IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v25y2023i2d10.1007_s10668-022-02120-0.html
   My bibliography  Save this article

Spatiotemporal evolution of pseudo human settlements: case study of 36 cities in the three provinces of Northeast China from 2011 to 2018

Author

Listed:
  • Shenzhen Tian

    (Liaoning Normal University
    Liaoning Normal University
    China Urban Agglomeration Research Base Alliance, Mid-Southern Liaoning Urban Agglomerations
    CAS)

  • Xueming Li

    (Liaoning Normal University
    China Urban Agglomeration Research Base Alliance, Mid-Southern Liaoning Urban Agglomerations)

  • Jun Yang

    (Liaoning Normal University
    Liaoning Normal University
    China Urban Agglomeration Research Base Alliance, Mid-Southern Liaoning Urban Agglomerations)

  • Hui Wang

    (Liaoning Normal University)

  • Jianke Guo

    (Liaoning Normal University)

Abstract

The Internet is an important component of human settlements, the current research on reality human settlements is far from satisfying the theory and practice development in the Sciences of Human Settlements in information era, it is necessary to introduce pseudo human settlements (PHSs), and the three provinces of Northeast China (TPNC) are a typical area of “unbalanced development.” It is obviously inappropriate to use the traditional geographical concept of man–land to recognize the new man–land Relationship, cognizing and studying the spatiotemporal evolution of TPNC’s PHSs make a beneficial supplement to the theoretical exploration of the Sciences of Human Settlements and the revitalization of TPNC. Data mining technology is used to establish the PHSs database, entropy weight method is used to study the time course, and the spatial analysis function of ArcGIS 10.2 carries out spatial analysis, type analysis, pattern analysis and visualization of corresponding maps of the urban PHSs. Two processes of PHSs change: development and shrinkage were considered, and several conclusions were arrived at after studying its hierarchical system, temporal processes, spatial patterns and special effects. (1). The hierarchical system has significance, with the urban PHSs in 2011–2018 presenting obvious hierarchical differences and the characteristic of primacy. Specifically, the hierarchical system is jointly formed by the regional centers, regional subcenters, urban centers and nodes, Shenyang and Dalian form a dual core, while Changchun and Harbin are single centers, which constitute the contextual framework of the TPNC’ PHSs. (2). The overall trend of urban PHSs is development in the temporal processes; at the same time, there are both continuous development periods and isolated shrinkage points in 2011–2018; the years with high degrees of development and shrinkage are 2016 and 2018, respectively. The two main temporal categories are development and shrinkage, development is divided into three sub-categories and shrinkage is divided into two sub-categories. (3) The spatial patterns of urban PHSs presents obvious typical characteristics in geographical space, which can be divided into five categories. Even though the spatial patterns contain shrinkages, the dominant trend is still development. The overall characteristic of the spatiotemporal evolution is “evolving from shrinkage to development and then to shrinkage, specifically, from mild shrinkage to general development and then to mild shrinkage.” (4). The special effects of urban PHSs mainly three types, including “double eleven effect,” “precursor effect” and “Friday and Saturday effect”; in essence, these effects represent the spatiotemporal evolution trend of geographical phenomena such as the development and shrinkage of PHSs in a certain time and space.

Suggested Citation

  • Shenzhen Tian & Xueming Li & Jun Yang & Hui Wang & Jianke Guo, 2023. "Spatiotemporal evolution of pseudo human settlements: case study of 36 cities in the three provinces of Northeast China from 2011 to 2018," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1742-1772, February.
  • Handle: RePEc:spr:endesu:v:25:y:2023:i:2:d:10.1007_s10668-022-02120-0
    DOI: 10.1007/s10668-022-02120-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02120-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10668-022-02120-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fatehkia, Masoomali & Kashyap, Ridhi & Weber, Ingmar, 2018. "Using Facebook Ad Data to Track the Global Digital Gender Gap," SocArXiv rkvb3, Center for Open Science.
    2. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    3. Jiaji Gao & Yingjia Zhang & Xueming Li, 2016. "Basic Characteristics and Spatial Patterns of Pseudo-Settlements—Taking Dalian as An Example," IJERPH, MDPI, vol. 13(1), pages 1-14, January.
    4. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    5. Wenjie Wu & Jianghao Wang & Tianshi Dai, 2016. "The Geography of Cultural Ties and Human Mobility: Big Data in Urban Contexts," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(3), pages 612-630, May.
    6. Clifford Lynch, 2008. "How do your data grow?," Nature, Nature, vol. 455(7209), pages 28-29, September.
    7. Jie Huang & David Levinson & Jiaoe Wang & Haitao Jin, 2019. "Job-worker spatial dynamics in Beijing: Insights from Smart Card Data," Working Papers 2019-01, University of Minnesota: Nexus Research Group.
    8. Concha Artola & Fernando Pinto & Pablo de Pedraza García, 2015. "Can internet searches forecast tourism inflows?," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 103-116, April.
    9. Pan, Bing, 2015. "The power of search engine ranking for tourist destinations," Tourism Management, Elsevier, vol. 47(C), pages 79-87.
    10. Fatehkia, Masoomali & Kashyap, Ridhi & Weber, Ingmar, 2018. "Using Facebook ad data to track the global digital gender gap," World Development, Elsevier, vol. 107(C), pages 189-209.
    11. Concha Artola & Fernando Pinto & Pablo de Pedraza García, 2015. "Can internet searches forecast tourism inflows?," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 103-116, April.
    12. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Fengzhi Sun & Zihan Li & Mingzhi Xu & Mingcan Han, 2024. "New Changes in Chinese Urban Tourism Pattern under the Impact of COVID-19 Pandemic: Based on Internet Attention," Sustainability, MDPI, vol. 16(14), pages 1-22, July.
    3. 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.
    4. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Bai, Lijuan & Yan, Xiangbin & Yu, Guang, 2019. "Impact of CEO media appearance on corporate performance in social media," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    10. Andreea Avramescu & Arkadiusz Wiśniowski, 2021. "Now-casting Romanian migration into the United Kingdom by using Google Search engine data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(40), pages 1219-1254.
    11. Yang, Yang & Fan, Yawen & Jiang, Lan & Liu, Xiaohui, 2022. "Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?," Annals of Tourism Research, Elsevier, vol. 93(C).
    12. Katerina Volchek & Anyu Liu & Haiyan Song & Dimitrios Buhalis, 2019. "Forecasting tourist arrivals at attractions: Search engine empowered methodologies," Tourism Economics, , vol. 25(3), pages 425-447, May.
    13. Silva, Emmanuel Sirimal & Hassani, Hossein & Heravi, Saeed & Huang, Xu, 2019. "Forecasting tourism demand with denoised neural networks," Annals of Tourism Research, Elsevier, vol. 74(C), pages 134-154.
    14. Imene Ben El Hadj Said & Skander Slim, 2022. "The Dynamic Relationship between Investor Attention and Stock Market Volatility: International Evidence," JRFM, MDPI, vol. 15(2), pages 1-25, February.
    15. Keqing Li & Wenxing Lu & Changyong Liang & Binyou Wang, 2019. "Intelligence in Tourism Management: A Hybrid FOA-BP Method on Daily Tourism Demand Forecasting with Web Search Data," Mathematics, MDPI, vol. 7(6), pages 1-14, June.
    16. Fabo, B., 2017. "Towards an understanding of job matching using web data," Other publications TiSEM b8b877f2-ae6a-495f-b6cc-9, Tilburg University, School of Economics and Management.
    17. Silva, Emmanuel Sirimal & Ghodsi, Zara & Ghodsi, Mansi & Heravi, Saeed & Hassani, Hossein, 2017. "Cross country relations in European tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 151-168.
    18. Mingming Hu & Haiyan Song, 2020. "Data source combination for tourism demand forecasting," Tourism Economics, , vol. 26(7), pages 1248-1265, November.
    19. Correa, Alexander, 2021. "Prediciendo la llegada de turistas a Colombia a partir de los criterios de Google Trends," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue No. 95, pages 105-134, July.
    20. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.

    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:spr:endesu:v:25:y:2023:i:2:d:10.1007_s10668-022-02120-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.