How Different Are Population Movements between Weekdays and Weekends: A Complex-Network-Based Analysis on 36 Major Chinese Cities

Abstract

With the rapid development of Information and Communications Technology (ICT) and transportation infrastructure, the flows of people between cities have become the cornerstone of shaping regional integration. Although research studies about the movement of people have aroused widespread interest in academia, research about the temporal and spatial dynamics of daily mobility between cities is sparse, which is called the temporal heterogeneity of mobility between cities. This research aims to study the temporal and spatial changes (Heterogeneity) of population mobility between cities, using big data obtained through China Unicom, in terms of mapping the spatial network of population mobility and complex network analysis, from which the following findings emerge: (1) On weekends, the gap between cities in the number of floating population flow and the capacity of transferring population has become smaller, indicating that there is better coordination between cities on weekends. (2) There are huge differences in population flow between cities, which reflects the imbalance of urban development, population is more concentrated in cities with higher level of development. (3) The heterogeneity of population flow between cities at weekdays and weekends is closely related to the city’s hierarchy, which can help us study the hierarchical structure of China’s cities from a dynamic perspective. The paper emphasizes the importance of researching heterogeneity issues, clarifies the difference between the heterogeneity of weekdays and weekends and the heterogeneity involved in previous population research fields in terms of population flow and deficiencies in research.

Urban Network Spatial Connection and Structure in China Based on Railway Passenger Flow Big Data

“…Chengdu and Chongqing were the important cities in the western region, and the economic connection between Chongqing and other cities was much greater than Chengdu. Based on mobile signal and OD data from China Unicom in 2018, we characterized the urban network spatial connection and structure using the gravity model [39]. The spatial characteristics also conformed to the "diamond" distribution and were consistent with the conclusion of this study (Figure 7).…”

The Spatial Structure and Driving Mechanisms of Multi-Source Networks in the Chengdu–Chongqing Economic Circle of China

“…where Y i represents the urban contact matrix, i = 1, 2, 3; a 0 is a constant; a 1 − a n are the regression coefficients; and x 1 − x n are the explanatory factors of the correlation matrix. According to Zhu, Cui, Zhang, and Wang [44,45,47,52], the factors that affect network relations are usually divided into four levels (Table 1): economic development difference, the difference in urbanization development, policy differences, and geographical factor. Economic disparities can promote the formation of urban relationships, but beyond a certain limit, they can hinder relationship development.…”

“…Using public budget expenditures to calculate policy similarity, this paper posits that when the public budget expenditures of two cities exhibit regional similarities, they share certain commonalities in policy planning [52].…”

Diurnal urban heat risk assessment using extreme air temperatures and real-time population data in Seoul

“…These patterns further differ between weekdays and weekends. 24 Therefore, daily heat risk assessments at different times in urban areas are vital to help local governments understand the temporal variation in risk and analyze hotspots in detail for specific times, such as extreme heat events. Consideration of temporal variations in thermal characteristics can allow for realistic and cost-effective action plans in response to heat risks.…”