Big data analysis on the spatial networks of urban agglomeration

doi:10.1016/j.cities.2020.102735

	Big data analysis on the spatial networks of urban agglomeration
	Fang, Chuanglin 2; Yu, Xiaohua 2; Zhang, Xiaoling 1; Fang, Jiawen 3; Liu, Haimeng 2
刊名	CITIES
	2020-07-01
卷号	102 页码:16
关键词	Social network big data Strength of spatial networks Spatial connectivity Urban network Integrated measurement model Beijing-Tianjin-Hebei urban agglomeration
ISSN号	0264-2751
DOI	10.1016/j.cities.2020.102735
通讯作者	Fang, Chuanglin(fangcl@igsnrr.ac.cn)
英文摘要	Urban agglomerations are considered as significant space typologies in the post globalization & digitalization era. The spatial linkage intensity of cities in urban agglomeration is an important basis for evaluating the development and compactness of urban agglomerations. The requirements associated with the major national strategy to achieve the coordinated development of the Beijing-Tianjin-Hebei (BTH) region and the research methods made possible by Big Data in the Internet era have created the realistic possibility of revealing the strength of spatial networks and the spatial differentiation rules of urban agglomerations. This study uses Web Crawler to obtain 500,000 sets of Weibo data in 13 cities of the BTH urban agglomeration. Three criteria and nine indicators are used to construct an index system and a model to quantitatively evaluate the strength of spatial networks in the BTH urban agglomeration. The results show that spatial network connections between cities in the urban agglomeration are not strong overall, reflecting the limited development of urban agglomeration; the spatial networks in the urban agglomeration have hierarchical and centralized characteristics, which reflect the imbalanced development of the city network; Beijing, Tianjin, and Shijiazhuang are the centers of the social network connections in the BTH urban agglomeration, which means that Weibo's online space reinforces the existing urban system; there is a positive correlation between network spatial connectivity and the hierarchy of cities in the urban agglomeration, where cities with higher levels would have more urban network connections; Generally, Weibo network connections are stronger than economic or transport links between the cities, and the development of information technology may reduce the disparity of regional development. This research innovatively uses social network big data to reveal the strength of spatial network connections and spatial differentiation rules in the urban agglomeration. The methodology provided in this paper is systematic enough for generalization.
资助项目	Major Program of the National Natural Science Foundation of China[41590840] ; Major Program of the National Natural Science Foundation of China[41590842] ; National Natural Science Foundation of China[71834005] ; National Natural Science Foundation of China[71673232] ; National Natural Science Foundation of China[41801164] ; Research Grant Council of Hong Kong, China[CityU 11271716] ; Research Grant Council of Hong Kong, China[CityU 21209715]
WOS关键词	CHINA ; EVOLUTION ; SYSTEM
WOS研究方向	Urban Studies
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000534585300002
资助机构	Major Program of the National Natural Science Foundation of China ; National Natural Science Foundation of China ; Research Grant Council of Hong Kong, China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/159570]
专题	中国科学院地理科学与资源研究所
通讯作者	Fang, Chuanglin
作者单位	1.City Univ Hong Kong, Dept Publ Policy, Hong Kong, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 3.Univ Southern Calif, Sol Price Sch Publ Policy, Los Angeles, CA 90007 USA
推荐引用方式 GB/T 7714	Fang, Chuanglin,Yu, Xiaohua,Zhang, Xiaoling,et al. Big data analysis on the spatial networks of urban agglomeration[J]. CITIES,2020,102:16.
APA	Fang, Chuanglin,Yu, Xiaohua,Zhang, Xiaoling,Fang, Jiawen,&Liu, Haimeng.(2020).Big data analysis on the spatial networks of urban agglomeration.CITIES,102,16.
MLA	Fang, Chuanglin,et al."Big data analysis on the spatial networks of urban agglomeration".CITIES 102(2020):16.