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Spatiotemporal Change Characteristics of Nodes’ Heterogeneity in the Directed and Weighted Spatial Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China

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  • Jing Yang

    (College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
    3D Information Collection and Application Key Lab of Education Ministry, Capital Normal University, Beijing 100048, China
    Beijing State key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China)

  • Disheng Yi

    (College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
    3D Information Collection and Application Key Lab of Education Ministry, Capital Normal University, Beijing 100048, China
    Beijing State key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China)

  • Jingjing Liu

    (College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
    3D Information Collection and Application Key Lab of Education Ministry, Capital Normal University, Beijing 100048, China
    Beijing State key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China)

  • Yusi Liu

    (College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
    3D Information Collection and Application Key Lab of Education Ministry, Capital Normal University, Beijing 100048, China
    Beijing State key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China)

  • Jing Zhang

    (College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
    3D Information Collection and Application Key Lab of Education Ministry, Capital Normal University, Beijing 100048, China
    Beijing State key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China)

Abstract

Spatial heterogeneity patterns in cities are an essential topic in geographic research and urban planning. This paper analyzes the spatial heterogeneity of places and reflects on the urban structure in cites based on spatial interaction networks. To begin with, we constructed 24 sequentially directed and weighted spatial interaction networks (DWNs) on the basis of points of interest (POIs) and taxi GPS data in Beijing. Then, we merged 24 sequential networks into four clusters: early morning, morning, afternoon, and evening. Next, we introduced the weighted D-core decomposition method in view of the complex network method and weighted distance in a geographic space in order to obtain the in-coreness/out-coreness of places. Finally, three indices (the entropy index, the node symmetry index, and the t -test) were used to measure the heterogeneity of places from both the strength dimension and the direction dimension. The results showed: (1) For the strength dimension, the spatiotemporal strength characteristics of the nodes in the DWN are uneven on weekdays or on the weekends, and the strength heterogeneity on weekdays is more obvious than on weekends; (2) for the direction dimension, out-flows and in-flows are different in the early morning and evening on weekends. In addition, the direction of the DWN is not obvious. The city networks present flat characteristics. This study used the weighted D-core method to identify the heterogeneity of nodes in the DWN, which has certain theoretical and practical value for the planning of urban and urban systems and the coordinated development of cities.

Suggested Citation

  • Jing Yang & Disheng Yi & Jingjing Liu & Yusi Liu & Jing Zhang, 2019. "Spatiotemporal Change Characteristics of Nodes’ Heterogeneity in the Directed and Weighted Spatial Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6359-:d:286267
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    References listed on IDEAS

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    1. Wenjia Zhang & Jean-Claude Thill, 2019. "Mesoscale Structures in World City Networks," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(3), pages 887-908, May.
    2. Derudder, Ben & Witlox, Frank, 2009. "The impact of progressive liberalization on the spatiality of airline networks: a measurement framework based on the assessment of hierarchical differentiation," Journal of Transport Geography, Elsevier, vol. 17(4), pages 276-284.
    3. Yu Liu & Chaogui Kang & Song Gao & Yu Xiao & Yuan Tian, 2012. "Understanding intra-urban trip patterns from taxi trajectory data," Journal of Geographical Systems, Springer, vol. 14(4), pages 463-483, October.
    4. Yu Liu & Xi Liu & Song Gao & Li Gong & Chaogui Kang & Ye Zhi & Guanghua Chi & Li Shi, 2015. "Social Sensing: A New Approach to Understanding Our Socioeconomic Environments," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(3), pages 512-530, May.
    5. Narisra Limtanakool & Tim Schwanen & Martin Dijst, 2009. "Developments in the Dutch Urban System on the Basis of Flows," Regional Studies, Taylor & Francis Journals, vol. 43(2), pages 179-196.
    6. Yu Liu & Zhengwei Sui & Chaogui Kang & Yong Gao, 2014. "Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    7. Liang, Xiao & Zheng, Xudong & Lv, Weifeng & Zhu, Tongyu & Xu, Ke, 2012. "The scaling of human mobility by taxis is exponential," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 2135-2144.
    8. Chengbin Peng & Xiaogang Jin & Ka-Chun Wong & Meixia Shi & Pietro Liò, 2012. "Collective Human Mobility Pattern from Taxi Trips in Urban Area," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-8, April.
    9. Manlio De Domenico & Vincenzo Nicosia & Alexandre Arenas & Vito Latora, 2015. "Structural reducibility of multilayer networks," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    10. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
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