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Study on the urban agglomerations structure of the Guangdong-Hong Kong-Macao Greater Bay Area based on spatiotemporal big data

  • role： First author第一作者
  • Affiliation：

    Tsinghua University, School of Architecture, Beijing 100084, China

  • Email：1137561049@qq.com
  • Introduction：吴冠秋，1992年生，男，博士研究生，研究方向为城乡规划技术科学。E-mail:1137561049@qq.com
WU Guanqiu1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Tsinghua University, School of Architecture, Beijing 100084, China

  • Email：danganrong@126.com
  • Introduction：党安荣，1964年生，男，教授，研究方向为城乡规划技术科学。E-mail: danganrong@126.com
DANG Anrong1*,  
  • Affiliation：

    Tsinghua University, School of Architecture, Beijing 100084, China

TIAN Ying1,  
  • Affiliation：

    Baidu.com Times Technology (Beijing)Co.,Ltd, Beijing 100085, China

KAN Changcheng2

résumé

The Guangdong-Hong Kong-Macao Greater Bay Area (the greater bay area) is the key strategic area in our country, and the cognition of the collaborative structure of the Greater Bay Area is an important research content for it to build a world-class bay area. As an important model of urban agglomerations, the Greater Bay Area has a complex structural relationship, which is fully reflected in the characteristics of the flow of people between cities and towns, yet inter-city job-housing migration behavior is an intuitive and stable manifestation of the regional population mobility. Therefore, it is significant to develop the cognition of the bay area collaborative structure based on high precision inter-city job-housing data.Based on the summary of the research in Bay Areas around the world, the application research on job-housing spatiotemporal big data, the current status of the relationship between the Greater Bay Area, this paper carried out the research and practice of the collaborative cognitive methods in the Greater Bay Area based on inter-city job-housing spatiotemporal data which is identified by Baidu map. The study built an inter-city job-housing exchange network, using the statistical units as the network nodes and the migration flow as the connection weight, and recognize the inter-city job-housing relationship from three aspects, including the proportion of people moving in and out, weighted degree centrality, and average distance of migration. The study further carries out a cluster analysis on each unit combines with economic data and summarize the units into six types, including exchange center units, dominant units and their special cases, units to be developed, output units, and input units.The research results found that the Greater Bay Area has constructed three complex groups with different exchange structures including Guangzhou and Foshan, Zhongshan and Zhuhai, Shenzhen Dongguan and Huizhou, with obvious multi-center structure. Also, the problem of unbalanced regional coordination in the Bay Area still exists. The spatial distribution of all units’ type shows a pronounced circle structure, and the exchange relationship between the east and west banks is obviously different. Finally, combined with the analysis of spatial structural of relevant policies, the paper preliminarily expounds the development status, development problems and future direction of the urban agglomeration structure, and pointed out that the future development of the Greater Bay Area needs to strengthen the advantages of the multi-center development structure and needs to solve the structural problems in the region, such as the “strong east and weak west”, the lagging development of periphery, and the northward shift of the exchange core. By sorting out the cooperation modes between various units, the Greater Bay Area needs to strengthen the contribution of the dominant units, consolidate the participation of the exchange center units, and avoid the formation of one-way input and output between the polar cores and the surrounding area. Also, the Greater Bay Area needs to make full use of the vast hinterland area to promote the complementarity of all kinds of functions, in order to provide support for the collaborative development of the urban agglomerations.

mots-clés

remote sensing;the Greater Bay Area;urban agglomerations structure;inter-city job-housing;spatiotemporal big data

References

  1. 1.
    Alina S,Jonathan D and Boris A.P.2018.Environmental risk factors associated with low birth weight: The case study of the Haifa Bay Area in Israel.Environmental Research, 165:337-348
  2. 2.
    Chen G H, Chen J Q, Song Z and Han X.2018.Research on Factors and Prediction Model about the Operation of Labor Crowdsourcing APP Platform in Big Data Environment:Part of Cities from the Pearl River Delta as an Example.Statistics & Information Forum,7:116-122
  3. 3.
    Chen Z X and Wu Z B.2019.Land use structure and land use efficiency evaluation on Guangdong Hong-kong Macao Greater Bay Area Urban Agglomeration.Urban Problems.4:29-35
  4. 4.
    Chen S, Yang S and Yu T F.2019.Infrastructure Construction and Regional Driving Performance in Circum-Bohai Bay Region.Planners,7:41-47
  5. 5.
    Dang A R, Yuan M, Shen Z J and Wang P.2015.Rational Planning and Urban Governance Based on Smart City and Big Data.Construction Science and Technology,5:64-66
  6. 6.
    Guo L, Bi Y F, Huang J Z, Zheng C Y, Hu G Y and Wang G.2018.Multi-scale Comparison and Analysis of Jobs-housing Spatiale Characteristics in Big Cities——Taking Wuhan as an Example.Urban Planning Forum,5:88-97
  7. 7.
    Gu C L, Guo J, Yun Y X, Bao L, Zhang Z X, Hou C L, Zheng Y, Li M Y, Niu P Y, Zhang Z X and Li H C.2015.Research on Urban Spatial Layout in Beijing-Tianjin-Hebei Region.Journal of Urban and Regional Planning,1:88-131
  8. 8.
    Jean G, LI H and Chen X Y.2009.Megalopolis:or the Urbanization of the Northeastern Seaboard.Urban Planning International,1:305-311
  9. 9.
    Long Y and Mao Q Z.2019.Theory and Method of Urban Planning Big Data.Beijing: China Architecture & Building Press
  10. 10.
    Meng M X, Zhang X L and Pan Z.2019.Planning practice of metropolitan areas crossing administrative boundaries:The implication of New York Metropolitan Area's four overall regional planning on China.Journal of Chongqing University(Social Science Edition),4:22-37
  11. 11.
    Matt B and Nicole M.A.Community context, human needs, and transportation choices: A view across San Francisco Bay Area communities.Journal of Transport Geography,2017,60:189-199.
  12. 12.
    Ma R F.2018.Analysis of the spatial expansion pattern and its driving factors of Pearl River Delta urban-metropolitan region on the basis of DMSP/OLS and NPP/VIIRS image.Journal of Baoji University of Arts and Sciences(Natural Science Edition),3:68-75
  13. 13.
    Niu X Y, Ding L and Song X D.2017.Analyzing Suburban New Town Development in Shanghai from the Perspective of Jobs-Housing Spatial Relationship.City Planning Review,8:47-53+126
  14. 14.
    Peter H, Kathy P, Luo Z D.2010.Polycentric Metropolises: Experience From The European Mega-Urban Region.Beijing:
  15. 15.
    Qiu J J, Liu Y H, Chen H R and Gao F.2019.Urban Network Structure of Guangdong-Hong Kong-Macao Greater Bay Area with the View of Space of Flows:A Comparison between Information Flow and Transportation Flow.Economic Geography,6:7-15
  16. 16.
    Qiang H H, Wu X, Wang H and Li Q B.2016.Analysis of the Job-Housing Balance for the Newly Employed_Take the Main City of Nanjing as an Example.Urban Planning Forum,4:75-81
  17. 17.
    Shi Y L.1998.“Desakota” Model and Its Effects on the Model of China’s Urban Economic Organization.Urban Development Studies, 5:3-5
  18. 18.
    Wu W J, Wu L Y, Wang K, Shi S, Shi N, Zhang Q, Liu Q Z, Lyu C T, Mao Q Z, Li L and Wu T H.2018.How Should Metropolitan Areas Take the Lead To Realize The“Centenary Goal”.City Planning Review,3:87-94
  19. 19.
    Wu T H and Zhang N.2015.Spatial Pattern And Development Of China's Urban Agglomerations: An Interpretation Of The Sciences Of Human Settlements.City Planning Review,6:14-25+36
  20. 20.
    Wu D G, Zhu W and Wang D.2013.The Process and Inspiration of Plan for the Randstad in Netherlands.Modern Urban Research,1:39-46
  21. 21.
    Xu J, Dang A R and Li T.2016.Research on Urban Planning Method from Big Data Perspective.Geomatics World,3:1-4+12
  22. 22.
    Yan Y G.2019.A Thought on Building the Guangdong-Hong Kong-Macao Greater Bay Area into a World-class Area for Innovative Talents.Macroeconomic Management,9:59-65
  23. 23.
    Zhen M C, Dang A R, Wu G Q and Ma Q W.2020.The Demand of Spatiotemporal Big Data for Quantitative Research on New Urbanization.Geomatics World,27(02):1-8+14
  24. 24.
    Zhen M C, Dang A R and Xu J.2019.Research Progress on the Applications of Big Data to Urban Planning.Geomatics World,26(01):6-12+24
  25. 25.
    Zhen M C, Dang A R and Kan C C.2019.Study on the Identification of Urban Agglomerations in the Yangtze River Delta Based on Big Data and Network Analysis.Shanghai Urban Planning Review,6:8-16
  26. 26.
    Zhou L, Zhao Q and Yang F.2019.Identification of urban agglomeration boundary based on POI and NPP/VIIRS night light data.Progress in Geography,6:840-850
  27. 27.
    Zheng L.2017.Big Data Brings Social Network Analysis into A Golden Age.Social Science Research,2:72-80

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