地理科学 ›› 2019, Vol. 39 ›› Issue (3): 442-449.doi: 10.13249/j.cnki.sgs.2019.03.010

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基于POI大数据的沈阳市住宅与零售业空间关联分析

薛冰1,2(), 肖骁1,2, 李京忠2,3, 谢潇1,2, 逯承鹏1,2, 任婉侠1,2   

  1. 1.中国科学院污染生态与环境工程重点实验室/沈阳应用生态研究所,辽宁 沈阳110016
    2.辽宁省环境计算与可持续发展重点实验室,辽宁 沈阳110016
    3.许昌学院城乡规划与园林学院,河南 许昌461000
  • 收稿日期:2017-12-22 修回日期:2018-03-11 出版日期:2019-03-10 发布日期:2019-03-10
  • 作者简介:

    作者简介:薛冰(1982-),男,江苏连云港人,研究员,主要从事人地关系研究。E-mail: xuebing@iae.ac.cn

  • 基金资助:
    国家自然科学基金项目(40471116, 41701142, 41701466)、辽宁省自然科学基金项目(201602743)、沈阳市科技局项目(17-117-6-00Z17-7-030)、中国科学院青年创新促进会项目(2016181,薛冰)资助

POI-based Spatial Correlation of the Residences and Retail Industry in Shenyang City

Bing Xue1,2(), Xiao Xiao1,2, Jingzhong Li2,3, Xiao Xie1,2, Chengpeng Lu1,2, Wanxia Ren1,2   

  1. 1. Key Lab of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, Liaoning, China
    2. Key Lab for Environmental Computation and Sustainability of Liaoning Province, Shenyang, 110016, Liaoning, China
    3.College of Urban Planning and Architecture, Xuchang University, Xuchang, 461000, Henan, China
  • Received:2017-12-22 Revised:2018-03-11 Online:2019-03-10 Published:2019-03-10
  • Supported by:
    National Natural Science Foundation of China (41471116,41701142,41701466), Sci & Tech Department of Liaoning Prov (201602743), Sci. & Tech. Department of Shenyang City(17-117-6-00, Z17-7-030), Youth Innovation Promotion Association CAS(Xue Bing, 2016181)

摘要:

城市住宅及其价格与区域商服业的空间关联性量化研究是人文-经济地理学的重要研究内容。以辽宁省沈阳市为案例,以住宅和零售业兴趣点(Point of Interest, POI)为数据源,基于空间核密度分析提取住宅和各类零售业的空间聚类形态,量化表达商住空间布局的相关性,并在此基础上运用地统计方法测算房价的空间异质性及其与零售业态空间布局的差异特征。结果表明,零售业的整体空间聚集特征与住宅相似,呈现中心城区块状聚集、外围城区多中心离散的分布格局;零售业与住宅核密度相关系数为0.95,超市、便利店等小规模的零售业与住宅密度相关性较强,商场商厦的聚集效应落后于城市住宅,大型零售业应该在铁西经济技术开发区等住宅密集区规划选址,为居民提供高端购物服务;住宅价格的倒“U”型空间分布模式与零售业空间密度的圈层衰减特征相符。

关键词: POI大数据, 城市住宅价格, 商住空间关联性, 沈阳市

Abstract:

It is a crucial research content of human-economic geography to quantitatively research the spatial correlation between urban residences with its price and the regional commercial service. Taking Shenyang City in Liaoning Province as a case study and using the residential and retail points of interest (POI) as a data source, this paper extracted the spatial clustering patterns of various residences based on the spatial kernel density analysis method, and then quantitatively expressed the correlation between commercial and residential spatial distribution. On this basis, this paper used the geo-statistical method to measure the spatial heterogeneity of houses prices and measured the impact of retail format layout on houses prices. Solutions presented in this research can be summarized as follows: The overall spatial aggregation characteristics of retailing are similar to that of dwellings, the distribution pattern of the central urban agglomeration and the multi-centers dispersion in the periphery city is presented. The correlation coefficient between retails’ density and residences’ density is 0.95. There is a strong correlation between residences, and some small-scale retails including supermarkets and convenience stores, the aggregation effect of shopping malls is lagging behind the urban dwellings. Large retail’ should be located in the Tiexi eco-technological development zone and other similar residential areas, in order to provide residents advanced shopping services. The inverted ‘U’ type spatial distribution model that houses prices presented is consistent with the attenuation characteristics of retail space’s density.

Key words: POI data, urban residential prices, correlation between commercial and residential space, Shenyang City

中图分类号: 

  • F129.9