基于POI大数据的沈阳市住宅与零售业空间关联分析
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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。
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 is0.95. 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.
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 is0.95. 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.
引文
[1]邹利林,杨俊,胡学东.中国城市住宅价格时空演变研究进展与展望[J].地理科学进展, 2013, 32(10):1479-1489.[Zou Lilin, Yang Jun, Hu Xuedong. Research on temporal-spatial changes of urban residential housing price in China:Progress and prospects. Progress in Geography, 2013, 32(10):1479-1489.]
[2]张文忠.城市居民住宅区位选择的因子分析[J].地理科学进展, 2001,20(3):267-274.[Zhang Wenzhong. An analysis of the factors that influence the urban residential location selection.Progress in Geography, 2001,20(3):267-274.]
[3]张文忠,刘旺,李业锦.北京城市内部居住空间分布与居民居住区位偏好[J].地理研究, 2003, 22(6):751-759.[Zhang Wenzhong, Liu Wang, Li Yejin. Housings’spatial distribution and residents'preference on housing location in Beijing. Geographical Research, 2003, 22(6):751-759.]
[4] Trojanek R,Glusza M,Tana?J. The effect of urban green spaces on house prices in Warsaw[J]. International Journal of Strategic Property Management, 2018, 22(5):358-371.
[5] Chugunov D, Polterovich V, Rubinshtein A. Impact of school quality and neighborhoods on housing prices in Moscow[J].Journal of the New Economic Association, 2013,17(1):87-112.
[6]瞿诗进,胡守庚,李全峰,等.城市住宅地价影响因素的定量识别与时空异质性——以武汉市为例[J].地理科学进展, 2018,37(10):1371-1380.[Qu Shijin, Hu Shougeng, Li Quanfeng et al. Quantitative evaluation of the impacts of driving factors on urban residential land price and analysis of their spatio-temporal heterogeneity:A case study of Wuhan City. Progress in Geography, 2018, 37(10):1371-1380.]
[7]吴丹贤,周素红.基于日常购物行为的广州社区居住-商业空间匹配关系[J].地理科学, 2017, 37(2):228-235.[Wu Danxian, Zhou Suhong. The matching pattern of housing-shopping space based on residents’daily shopping behaviors:A case study of communities in Guangzhou, China. Scientia Geographica Sinica, 2017, 37(2):228-235.]
[8]陈阳,岳文泽,高佳斌.城市商业综合体商圈划分及其对住宅的增值效应——以杭州主城区为例[J].经济地理, 2017, 37(11):68-75.[Chenyang, Yue Wenze, Gao Jiabin. Identifying commercial complex trade areas and its value-added impact on housing:The case of Hangzhou. Economic Geography, 2017, 37(11):68-75.]
[9]石忆邵,郭惠宁.上海南站对住宅价格影响的时空效应分析[J].地理学报, 2009, 64(2):167-176.[Shi Yishao, Guo Huining. Temporal-spatial impacts of the Shanghai south railway station on housing prices. Acta Geographica Sinica, 2009, 64(2):167-176.]
[10]熊昌盛,谭荣,岳文泽.快速城市化背景下不同建设用地扩张的驱动差异探讨:以浙江省义乌市为例[J].自然资源学报,2018,33(12):2124-2135.[Xiong Changsheng, Tan Rong, Yue Wenze. Discussion on different driving factors of diversified construction land expansion in rapid urbanization area:A case study of Yiwu City. Journal of Natural Resources, 2018, 33(12):2124-2135.]
[11]杨剩富,胡守庚,徐枫,等.特殊自然地物对城市住宅地价和房价的影响——以武汉市为例[J].资源科学,2016,38(4):738-749.[Yang Shengfu, Hu Shougeng, Xu Feng et al. Influence on urban residential land and housing prices by special natural features inWuhan. Resources Science, 2016, 38(4):738-749.]
[12]李志,周生路,张红富,等.基于GWR模型的南京市住宅地价影响因素及其边际价格作用研究[J].中国土地科学,2009, 23(10):20-25.[Li Zhi, Zhou Shenglu, Zhang Hongfu et al. Exploring the factors impacting on the residential land price and measuring their marginal effects based on geographically weighted regression model:A case study of Nanjing. China Land Science, 2009, 23(10):20-25.]
[13]郝前进,陈杰.到CBD距离-交通可达性与上海住宅价格的地理空间差异[J].世界经济文汇,2007(1):22-35.[Hao Qianjin,Chen Jie. Geographical spatial differences of distance to CBD,traffic accessibility and Shanghai Housing Price. World Economic Papers, 2007(1):22-35.]
[14] Sirpal R. Empirical modeling of the relative impacts of various sizes of shopping centers on the values of surrounding residential properties[J]. Journal of Real Estate Research,1994,9(4):487-506.
[15]庞瑞秋,赵梓渝,王唯,等.住房制度改革以来长春市新建住宅的空间布局研究[J].地理科学, 2013, 33(4):435-442.[Pang Ruiqiu, Zhao Ziyu, Wang Wei et al. The spatial layout of residence since the reform of housing system in Changchun. Scientia Geographica Sinica, 2013, 33(4):435-442.]
[16]吴文佳,张晓平,李媛芳.北京市景观可达性与住宅价格空间关联[J].地理科学进展,2014, 33(4):488-498.[Wu Wenjia, Zhang Xiaoping, Li Yuanfang.Spatial correlation analysis of landscape accessibility and residential housing price in Beijing.Progress in Geography,2014, 33(4):488-498.]
[17]曾文.转型期城市居民生活空间研究[D].南京:南京师范大学,2015.[Zeng Wen.Study on urban residents’living space during the transition period.Nanjing:Nanjing Normal University, 2015.]
[18]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.[Chen Weishan, Liu Lin, Liang Yutian.Retail center recognition and spatial aggregating feature analysis of retail formats in Guangzhou based on POI data. Geographical Research, 2016,35(4):703-716.]
[19]薛冰,肖骁,李京忠,等.基于POI大数据的城市零售业空间热点分析——以辽宁省沈阳市为例[J].经济地理, 2018, 38(5):36-43.[Xue Bing, Xiao Xiao, Li Jingzhong et al. POI-based analysis on retail’s spatial hot blocks at a city level:A case study of Shenyang, China. Economic Geography, 2018, 38(5):36-43.]
[20]孙宗耀,翟秀娟,孙希华,等.基于POI数据的生活设施空间分布及配套情况研究——以济南市内五区为例[J].地理信息世界, 2017, 24(1):65-70.[Sun Zongyao, Zhai Xiujuan,Sun Xihua et al. Study on spatial distribution and matching situation of living facilities based on POI—Taking five districts of Ji’nan as a case. Geomatics World, 2017, 24(1):65-70.]
[21]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,2015,44(1):82-90.[Yu Wenhao, Ai Tinghua. The visualization and analysis of POI features under network space supported by kernel density estimation. Acta Geodaetica et Cartographica Sinica, 2015, 44(1):82-90.]
[22]彭思伟.基于POI数据的南京商品房价格空间分布与驱动机制实证研究[J].建筑与文化,2017(3):64-68.[Peng Siwei.A study on the spatial distribution and dynamic characteristics of commercial housing price in Nanjing based on POI data. Architecture&Culture, 2017(3):64-68.]
[23] Lu Chengpeng, Xue Bing, Lu Chenyu et al. Sustainability investigation of resource-based cities in Northeastern China[J]. Sustainability, 2016, 8:1058.
[24] Can A. GIS and spatial analysis of housing and mortgage markets[J]. Journal of Housing Research, 1998, 99(1):61-86.
[25] Sanzana P, Gironas J,Braud I et al. A GIS-based urban and peri-urban landscape representation toolbox for hydrological distributed modeling[J]. Environmental Modelling and Software, 2017(91):168-185.
[26]任婉侠,耿涌,薛冰.中国老工业城市能源消费碳排放的驱动力分析——以沈阳市为例[J].应用生态学报,2012,23(10):2829-2835.[Ren Wanxia, Geng Yong, Xue Bing.Driving forces of carbon emission from energy consumption in China old industrial cities:A case study of Shenyang City, Northeast China.Chinese Journal of Applied Ecology, 2012, 23(10):2829-2835.]
[27]张黎明,薛冰,姜淼,等.老工业区城市功能的生态化路径评价——以沈阳为例[J].生态科学,2014,33(3):467-473.[Zhang Liming, Xue Bing, Jiang Miao et al. Ecologicalization route of urban function in old industrial area-case of Shenyang, China.Ecological Science, 2014, 33(3):467-473.]
[28]沃尔特·克里斯塔勒.德国南部的中心地原理[M].常正义译.北京:商务印书馆,1998.[Christallerr Walter. Die Zentralen Orte in Süddeutschland. Translated by Chang Zhengyi.Beijing:The Commercial Press, 1998.]
[29] Silverman B W. Density estimation for statistics and data analysis[M]. New York:Chapman and Hall,1986.
[30]范鑫.沈阳地区商业综合体的建筑设计研究[D].沈阳:沈阳建筑大学,2014.[Fan Xin.Commercial complex in Shenyang architectural design research. Shenyang:Shenyang Jianzhu University, 2014.]
[31]零售业态分类GB/T 18106-2004,[S].北京:中国标准出版社,2004.[Classification of Retail Formats(GB/T 18106-2004). Beijing:Standards Press of China, 2004.]
[32]李花,张志斌,王伟军.兰州市大中型超市的空间分布格局及其影响因素[J].经济地理,2016,36(9):85-93.[Li Hua, Zhang Zhibin, Wang Weijun. Spatial distribution and patterns and influential factors of large and medium supermarkets in Lanzhou. Economic Geography, 2016, 36(9):85-93.]
[33]李广娣,沈昊婧.城市住房价格的空间分布格局研究——以沈阳市为例[J].现代城市研究,2014(2):80-84.[Li Guangdi, Shen Haojing. Research on spatial distribution pattern of urban housing prices:A case study in Shenyang. Modern Urban Research,2014(2):80-84.]
[2]张文忠.城市居民住宅区位选择的因子分析[J].地理科学进展, 2001,20(3):267-274.[Zhang Wenzhong. An analysis of the factors that influence the urban residential location selection.Progress in Geography, 2001,20(3):267-274.]
[3]张文忠,刘旺,李业锦.北京城市内部居住空间分布与居民居住区位偏好[J].地理研究, 2003, 22(6):751-759.[Zhang Wenzhong, Liu Wang, Li Yejin. Housings’spatial distribution and residents'preference on housing location in Beijing. Geographical Research, 2003, 22(6):751-759.]
[4] Trojanek R,Glusza M,Tana?J. The effect of urban green spaces on house prices in Warsaw[J]. International Journal of Strategic Property Management, 2018, 22(5):358-371.
[5] Chugunov D, Polterovich V, Rubinshtein A. Impact of school quality and neighborhoods on housing prices in Moscow[J].Journal of the New Economic Association, 2013,17(1):87-112.
[6]瞿诗进,胡守庚,李全峰,等.城市住宅地价影响因素的定量识别与时空异质性——以武汉市为例[J].地理科学进展, 2018,37(10):1371-1380.[Qu Shijin, Hu Shougeng, Li Quanfeng et al. Quantitative evaluation of the impacts of driving factors on urban residential land price and analysis of their spatio-temporal heterogeneity:A case study of Wuhan City. Progress in Geography, 2018, 37(10):1371-1380.]
[7]吴丹贤,周素红.基于日常购物行为的广州社区居住-商业空间匹配关系[J].地理科学, 2017, 37(2):228-235.[Wu Danxian, Zhou Suhong. The matching pattern of housing-shopping space based on residents’daily shopping behaviors:A case study of communities in Guangzhou, China. Scientia Geographica Sinica, 2017, 37(2):228-235.]
[8]陈阳,岳文泽,高佳斌.城市商业综合体商圈划分及其对住宅的增值效应——以杭州主城区为例[J].经济地理, 2017, 37(11):68-75.[Chenyang, Yue Wenze, Gao Jiabin. Identifying commercial complex trade areas and its value-added impact on housing:The case of Hangzhou. Economic Geography, 2017, 37(11):68-75.]
[9]石忆邵,郭惠宁.上海南站对住宅价格影响的时空效应分析[J].地理学报, 2009, 64(2):167-176.[Shi Yishao, Guo Huining. Temporal-spatial impacts of the Shanghai south railway station on housing prices. Acta Geographica Sinica, 2009, 64(2):167-176.]
[10]熊昌盛,谭荣,岳文泽.快速城市化背景下不同建设用地扩张的驱动差异探讨:以浙江省义乌市为例[J].自然资源学报,2018,33(12):2124-2135.[Xiong Changsheng, Tan Rong, Yue Wenze. Discussion on different driving factors of diversified construction land expansion in rapid urbanization area:A case study of Yiwu City. Journal of Natural Resources, 2018, 33(12):2124-2135.]
[11]杨剩富,胡守庚,徐枫,等.特殊自然地物对城市住宅地价和房价的影响——以武汉市为例[J].资源科学,2016,38(4):738-749.[Yang Shengfu, Hu Shougeng, Xu Feng et al. Influence on urban residential land and housing prices by special natural features inWuhan. Resources Science, 2016, 38(4):738-749.]
[12]李志,周生路,张红富,等.基于GWR模型的南京市住宅地价影响因素及其边际价格作用研究[J].中国土地科学,2009, 23(10):20-25.[Li Zhi, Zhou Shenglu, Zhang Hongfu et al. Exploring the factors impacting on the residential land price and measuring their marginal effects based on geographically weighted regression model:A case study of Nanjing. China Land Science, 2009, 23(10):20-25.]
[13]郝前进,陈杰.到CBD距离-交通可达性与上海住宅价格的地理空间差异[J].世界经济文汇,2007(1):22-35.[Hao Qianjin,Chen Jie. Geographical spatial differences of distance to CBD,traffic accessibility and Shanghai Housing Price. World Economic Papers, 2007(1):22-35.]
[14] Sirpal R. Empirical modeling of the relative impacts of various sizes of shopping centers on the values of surrounding residential properties[J]. Journal of Real Estate Research,1994,9(4):487-506.
[15]庞瑞秋,赵梓渝,王唯,等.住房制度改革以来长春市新建住宅的空间布局研究[J].地理科学, 2013, 33(4):435-442.[Pang Ruiqiu, Zhao Ziyu, Wang Wei et al. The spatial layout of residence since the reform of housing system in Changchun. Scientia Geographica Sinica, 2013, 33(4):435-442.]
[16]吴文佳,张晓平,李媛芳.北京市景观可达性与住宅价格空间关联[J].地理科学进展,2014, 33(4):488-498.[Wu Wenjia, Zhang Xiaoping, Li Yuanfang.Spatial correlation analysis of landscape accessibility and residential housing price in Beijing.Progress in Geography,2014, 33(4):488-498.]
[17]曾文.转型期城市居民生活空间研究[D].南京:南京师范大学,2015.[Zeng Wen.Study on urban residents’living space during the transition period.Nanjing:Nanjing Normal University, 2015.]
[18]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.[Chen Weishan, Liu Lin, Liang Yutian.Retail center recognition and spatial aggregating feature analysis of retail formats in Guangzhou based on POI data. Geographical Research, 2016,35(4):703-716.]
[19]薛冰,肖骁,李京忠,等.基于POI大数据的城市零售业空间热点分析——以辽宁省沈阳市为例[J].经济地理, 2018, 38(5):36-43.[Xue Bing, Xiao Xiao, Li Jingzhong et al. POI-based analysis on retail’s spatial hot blocks at a city level:A case study of Shenyang, China. Economic Geography, 2018, 38(5):36-43.]
[20]孙宗耀,翟秀娟,孙希华,等.基于POI数据的生活设施空间分布及配套情况研究——以济南市内五区为例[J].地理信息世界, 2017, 24(1):65-70.[Sun Zongyao, Zhai Xiujuan,Sun Xihua et al. Study on spatial distribution and matching situation of living facilities based on POI—Taking five districts of Ji’nan as a case. Geomatics World, 2017, 24(1):65-70.]
[21]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,2015,44(1):82-90.[Yu Wenhao, Ai Tinghua. The visualization and analysis of POI features under network space supported by kernel density estimation. Acta Geodaetica et Cartographica Sinica, 2015, 44(1):82-90.]
[22]彭思伟.基于POI数据的南京商品房价格空间分布与驱动机制实证研究[J].建筑与文化,2017(3):64-68.[Peng Siwei.A study on the spatial distribution and dynamic characteristics of commercial housing price in Nanjing based on POI data. Architecture&Culture, 2017(3):64-68.]
[23] Lu Chengpeng, Xue Bing, Lu Chenyu et al. Sustainability investigation of resource-based cities in Northeastern China[J]. Sustainability, 2016, 8:1058.
[24] Can A. GIS and spatial analysis of housing and mortgage markets[J]. Journal of Housing Research, 1998, 99(1):61-86.
[25] Sanzana P, Gironas J,Braud I et al. A GIS-based urban and peri-urban landscape representation toolbox for hydrological distributed modeling[J]. Environmental Modelling and Software, 2017(91):168-185.
[26]任婉侠,耿涌,薛冰.中国老工业城市能源消费碳排放的驱动力分析——以沈阳市为例[J].应用生态学报,2012,23(10):2829-2835.[Ren Wanxia, Geng Yong, Xue Bing.Driving forces of carbon emission from energy consumption in China old industrial cities:A case study of Shenyang City, Northeast China.Chinese Journal of Applied Ecology, 2012, 23(10):2829-2835.]
[27]张黎明,薛冰,姜淼,等.老工业区城市功能的生态化路径评价——以沈阳为例[J].生态科学,2014,33(3):467-473.[Zhang Liming, Xue Bing, Jiang Miao et al. Ecologicalization route of urban function in old industrial area-case of Shenyang, China.Ecological Science, 2014, 33(3):467-473.]
[28]沃尔特·克里斯塔勒.德国南部的中心地原理[M].常正义译.北京:商务印书馆,1998.[Christallerr Walter. Die Zentralen Orte in Süddeutschland. Translated by Chang Zhengyi.Beijing:The Commercial Press, 1998.]
[29] Silverman B W. Density estimation for statistics and data analysis[M]. New York:Chapman and Hall,1986.
[30]范鑫.沈阳地区商业综合体的建筑设计研究[D].沈阳:沈阳建筑大学,2014.[Fan Xin.Commercial complex in Shenyang architectural design research. Shenyang:Shenyang Jianzhu University, 2014.]
[31]零售业态分类GB/T 18106-2004,[S].北京:中国标准出版社,2004.[Classification of Retail Formats(GB/T 18106-2004). Beijing:Standards Press of China, 2004.]
[32]李花,张志斌,王伟军.兰州市大中型超市的空间分布格局及其影响因素[J].经济地理,2016,36(9):85-93.[Li Hua, Zhang Zhibin, Wang Weijun. Spatial distribution and patterns and influential factors of large and medium supermarkets in Lanzhou. Economic Geography, 2016, 36(9):85-93.]
[33]李广娣,沈昊婧.城市住房价格的空间分布格局研究——以沈阳市为例[J].现代城市研究,2014(2):80-84.[Li Guangdi, Shen Haojing. Research on spatial distribution pattern of urban housing prices:A case study in Shenyang. Modern Urban Research,2014(2):80-84.]
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