Uncovering Network Heterogeneity of China’s Three Major Urban Agglomerations from Hybrid Space Perspective-Based on TikTok Check-In Records

Abstract

Urban agglomeration is an essential spatial support for the urbanization strategies of emerging economies, including China, especially in the era of mediatization. From a hybrid space perspective, this paper invites TikTok cross-city check-in records to empirically investigate the vertical and flattened distribution characteristics of check-in networks of China’s three major urban agglomerations by the hierarchical property, community scale, and node centrality. The result shows that (1) average check-in flow in the Yangtze River Delta, Beijing-Tianjin-Hebei, and Pearl River Delta network decreases in descending order, forming a Z-shaped, single-point radial, and N-shaped structure, respectively. (2) All three urban agglomerations exhibit a nexus community structure with the regional high-flow cities as the core and the surrounding cities as the coordinator. (3) Geographically proximate or recreation-resource cities have a high degree of hybrid spatial accessibility, highlighting their nexus role. Finally, the article further discusses the flattened evolutionary structure of the check-in network and proposes policy recommendations for optimizing check-in networks at both the digital and geospatial levels. The study gains from the lack of network relationship perspective in the study of location-based social media and provides a novel research method and theoretical support for urban agglomeration integration in the context of urban mediatization.

Evolution characteristics and influencing factors of information network in Guangdong-Hong Kong-Macao Greater Bay Area

“…With the advent of the Internet, a considerable amount of user behavior data has been generated, which provides important data support for the measurement of regional networks [29]. For example, Pan et al (2019) used social network analysis to explore the spatial structure network characteristics of the Chengdu-Chongqing urban agglomeration through urban Weibo check-in big data constituting the Chengdu-Chongqing population flow information network data [30]; Xiang B (2023)used TikTok cross-city check-in data to explore the Chinese information network in terms of hierarchical attributes, community size, and node centrality spatial structure [31]; Bao Z (2021) utilized the loan records of fintech borrowers to explore the spatial structure exhibited by fintech borrower data from data on demographic characteristics, credit characteristics, and current loan information [32]. Some scholars have also used behavioral data such as Weibo data, Tencent location data, and Baidu migration data to study the spatial association characteristics of information flow [33][34][35], however, there are data processing problems with user behavioral data in accurately measuring information intensity [36].In reality, with the widespread use of search engines, Internet users' search activity has become a normal social behavior in the information age, which plays an essential part in the development of intercity information flows [37,38].…”

Spatial structure and proximity mechanism of cross-city patient mobility networks in the Yangtze River Economic Belt of China: Based on patient online evaluation data

“…Unravelling this network's structure is crucial for guiding intercity medical resource allocation and represents a challenging research area in large-scale patient mobility studies (C. Wang et al, 2021). With spatial barriers between cities diminishing globally, almost every city is now integrated into a global network system (Cui et al, 2020;Guo & Qin, 2022;Taylor, 2005;Xiang et al, 2022), which has led to a proliferation of urban network studies, opening new avenues for patient mobility research. These studies can be broadly classified into three categories: (1) analysing city roles and importance in the network using node centrality indicators (D. Li et al, 2015;Wei et al, 2022); (2) exploring network spatial organization (Deng et al, 2022;W.…”