Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data

Abstract

The rapid development of the economy promotes the increasing of interactions between cities and forms complex networks. Many scholars have explored the structural characteristics of urban spatial interaction networks in China and have conducted spatio-temporal analyzes. However, scholars have mainly focused on the perspective of static networks and have not understood the dynamic spatial interaction patterns of Chinese cities. Therefore, this paper proposes a research framework to explore the urban dynamic spatial interaction patterns. Firstly, we establish a dynamic urban spatial interaction network according to monthly migration data. Then, the dynamic community detection algorithm, combined with the Louvain and Jaccard matching method, is used to obtain urban communities and their dynamic events. We construct event vectors for each urban community and use hierarchical clustering to cluster event vectors to obtain different types of spatial interaction patterns. Finally, we divide the urban dynamic interaction into three urban spatial interaction modes: fixed spatial interaction pattern, long-term spatial interaction pattern, and short-term spatial interaction pattern. According to the results, we find that the cities in well-developed areas (eastern China) and under-developed areas (northwestern China) mostly show fixed spatial interaction patterns and long-term spatial interaction patterns, while the cities in moderately developed areas (central and western China) often show short-term spatial interaction patterns. The research results and conclusions of this paper reveal the inter-monthly urban spatial interaction patterns in China, provide theoretical support for the policy making and development planning of urban agglomeration construction, and contribute to the coordinated development of national and regional cities.

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

“…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]. Currently, scientists have started to employ Google or Baidu's web search data to mine the evolutionary patterns and inner processes of spatial association links in urban clusters [14,36,39].…”

Characterizing Intercity Mobility Patterns for the Greater Bay Area in China

“…It has laid an important foundation for studying urban networks. For example, some researchers take cities as nodes to form a network for urban agglomerations [16][17][18], to explore the connections between cities or to evaluate each city's importance based upon the complex network theory [19]. However, such a method mainly analyzes the intercity patterns from the average level at a certain time or stage and fails to effectively characterize the dynamic variations of urban connection.…”