SCIENTIA GEOGRAPHICA SINICA ›› 2019, Vol. 39 ›› Issue (9): 1371-1377.doi: 10.13249/j.cnki.sgs.2019.09.002

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Network Association, Spillover Effect and China's Regional Economic Growth Based on Tencent's Location Big Data

Zhang Weili1, Ye Xinyue2, Li Dong3,4, Fu Jibin5, Wu Menghe3   

  1. 1. Academician Laboratory for Urban and Rural Spatial Data Mining, College of Resource and Environments, Henan University of Economics and Law, Zhengzhou 450046, Henan, China
    2. College of Computing Sciences, New Jersey Institute of Technology, Newark 07101, New Jersey, USA
    3. Beijing Tsinghua Tongheng Urban Planning & Design Institute, Beijing 100085, China;
    4. Beijing Key Laboratory of Megaregions Sustainable Development Modelling, Innovation Center for Technology, Beijing 100085, China
    5. College of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou 450046, Henan, China
  • Received:2018-12-04 Revised:2019-02-20 Online:2019-09-10 Published:2019-12-02
  • Supported by:
    National Natural Science Foundation of China(41771124);National Natural Science Foundation of China(71473070);National Natural Science Foundation of China(41101128);Humanities and Social Sciences Research Fund of Ministry of Education in China(17YJC790198);Philosophy and Social Science Program in Henan Province(2017BJJ009);Outstanding Scholars of Philosophy and Social Sciences in Colleges and Universities of Henan Province(2015-YXXZ-17);Young Top Talent Program of Henan University of Economics and Law(hncjzfdxqnbjrc201602)


Based on population mobility data between all prefecture-level cities in China acquired from Tencent mobile phone locations, this article examines the evolution of the spatial correlation model of China's prefecture-level economic growth under the population mobility network. And the spatial spillover effect of economic growth among these cities is also measured in this article by utilizing the network analysis method. The main conclusions obtained in this article are: 1) The construction of the spatial weight matrix should be based on the data that characterizes the interaction and degree between regions. The spatial weight matrix that conforms to reality should be asymmetric and change with the modifying of interaction. 2) Factors such as population mobility networks that interact with prefecture-level cities play an important role in the spillover of economic growth. China's prefecture-level cities show a more obvious ‘center-periphery’ structure. Five central cities: Beijing, Shanghai, Guangzhou, Shenzhen and Chengdu form five fulcrums from north to south and from east to west. The prefecture-level cities with which these five cities have population relationships are concentrated in the eastern and central regions in terms of spatial distribution, and slightly less in the west and northeast. Meanwhile, sub-central prefecture-level cities have formed a hexagonal population mobility network pattern from northeast to southwest. Changchun, Lanzhou, Hangzhou, Dongguan, Nanning and Kunming are vertices of this network. Population mobility is very frequent in this hexagon network and is relatively infrequent outside this hexagon. 3) Network growth effect is an important factor in the economic growth of prefecture-level cities. Using the population mobility network, the population factor has a negative network spillover effect, while foreign direct investment has a positive spillover effect on economic growth. The population factor also has a negative spillover effect on local economic growth, while the fixed asset investment and total retail sales of consumer goods have a significant positive spillover effect, if the geographical proximity network and geographical distance network are used. Finally, according to the research in this article, the policy recommendations are proposed to narrow the economic differences between prefecture-level cities and to achieve coordinated development.

Key words: population mobility network, spatial correlation, spatial spillover effect, network analysis, prefecture-level cities in China, big data

CLC Number: 

  • F129.9