大数据视角下的商业中心和热点区分布特征及其影响因素分析——以乌鲁木齐主城区为例

doi:10.18306/dlkxjz.2020.05.004

地理科学进展 ›› 2020, Vol. 39 ›› Issue (5): 738-750.doi: 10.18306/dlkxjz.2020.05.004

大数据视角下的商业中心和热点区分布特征及其影响因素分析——以乌鲁木齐主城区为例

陈洪星^1,², 杨德刚^1,^*(), 李江月^1,², 武荣伟^1,², 霍金炜¹

^1. 中国科学院新疆生态与地理研究所,荒漠与绿洲生态国家重点实验室,乌鲁木齐 830011
^2. 中国科学院大学,北京100049

收稿日期:2019-04-26 修回日期:2019-07-26 出版日期:2020-05-28 发布日期:2020-07-28
通讯作者: 杨德刚
作者简介:陈洪星(1994— ),男,汉族,山东德州人,硕士生,主要从事城市地理研究。E-mail:chenhongxing17@mails.ucas.ac.cn
基金资助:
资源与环境信息系统国家重点实验室开放基金(201619)

Distribution characteristics and influencing factors of commercial center and hotspots based on big data: A case of the main urban area of Urumqi City

CHEN Hongxing^1,², YANG Degang^1,^*(), LI Jiangyue^1,², WU Rongwei^1,², HUO Jinwei¹

^1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China
^2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2019-04-26 Revised:2019-07-26 Online:2020-05-28 Published:2020-07-28
Contact: YANG Degang
Supported by:
2016 Open Foundation of State Key Laboratory of Resources and Environmental Information System(201619)

摘要/Abstract

摘要：

商业空间结构是城市经济活动的重要载体,识别商业中心和商业热点区以及探究其影响因素对于商业资源优化配置显得尤为必要,进而指导城市有序发展。论文以乌鲁木齐主城区为例,利用开放平台大数据兴趣点(point of interest, POI),采用地理学空间统计方法定量识别商业中心和商业热点区,对商业分布和空间集聚特征进行分类和解读,并利用地理探测器方法探寻其影响因素。主要结论如下：① 乌鲁木齐市商业高值区分布在吐乌大高速—和平渠沿线地带,大型商业中心主要有南湖商圈、中山路商圈、友好商圈、会展商圈、米东商圈、铁路局商圈。② 商业热点区呈现“T型”双轴分布,北部新城商业地带与南部传统商业地带共同构成乌鲁木齐市最具活力的商业地带;6类商业热点区的分布可归纳为3种类型,商务和金融服务类为单一点状型,住宿和餐饮服务类为带状延伸型,生活与购物服务类为带状双核型。③地价、集聚效应、路网密度等是影响商业宏观分布的主要因素,其次为人口密度和中心可达性,自然因素如高程、地形起伏度等对商业布局影响有限;各因素对不同类型商业业态的影响程度各异,如人口密度、路网密度对购物类影响较大,中心可达性和地价对于商务、金融类影响较大;就各业态类型网点间的关系而言,商务和金融类协同作用强,餐饮与购物类协同效应较强,共同影响城市商业空间。

关键词: 大数据, 商业中心, 热点区, 地理探测器, 乌鲁木齐主城区

Abstract:

The structure of commercial space is vital to the vitality of cities, therefore it is essential to quantitatively identify and analyze the distribution of different types of commercial sites so as to optimize the configuration of commercial resources and facilitate the orderly development of cities. Taking the main urban area of Urumqi City as the case study area, using 136975 business-related points of interest (POIs) including six types of businesses in 2018 and open street map (OSM) road network, and based on head/tail division rule, this study identified high-density commercial parcels and used kernel density estimation to estimate the core region of business activities. The Getis-Ord G ^* method was used to identify the overall and different types of commercial hot spot areas. Geographic detector analysis was performed to explore the determinants of overall and different types of commercial site distribution in Urumqi, and Pearson correlation coefficient matrix of commercial sites was established to estimate the impact of the combination and coordination of business forms on commercial space. The findings of this study suggest that the key features of high-density commercial parcel distribution are central-peripheral, separated by highways and internal loops; the number of high value parcels from the center to the peripheral area reduces progressively; and the distribution of the six types of commercial sites varies. Commercial zone presents multi-core distribution characteristics, the agglomeration characteristic is apparent in the urban center region, and the northern commercial agglomeration is gradually becoming obvious. There are six main commercial centers including Nanhu, Zhongshan Road, Youhao, Huizhan Center, Midong New Area, and Tieluju. Tuwu Expressway and Wukui Expressway together constitute the two axes of commercial hotspots. Hotspots of the six types of commercial sites can be divided into three spatial structures. Business and finance show a single-center distribution trend; accommodation and food & restraurant are of banded extension type; while services and shopping spots are of banded dual-core type. The primary determinants of the spatial distribution of commercial sites are: land price, agglomeration effect, and road network density. The influence of population and central accessibility is secondary; elevation has no significant effect. In particular, for business and financial services, land price and center accessibility are the main factors affecting the distribution. Accommodation and food & restraurant are affected by road network density. Shopping and services are significantly affected by population density. Business and finance sites, food & restraurant and shopping sites all have strong synergistic effects on the formation of urban commercial space, while others are not significance.

Key words: big data, commercial center, commercial hotspot region, geodetector method, main urban area of Urumqi City

陈洪星, 杨德刚, 李江月, 武荣伟, 霍金炜. 大数据视角下的商业中心和热点区分布特征及其影响因素分析——以乌鲁木齐主城区为例[J]. 地理科学进展, 2020, 39(5): 738-750.

CHEN Hongxing, YANG Degang, LI Jiangyue, WU Rongwei, HUO Jinwei. Distribution characteristics and influencing factors of commercial center and hotspots based on big data: A case of the main urban area of Urumqi City[J]. PROGRESS IN GEOGRAPHY, 2020, 39(5): 738-750.

图/表 11

图1

表1

图2

图3

表2

图4

图5

图6

表3

表4

表5

参考文献 37

[1]	阎小培, 周春山, 冷勇 , 等. 广州CBD的功能特征与空间结构 [J]. 地理学报, 2000,67(4):475-486.
	[ Yan Xiaopei, Zhou Chunshan, Leng Yong , et al. Functional features and spatial structure of CBDs in Guangzhou. Acta Geographica Sinica, 2000,67(4):475-486. ]
[2]	Berry B J L . Commercial structure and commercial blight: Retail patterns and processes in the city of Chicago [R]. University of Chicago, Department of Geography, Research Paper No. 85. Chicago, USA, 1963.
[3]	Kohsaka H . An optimization of the central place system in terms of multipurpose shopping trip [J]. Geographical Analysis, 2010,16(3):250-269.
[4]	吴郁文, 谢彬, 骆慈广 , 等. 广州市城区零售商业企业区位布局的探讨 [J]. 地理科学, 1988,8(3):208-217.
	[ Wu Yuwen, Xie Bin, Luo Ciguang , et al. An approach to retailcommerce location of Guangzhou urban area. Scientia Geographica Sinica, 1988,8(3):208-217. ]
[5]	杨吾扬 . 北京市零售商业与服务业中心和网点的过去、现在和未来 [J]. 地理学报, 1994,49(1):9-17.
	[ Yang Wuyang . The retailing and services center and network ofBeijing: Then, now and long before. Acta Geographica Sinica, 1994,49(1):9-17. ]
[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.
	[ Wang Shijun , HaoFeilong, Jiang Lili. Locations and theirdeterminants of large-scale commercial sites in Changchun, China. Acta Geographica Sinica, 2015,70(6):893-905. ]
[7]	宁越敏 . 上海市区商业中心区位的探讨 [J]. 地理学报, 1984,39(2):163-172.
	[ NingYuemin. An approach to shopping center location of Shanghai's urban area. Acta Geographica Sinica, 1984,39(2):163-172. ]
[8]	周素红, 郝新华, 柳林 . 多中心化下的城市商业中心空间吸引衰减率验证: 深圳市浮动车 GPS 时空数据挖掘 [J]. 地理学报, 2014,69(12):1810-1820.
	[ Zhou Suhong, Hao Xinhua, Liu Lin . Validation of spatial decay law caused by urban commercial center's mutual attraction in polycentriccity: Spatio-temporal data mining of floating cars' GPS data in Shenzhen. Acta Geographica Sinica, 2014,69(12):1810-1820. ]
[9]	吴康敏, 张虹鸥, 王洋 , 等. 广州市多类型商业中心识别与空间模式 [J]. 地理科学进展, 2016,35(8):963-974.
	[ Wu Kangmin, Zhang Hongou, Wang Yang , et al. Identify of the multiple types of commercial center in Guangzhou and its spatial pattern. Progress in Geography, 2016,35(8):963-974. ]
[10]	Goodchild M F . Citizens as sensors: The world of volunteered geography [J]. GeoJournal, 2007,69(4):211-221.
[11]	杨振山, 龙瀛 , Douay N. 大数据对人文-经济地理学研究的促进与局限 [J]. 地理科学进展, 2015,34(4):410-417.
	[ Yang Zhenshan, Long Ying, Douay N . Opportunities and limitations of big data applications to human and economic geography: The state of the art. Progress in Geography, 2015,34(4):410-417. ]
[12]	Liu Y, Liu X, Gao S , et al. Social sensing: A new approach to understanding our socioeconomic environments [J]. Annals of the Association of American Geographers, 2015,105(3):512-530.
[13]	Lloyd A, Cheshire J . Deriving retail centre locations and catchments from geo-tagged Twitter data [J]. Computers Environment & Urban Systems, 2017,61:108-118.
[14]	王晓梦, 王锦, 朱青 . 基于签到数据的城市商业空间空心化识别研究: 以北京市城六区为例 [J]. 城市发展研究, 2018,25(2):77-84.
	[ Wang Xiaomeng, Wang Jin, Zhu Qing . Identification of following phenomenon in commercial space of six district of Beijing based on checking-in data. Urban Development Studies, 2018,25(2):77-84. ]
[15]	Ting C Y, Ho C C, Yee H J , et al. Geospatial analytics in retail site selection and sales prediction [J]. Big Data, 2018,6(1):42-52.
[16]	王芳, 牛方曲, 王志强 . 微观尺度下基于商圈的北京市商业空间结构优化 [J]. 地理研究, 2017,36(9):1697-1708.
	[ Wang Fang, Niu Fangqu, Wang Zhiqiang . Commercial spatial structure optimization based on trade areaanalysis from a micro-scale perspective in Beijing. Geographical Research, 2017,36(9):1697-1708. ]
[17]	浩飞龙, 王士君, 冯章献 , 等. 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.
	[ Hao Feilong, Wang Shijun, Feng Zhangxian , et al. Spatial pattern and its industrial distribution of commercialspace in Changchun based on POI data. Geographical Research, 2018,37(2):366-378. ]
[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 centerrecognition 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]	陆娟, 汤国安, 张宏 , 等. 犯罪热点时空分布研究方法综述 [J]. 地理科学进展, 2012,31(4):419-425.
	[ Lu Juan, Tang Guo'an, Zhang Hong, et al. A review of research methods for spatiotemporal distribution of the crime hot spots. Progressin Geography, 2012,31(4):419-425. ]
[21]	薛东前, 黄晶, 马蓓蓓 , 等. 西安市文化娱乐业的空间格局及热点区模式研究 [J]. 地理学报, 2014,69(4):541-552.
	[ Xue Dongqian, Huang Jing, Ma Beibei , et al. Spatial distribution characteristics and hot zone patterns of entertainmentindustry in Xi'an. Acta Geographica Sinica, 2014,69(4):541-552. ]
[22]	Chen X, Xu F C, Wang W L , et al. Geographic big data's applications in retailing business market [M] // Shen Z J, Li M Y, Big data support of urban planning and management. Berlin, Germany: Springer, 2018: 157-176.
[23]	Lin G, Chen X X, Liang Y T . The location of retail stores and street centrality in Guangzhou, China [J]. Applied Geography, 2018,100:12-20.
[24]	傅辰昊, 周素红, 闫小培 , 等. 广州市零售商业中心的居民消费时空行为及其机制 [J]. 地理学报, 2017,72(4):603-617.
	[ Fu Chenhao, Zhou Suhong, Yan Xiaopei , et al. Spatio-temporalcharacteristics and influencing factors of consumer behavior inretailing centers: A case study of Guangzhou in GuangdongProvince. Acta Geographica Sinica, 2017,72(4):603-617. ]
[25]	赵宏波, 余涤非, 苗长虹 , 等. 基于POI数据的郑州市文化设施的区位布局特征与影响因素研究 [J]. 地理科学, 2018,38(9):1525-1534.
	[ Zhao Hongbo, Yu Difei, Miao Changhong , et al. The location distribution characteristics and influencing factors of cultural facilities in Zhengzhou based on POI data. Scientia Geographica Sinica, 2018,38(9):1525-1534. ]
[26]	Long Y, Liu X . Automated identification and characterization of parcels (AICP) with open street map and points of interest [J]. Environment & Planning B, 2015,43(2):498-510.
[27]	李德华 . 城市规划原理 [M]. 第3版. 北京: 中国建筑工业出版社, 2001.
	[ Li Dehua. Principles of urban planning. 3nd ed. Beijing: China Architecture & Building Press. 2001. ]
[28]	Hu T Y, Yang J, Li X C , et al. Mapping urban land use by using Landsat images and open social data [J]. Remote Sensing, 2016,8(2):151-169.
[29]	Jiang B . Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution [J]. Professional Geographer, 2013,65(3):482-494.
[30]	Jiang B, Yin J . Ht-index for quantifying the fractal or scaling structure of geographic features [J]. Annals of the Association of American Geographers, 2014,104(3):530-540.
[31]	焦利民, 李泽慧, 许刚 , 等. 武汉市城市空间集聚要素的分布特征与模式 [J]. 地理学报, 2017,72(8):1432-1443.
	[ Jiao Limin, Li Zehui, Xu Gang , et al. Distribution characteristics and models of urban agglomeration factors in Wuhan. Acta Geographica Sinica, 2017,72(8):1432-1443. ]
[32]	韩会然, 杨成凤, 宋金平 . 北京批发企业空间格局演化与区位选择因素 [J]. 地理学报, 2018,73(2):219-231.
	[ Han Huiran, Yang Chengfeng, Song Jinping . Impact factors of location choice and spatial pattern evolution ofwholesale enterprises in Beijing. Acta Geographica Sinica, 2018,73(2):219-231. ]
[33]	王劲峰, 徐成东 . 地理探测器: 原理与展望 [J]. 地理学报, 2017,72(1):116-134.
	[ Wang Jinfeng , Xu Cheng dong. Geodetector: Principle and prospective. Acta Geographica Sinica, 2017,72(1):116-134. ]
[34]	曹芳洁, 邢汉发, 侯东阳 , 等. 基于POI数据的北京市商业中心识别与空间格局探究 [J]. 地理信息世界, 2019,26(1):66-71.
	[ Cao Fangjie, Xing Hanfa, Hou Dongyang , et al. Research on identification and spatial patterns of commercial centers in Beijing based on POI data. Geomatics World, 2019,26(1):66-71. ]
[35]	刘晓倩 . 成都市城市商业空间发展研究[D]. 成都: 西南交通大学, 2008.
	[ Liu Xiaoqian . Reaserch on the development of urban commercial space in Chengdu. Chengdu, China: Southwest Jiaotong University, 2008. ]
[36]	安成谋 . 兰州市商业中心的区位格局及优势度分析 [J]. 地理研究, 1990,9(1):28-34.
	[ An Chengmou . Analysis of Lanzhou commercial center's location pattern and advantage degree. Geographical Research, 1990,9(1):28-34. ]
[37]	张利, 雷军, 张小雷 , 等. 乌鲁木齐城市社会区分析 [J]. 地理学报, 2012,67(6):817-828.
	[ Zhang Li, Lei Jun, Zhang Xiaolei , et al. Analysis of the urban social areas in Urumqi. Acta Geographica Sinica, 2012,67(6):817-828. ]

主类	亚类	占比/%
餐饮服务	糕饼店、外国餐厅、快餐店、冷饮店、甜品店、餐饮相关场所、饮茶馆、咖啡厅、茶艺馆	31.58
购物服务	便民商店、家居建材市场、特色商业街、服装鞋帽皮具店、专卖店、文化用品店、超市、购物相关场所、家电电子卖场、体育用品店、商场、花鸟鱼虫市场、综合市场、特殊买卖场所	38.22
住宿服务	宾馆、酒店、旅馆、招待所、住宿服务相关场所	2.86
金融保险服务	保险公司、财务公司、银行、ATM、证券公司、金融保险服务机构	1.76
商务服务	商务写字楼、商住两用楼宇	1.81
生活服务	旅行社、美容店、摄影冲印店、事务所、售票处、物流速递点、洗衣店、洗浴推拿、中介机构、药店、诊所、驾校、培训机构	23.76

类型	街区单元数量	单元密度均值	头部单元数量	头部单元占比/%
商务服务	4138	0.58	190	4.59
	190	12.62	63	33.15
	63	25.30	24	38.09
	24	37.93	10	41.00
住宿服务	4138	6.50	555	13.41
	555	47.60	173	31.17
	173	68.54	113	65.00
餐饮服务	4138	68.95	680	16.43
	680	397.94	232	34.11
	232	809.86	85	36.48
	85	1299.73	36	42.35
购物服务	4138	83.05	662	16.00
	662	489.71	205	30.96
	205	1091.94	78	38.04
金融服务	4138	4.10	541	13.07
	541	31.03	160	29.57
	160	73.59	52	32.50
	52	134.36	19	36.50
	19	180.22	8	42.10
生活服务	4138	39.96	670	16.19
	670	232.95	223	33.28
	223	471.24	102	45.74

变量属性	变量	探测因子	计算方法
自然因素	高程	X₁	街道单元内各高程栅格数据的均值作为街道的高程属性
	地形起伏度	X₂	街道单元的高程栅格数据的最高和最低值之差
社会因素	人口密度	X₃	街道单元人口数量/街道面积
	路网密度	X₄	街道单元路网长度/街道面积
	中心可达性	X₅	距离市政府所在地的欧氏距离
经济因素	地价	X₆	街道单元土地基准价格
	集聚效应	X₇	距离大型商圈的最近欧氏距离

探测因子	整体	商务服务	住宿服务	餐饮服务	金融服务	生活服务	购物服务
高程X₁	0.03^**	0.03	0.05	0.06	0.01	0.04	0.02
地形起伏度X₂	0.23^*	0.07^*	0.11	0.30	0.04	0.15	0.19
人口密度X₃	0.37^**	0.41	0.38	0.45^*	0.31	0.62^***	0.66^**
路网密度X₄	0.39^**	0.17	0.31	0.29^*	0.08	0.44^**	0.29
中心可达性X₅	0.28^**	0.67^***	0.12	0.16	0.33^***	0.07	0.22
地价X₆	0.50^*	0.53	0.44	0.21	0.55^**	0.21	0.34
集聚效应X₇	0.47^***	0.36	0.09	0.16^**	0.25	0.14	0.44^**

商业业态	餐饮服务	住宿服务	商务服务	购物服务	金融服务	生活服务
餐饮服务	1.000	0.608^***	0.474^***	0.822^***	0.565^***	0.842^***
住宿服务		1.000	0.457^***	0.527^***	0.430^***	0.604^***
商务服务			1.000	0.420^***	0.695^***	0.513^***
购物服务				1.000	0.558^***	0.777^***
金融服务					1.000	0.606^***
生活服务						1.000

大数据视角下的商业中心和热点区分布特征及其影响因素分析——以乌鲁木齐主城区为例

Distribution characteristics and influencing factors of commercial center and hotspots based on big data: A case of the main urban area of Urumqi City

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 37

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	杨延杰, 尹丹, 刘紫玟, 黄庆旭, 何春阳, 吴康. 基于大数据的流空间研究进展[J]. 地理科学进展, 2020, 39(8): 1397-1411.
[2]	刘骁啸, 吴康. 功能疏解背景下京津冀中部核心区产业投资网络演化研究[J]. 地理科学进展, 2020, 39(12): 1972-1984.
[3]	韩静, 芮旸, 杨坤, 刘薇, 马滕. 基于地理探测器和GWR模型的中国重点镇布局定量归因[J]. 地理科学进展, 2020, 39(10): 1687-1697.
[4]	杨俊, 由浩琳, 张育庆, 金翠. 从传统数据到大数据+的人居环境研究进展[J]. 地理科学进展, 2020, 39(1): 166-176.
[5]	杨忍,罗秀丽,陈燕纯. 中国县域乡村地域多功能格局及影响因素识别[J]. 地理科学进展, 2019, 38(9): 1316-1328.
[6]	马明清, 袁武, 葛全胜, 袁文, 杨林生, 李汉青, 李萌. “一带一路”若干区域社会发展态势大数据分析[J]. 地理科学进展, 2019, 38(7): 1009-1020.
[7]	申犁帆, 张纯, 李赫, 王烨, 王子甲. 城市轨道交通通勤与职住平衡状况的关系研究——基于大数据方法的北京实证分析[J]. 地理科学进展, 2019, 38(6): 791-806.
[8]	杨喜平, 方志祥. 移动定位大数据视角下的人群移动模式及城市空间结构研究进展[J]. 地理科学进展, 2018, 37(7): 880-889.
[9]	曹峥, 吴志峰, 马文军. 人为热排放对不同类型建成区温度影响的模拟研究[J]. 地理科学进展, 2018, 37(4): 515-524.
[10]	黄洁, 王姣娥, 靳海涛, 金凤君. 北京市地铁客流的时空分布格局及特征——基于智能交通卡数据[J]. 地理科学进展, 2018, 37(3): 397-406.
[11]	关雪峰, 曾宇媚. 时空大数据背景下并行数据处理分析挖掘的进展及趋势[J]. 地理科学进展, 2018, 37(10): 1314-1327.
[12]	王娟, 孟斌, 张景秋, 李琛, 邹柏贤. 感知技术在文化遗产研究中的应用与展望[J]. 地理科学进展, 2017, 36(9): 1092-1098.
[13]	杨微石, 郭旦怀, 逯燕玲, 王德强, 朱映秋, 张宝秀. 基于大数据的文化遗产认知分析方法——以北京旧城中轴线为例[J]. 地理科学进展, 2017, 36(9): 1111-1118.
[14]	谢永俊, 彭霞, 黄舟, 刘瑜. 基于微博数据的北京市热点区域意象感知[J]. 地理科学进展, 2017, 36(9): 1099-1110.
[15]	张少尧, 宋雪茜, 邓伟. 空间功能视角下的公共服务对房价的影响——以成都市为例[J]. 地理科学进展, 2017, 36(8): 995-1005.