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Spatial Spillover Effect and Influencing Factors of Information Flow in Urban Agglomerations—Case Study of China Based on Baidu Search Index

Author

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  • Chengzhuo Wu

    (Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

  • Li Zhuo

    (Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China
    Guangdong Provincial Engineering Research Center for Public Security and Disaster, Sun Yat-sen University, Guangzhou 510275, China)

  • Zhuo Chen

    (Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

  • Haiyan Tao

    (Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    Guangdong Provincial Engineering Research Center for Public Security and Disaster, Sun Yat-sen University, Guangzhou 510275, China)

Abstract

Cities in an urban agglomeration closely interact with each other through various flows. Information flow, as one of the important forms of urban interactions, is now increasingly indispensable with the fast development of informatics technology. Thanks to its timely, convenient, and spatially unconstrained transmission ability, information flow has obvious spillover effects, which may strengthen urban interaction and further promote urban coordinated development. Therefore, it is crucial to quantify the spatial spillover effect and influencing factors of information flows, especially at the urban agglomeration scale. However, the academic research on this topic is insufficient. We, therefore, developed a spatial interaction model of information flow (SIM-IF) based on the Baidu Search Index and used it to analyze the spillover effects and influencing factors of information flow in the three major urban agglomerations in China, namely Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) in the period of 2014–2019. The results showed that the SIM-IF performed well in all three agglomerations. Quantitative analysis indicated that the BTH had the strongest spillover effect of information flow, followed by the YRD and the PRD. It was also found that the hierarchy of cities had the greatest impact on the spillover effects of information flow. This study may provide scientific basis for the information flow construction in urban agglomerations and benefit the coordinated development of cities.

Suggested Citation

  • Chengzhuo Wu & Li Zhuo & Zhuo Chen & Haiyan Tao, 2021. "Spatial Spillover Effect and Influencing Factors of Information Flow in Urban Agglomerations—Case Study of China Based on Baidu Search Index," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:8032-:d:596789
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    References listed on IDEAS

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    1. Edward L. Glaeser & Janet E. Kohlhase, 2004. "Cities, regions and the decline of transport costs," Advances in Spatial Science, in: Raymond J. G. M. Florax & David A. Plane (ed.), Fifty Years of Regional Science, pages 197-228, Springer.
    2. Yongwan Chun, 2008. "Modeling network autocorrelation within migration flows by eigenvector spatial filtering," Journal of Geographical Systems, Springer, vol. 10(4), pages 317-344, December.
    3. A. Varga, 2006. "Spatial Knowledge Spillovers and University Research: Evidence from Austria," Springer Books, in: Innovation, Networks, and Knowledge Spillovers, chapter 10, pages 211-232, Springer.
    4. Gabauer, David & Gupta, Rangan, 2018. "On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach," Economics Letters, Elsevier, vol. 171(C), pages 63-71.
    5. Hong, Jingke & Gu, Jianping & He, Rongxiao & Wang, Xianzhu & Shen, Qiping, 2020. "Unfolding the spatial spillover effects of urbanization on interregional energy connectivity: Evidence from province-level data," Energy, Elsevier, vol. 196(C).
    6. Conley, Timothy G & Ligon, Ethan, 2002. "Economic Distance and Cross-Country Spillovers," Journal of Economic Growth, Springer, vol. 7(2), pages 157-187, June.
    7. Evert Meijers & Martijn Burger & Edward L. Glaeser & Giacomo A. M. Ponzetto & Yimei Zou, 2016. "Urban networks: Connecting markets, people, and ideas," Papers in Regional Science, Wiley Blackwell, vol. 95(1), pages 17-59, March.
    8. James P. LeSage & Christine Thomas-Agnan, 2015. "Interpreting Spatial Econometric Origin-Destination Flow Models," Journal of Regional Science, Wiley Blackwell, vol. 55(2), pages 188-208, March.
    9. Sabina Buczkowska & Nicolas Coulombel & Matthieu Lapparent, 2019. "A comparison of Euclidean Distance, Travel Times, and Network Distances in Location Choice Mixture Models," Networks and Spatial Economics, Springer, vol. 19(4), pages 1215-1248, December.
    10. Shi, Kaifang & Yu, Bailang & Zhou, Yuyu & Chen, Yun & Yang, Chengshu & Chen, Zuoqi & Wu, Jianping, 2019. "Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels," Applied Energy, Elsevier, vol. 233, pages 170-181.
    11. James P. LeSage & R. Kelley Pace, 2008. "Spatial Econometric Modeling Of Origin‐Destination Flows," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 941-967, December.
    12. Ni, Linglin & Wang, Xiaokun (Cara) & Zhang, Dapeng, 2016. "Impacts of information technology and urbanization on less-than-truckload freight flows in China: An analysis considering spatial effects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 12-25.
    13. Yingxia Pu & Xinyi Zhao & Guangqing Chi & Jin Zhao & Fanhua Kong, 2019. "A spatial dynamic panel approach to modelling the space-time dynamics of interprovincial migration flows in China," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(31), pages 913-948.
    14. Smith, Pamela J., 1999. "Do Knowledge Spillovers Contribute to U.S. State Output and Growth?," Journal of Urban Economics, Elsevier, vol. 45(2), pages 331-353, March.
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    2. Chengfu Wang & Lijun Song & Haoqi Lu & Shuxin Zheng & Chengfeng Huang, 2022. "Assessing the Economic Energy Level of the Chengdu–Chongqing Economic Circle: An Integrative Perspective of “Field Source” and “Field”," Sustainability, MDPI, vol. 14(16), pages 1-22, August.
    3. Sujuan Li & Xiaohui Zhang & Xueling Wu & Erbin Xu, 2022. "Exploration of Urban Network Spatial Structure Based on Traffic Flow, Migration Flow and Information Flow: A Case Study of Shanxi Province, China," Sustainability, MDPI, vol. 14(23), pages 1-18, December.
    4. Fei Ma & Yujie Zhu & Kum Fai Yuen & Qipeng Sun & Haonan He & Xiaobo Xu & Zhen Shang & Yan Xu, 2022. "Exploring the Spatiotemporal Evolution and Sustainable Driving Factors of Information Flow Network: A Public Search Attention Perspective," IJERPH, MDPI, vol. 19(1), pages 1-25, January.

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