IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i12p4235-d371198.html
   My bibliography  Save this article

Exploration of Urban Interaction Features Based on the Cyber Information Flow of Migrant Concern: A Case Study of China’s Main Urban Agglomerations

Author

Listed:
  • Chun Li

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China)

  • Xingwu Duan

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China)

Abstract

In the context of “space of flow”, urban interaction has become the key force impacting urban landscape evolution and urban sustainable development. Current research on urban interaction analysis is mainly conducted based on the interaction of geographical elements, the virtual flow of information in cyberspace has not been given sufficient attention, particularly the information flows with explicit geographical meaning. Considering the dramatic population migration and the explosive growth of cyberspace in China’s main urban agglomerations, we constructed the information flow of migrant attention (IFMA) index to quantify the urban information interaction derived from public migrant concern in cyberspace. Under the framework coupling spatial pattern analysis and spatial network analysis, exploration spatial data analysis (ESDA) and complex network analysis (CNA) were adopted to identify the urban interaction features depicted by IFMA index in the three main urban agglomerations in China. The results demonstrated that, in the study area: (1) The IFMA index presented a reasonable performance in depicting geographical features of cities; (2) the inconformity between urban role in the network and development positioning confirmed by national planning existed; (3) in the context of New-type urbanization of China, urban interaction feature can be a beneficial reference for urban spatial reconstruction and urban life improvement. Using the cyber information flow with geographical meaning to analyze the urban interaction characteristics can extend the research angle of urban relationship exploration, and provide some suggestion for the adjustment of urban landscape planning.

Suggested Citation

  • Chun Li & Xingwu Duan, 2020. "Exploration of Urban Interaction Features Based on the Cyber Information Flow of Migrant Concern: A Case Study of China’s Main Urban Agglomerations," IJERPH, MDPI, vol. 17(12), pages 1-20, June.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:12:p:4235-:d:371198
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/12/4235/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/12/4235/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Wei & Shen, Dehua & Zhang, Yongjie & Xiong, Xiong, 2013. "Open source information, investor attention, and asset pricing," Economic Modelling, Elsevier, vol. 33(C), pages 613-619.
    2. Xindong Du & Xiaobin Jin & Xilian Yang & Xuhong Yang & Yinkang Zhou, 2014. "Spatial Pattern of Land Use Change and Its Driving Force in Jiangsu Province," IJERPH, MDPI, vol. 11(3), pages 1-18, March.
    3. Smith, Tony E., 1978. "A cost-efficiency principle of spatial interaction behavior," Regional Science and Urban Economics, Elsevier, vol. 8(4), pages 313-337, December.
    4. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    5. Yuan Gao & Qingsong He & Yaolin Liu & Lingyu Zhang & Haofeng Wang & Enxiang Cai, 2016. "Imbalance in Spatial Accessibility to Primary and Secondary Schools in China: Guidance for Education Sustainability," Sustainability, MDPI, vol. 8(12), pages 1-16, November.
    6. Liwen Vaughan & Yue Chen, 2015. "Data mining from web search queries: A comparison of google trends and baidu index," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(1), pages 13-22, January.
    7. Gangopadhyay, Kausik & Basu, B., 2009. "City size distributions for India and China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(13), pages 2682-2688.
    8. Xiushi Yang, 2000. "Determinants of Migration Intentions in Hubei Province, China: Individual versus Family Migration," Environment and Planning A, , vol. 32(5), pages 769-787, May.
    9. Filippo Simini & Marta C. González & Amos Maritan & Albert-László Barabási, 2012. "A universal model for mobility and migration patterns," Nature, Nature, vol. 484(7392), pages 96-100, April.
    10. Li Yue & Dan Xue & Muhammad Umar Draz & Fayyaz Ahmad & Jiaojiao Li & Farrukh Shahzad & Shahid Ali, 2020. "The Double-Edged Sword of Urbanization and Its Nexus with Eco-Efficiency in China," IJERPH, MDPI, vol. 17(2), pages 1-20, January.
    11. Qingyu Fan & Shan Yang & Shuaibin Liu, 2019. "Asymmetrically Spatial Effects of Urban Scale and Agglomeration on Haze Pollution in China," IJERPH, MDPI, vol. 16(24), pages 1-18, December.
    12. Roberta Capello, 2000. "The City Network Paradigm: Measuring Urban Network Externalities," Urban Studies, Urban Studies Journal Limited, vol. 37(11), pages 1925-1945, October.
    13. Ren, Yu & Xiong, Cong & Yuan, Yufei, 2012. "House price bubbles in China," China Economic Review, Elsevier, vol. 23(4), pages 786-800.
    14. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    15. Frank Goetzke & Regine Gerike & Antonio Páez & Elenna Dugundji, 2015. "Social interactions in transportation: analyzing groups and spatial networks," Transportation, Springer, vol. 42(5), pages 723-731, September.
    16. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    17. Haisen Wang & Gangqiang Yang & Jiaying Qin, 2020. "City Centrality, Migrants and Green Inovation Efficiency: Evidence from 106 Cities in the Yangtze River Economic Belt of China," IJERPH, MDPI, vol. 17(2), pages 1-21, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Jinlong Wang & Ling Yang & Min Deng & Gui Zhang & Yaoqi Zhang, 2023. "Selection of optimal regulation scheme by simulating spatial network of ecological-economic-social compound system: a case study of Hunan province, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(3), pages 2831-2856, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
    2. Zhongchen Song & Tom Coupé, 2023. "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
    3. Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping, 2022. "Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1005-1024.
    4. Chun Li & Jianhua He & Xingwu Duan, 2020. "The Relationship Exploration between Public Migration Attention and Population Migration from a Perspective of Search Query," IJERPH, MDPI, vol. 17(7), pages 1-18, April.
    5. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    6. Binru Zhang & Yulian Pu & Yuanyuan Wang & Jueyou Li, 2019. "Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    7. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    8. Stephen L. France & Yuying Shi, 2017. "Aggregating Google Trends: Multivariate Testing and Analysis," Papers 1712.03152, arXiv.org, revised Mar 2018.
    9. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.
    10. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
    11. Javier Sebastian, 2016. "Blockchain in financial services: Regulatory landscape and future challenges," Working Papers 16/21, BBVA Bank, Economic Research Department.
    12. A Fronzetti Colladon & B Guardabascio & R Innarella, 2021. "Using social network and semantic analysis to analyze online travel forums and forecast tourism demand," Papers 2105.07727, arXiv.org.
    13. Vilma Deltuvaitė & Svatopluk Kapounek & Petr Koráb, 2019. "Impact of Behavioural Attention on the Households Foreign Currency Savings as a Response to the External Macroeconomic Shocks," Prague Economic Papers, Prague University of Economics and Business, vol. 2019(2), pages 155-177.
    14. Yu Qin & Hongjia Zhu, 2018. "Run away? Air pollution and emigration interests in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 31(1), pages 235-266, January.
    15. Zhang, Yongjie & Feng, Lina & Jin, Xi & Shen, Dehua & Xiong, Xiong & Zhang, Wei, 2014. "Internet information arrival and volatility of SME PRICE INDEX," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 70-74.
    16. Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.
    17. Mingyang Zhang & Heyan Xu & Ning Ma & Xinglin Pan, 2022. "Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search Index," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    18. Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.
    19. Aura Reggiani, 2022. "The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience," Networks and Spatial Economics, Springer, vol. 22(3), pages 415-437, September.
    20. Benedikt Maas, 2020. "Short‐term forecasting of the US unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 394-411, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:12:p:4235-:d:371198. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.