, LIANG Shichu. Effects of Eichhornia crassipes Aqueous Extracts on Seed Germination of Ottelia acuminata var. jingxiensis[J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(3): 87 -92 .
[2] ZHANG Ru, ZHANG Bei, REN Hongrui. Spatio-temporal Dynamics Analysis and Its Affecting Factors of Cropland Loss in Xuangang Mining Area, Shanxi, China[J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(3): 121 -132 .
[3] WEN Yuzhuo, TANG Shengda, DENG Guohe. Analysis of the Ruin Time of Threshold Dividend Strategy Risk Model under Stochastic Environment[J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(3): 56 -62 .
[4] FENG Xiu, MA Nannan, ZHI Hongtao, HAN Shuangqiao, ZHANG Xiang. Removal of Low Concentration Cadmium Ion in the Wastewater by Heavy Metal Capturing Agent UDTC[J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(3): 63 -67 .
[5] WAN Lei,LUO Yuling,HUANG Xingyue. Monitoring Platform for the Hardware Spike Neural Networks[J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(1): 9 -16 .
[6] DENG Yabin,JIANG Pinqun,SONG Shuxiang. A Novel Low-voltage Micro-power Pseudo-differential CMOS OTA[J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(1): 17 -24 .
[7] HU Wenjun. Delay Consensus of Leader-following Multi-agent Systems via the Adaptive Distributed Control[J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(1): 70 -75 .
[8] ZHANG Junwen. Determination of Oleanic Acid and Uosolic Acid in Gentiana straminea Maxim. by MEKC[J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(1): 99 -104 .
[9] HU Xiaoxi. Interaction between Perfluorinated Amphiphilic Surfactant and Protein[J]. Journal of Guangxi Normal University(Natural Science Edition), 2018, 36(1): 105 -111 .
[10] XIE Rong, ZOU Yanli, FU Jie. Research on the Strategy of Fault Generator Recovery Based on the Dynamics in the Power Grid[J]. Journal of Guangxi Normal University(Natural Science Edition), 2017, 35(1): 1 -6 .
Copyright © Journal of Guangxi Normal University(Natural Science Edition), All Rights Reserved.
Tel: 0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
Powered by Beijing Magtech Co. Ltd