基于POI大数据的城市零售商业空间布局与人口耦合关系研究——以上海市为例
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摘要
城市零售商业空间的合理布局是推动城市经济发展、满足居民消费需求的基础,商业空间与人口的耦合度高低是商业布局合理的主要因素之一.以上海市零售商业POI数据、人口数据等为基础,通过最邻近距离分析法、核密度估计法、Ripleys K函数空间聚类分析等方法分析了上海市零售商业空间布局特征,建立人口耦合度模型,分析各类商业与人口的耦合度.结果表明:①上海市各类商业空间均显示出明显的集聚特征;②各类商业的集聚区域有所不同,且中心与外围分异明显;③各类商业在不同尺度范围内集聚表现不同,在距离范围为10～12km时,大多数类型的商业空间呈现聚集状态,便利店、电子卖场区位空间选择尺度较大,特殊买卖场所区位选择尺度较小;④便利店的人口耦合度最高,超市的人口耦合度最低.
The rational layout of urban retail space is the basis of promoting urban economic development and satisfying residents'consumption demand,and the matching degree between commercial space and population is one of the main factors for the rational layout of urban retail space.Based on POI data and population data of retail business in Shanghai,this paper analyzes the spatial distribution characteristics of Shanghai retail business through the methods of nearest neighbor distance analysis,kernel density estimation and Ripley's K function spatial clustering analysis,establishes a population matching model,and analyzes the matching degree between various types of business and population.The results show that:(1)All kinds of commercial space in Shanghai show that agglomeration characteristics obviously.(2)The agglomeration areas of various types of commercial space are different,and the central and peripheral differences obviously.(3)The agglomeration performance of various types of commercial space is different in different scales,and most types of commercial space show agglomeration in the distance range of 10—12 km.(4)Convenience stores have the highest population matching degree and supermarkets have the lowest population matching degree.
The rational layout of urban retail space is the basis of promoting urban economic development and satisfying residents'consumption demand,and the matching degree between commercial space and population is one of the main factors for the rational layout of urban retail space.Based on POI data and population data of retail business in Shanghai,this paper analyzes the spatial distribution characteristics of Shanghai retail business through the methods of nearest neighbor distance analysis,kernel density estimation and Ripley's K function spatial clustering analysis,establishes a population matching model,and analyzes the matching degree between various types of business and population.The results show that:(1)All kinds of commercial space in Shanghai show that agglomeration characteristics obviously.(2)The agglomeration areas of various types of commercial space are different,and the central and peripheral differences obviously.(3)The agglomeration performance of various types of commercial space is different in different scales,and most types of commercial space show agglomeration in the distance range of 10—12 km.(4)Convenience stores have the highest population matching degree and supermarkets have the lowest population matching degree.
引文
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[24]戚伟,李颖,刘盛和,等.城市昼夜人口空间分布的估算及其特征---以北京市海淀区为例[J].地理学报,2013,68(10):1344-1356.
[2]REILLY W J.The lay of retail gravitation[M].New York:Knickers Blocker Press,1931.
[3]HUFF D L.Defining and estimating a trading area[J].The Journal of Marketing,1964,28(3):34-38.
[4]BURGER M J,MEIJERS E J,OORT F G V.Regional spatial structure and retail amenities in the netherlands[J].Regional Studies,2014,48(12):1972-1992.
[5]JACKSON P,AGUILA R P D,CLARKE I,et al.Retail restructuring and consumer choice 2.Understanding consumer choice at the household level[J].Environment&Planning A,2006,38(1):47-67.
[6]王士君,浩飞龙,姜丽丽.长春市大型商业网点的区位特征及其影响因素[J].地理学报,2015,70(6):893-905.
[7]郭玮玮.大连市大型零售商店空间分布研究[D].大连:东北财经大学,2012.
[8]王永超,王士君,李强.基于GIS空间统计的县级商业布局模式及形成机理研究---以吉林省乾安县城为例[J].经济地理,2011,31(9):1504-1510.
[9]张志斌,王凯佳.基于POI数据的兰州市商业网点及其空间结构[J].开发研究,2018(4):80-86.
[10]仵宗卿,戴学珍.北京市商业中心的空间结构研究[J].城市规划,2001,25(10):15-19.
[11]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.
[12]陈玉慧,陈剑健.城市传统商业中心内部结构及形成机制研究---以厦门市中山商圈为例[J].世界地理研究,2008,17(4):104-114.
[13]许学强,周素红,林耿.广州市大型零售商店布局分析[J].城市规划,2002,26(7):23-28.
[14]林耿,许学强.广州市商业业态空间形成机理[J].地理学报,2004,59(5):754-762.
[15]薛冰,肖骁,李京忠,姜璐,谢潇.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.
[16]赵向阳,杜洪涛,赵鹏.基于标准化规则的城市POI数据库建库研究---以济南市为例[J].城市勘测,2013(4):21-24.
[17]浩飞龙,王士君.长春市零售商业空间分布特征及形成机理[J].地理科学,2016,36(6):855-862.
[18]查小磊,彭鹏.基于POI数据试析长沙市零售商业空间格局[J].城市管理与科技,2018,20(4):58-61.
[19]王芳,高晓路.北京市商业空间格局及其与人口耦合关系研究[J].城市规划,2015,39(11):23-29.
[20]浩飞龙,王士君,冯章献,于婷婷,马丽.基于POI数据的长春市商业空间格局及行业分布[J].地理研究,2018,37(2):366-378.
[21]王海仓.赣州老城区商业空间布局研究[D].赣州:江西理工大学,2017.
[22]张珣,钟耳顺.2004-2008年北京城区商业网点空间分布与集聚特征[J].地理科学进展,2013,(8):1207-1215.
[23]张子民,周英,李琦,林俞先.城市局域动态人口估算方法与模拟应用[J].地球信息科学学报,2010,12(4):503-509.
[24]戚伟,李颖,刘盛和,等.城市昼夜人口空间分布的估算及其特征---以北京市海淀区为例[J].地理学报,2013,68(10):1344-1356.