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

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  • 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
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    References listed on IDEAS

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    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.
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