In our recent study titled “The rich-club phenomenon of China’s population flow network during the country’s spring festival.” published in the journal Applied Geography, the rich-club phenomenon of China’s population flow network during the country’s spring festival was investigated.
What is a population flow network?
Population flow is the collective result of individual human behavior. Like other flows, population flow has origins, destinations, and flow paths that constitute a complex population flow network (PFN). For instance, origins and destinations may be countries, provinces, cities, or smaller administrative districts.
Why focus on the Spring Festival period?
Since the 1980s, China’s economy has developed rapidly, and the imbalance of development between regions has also emerged. The competitiveness and attractiveness of more developed eastern coastal regions, particularly large cities, pull migrant workers from the vast less-developed central and western regions. It is very interesting that this implicit fact was reflected explicitly during the “Spring Festival period.”
The Spring Festival period officially spreads over 40 days, including 15 days before Chinese New Year and 25 days after. During the Spring Festival period, the deep-rooted cultural tradition of going home for the Chinese New Year drives a large number of migrant workers from their places of work to return to their hometowns as the Chinese Spring Festival (CSF) approaches, thus forming strong home-bound flows to their hometowns. After CSF, these migrants leave home and go back to their workplaces, forming return flows toward cities.
More than 3 billion population flows were produced during this period (2015) according to official statistics; thus, the Spring Festival travel is considered to be the largest periodical and unique human migration in history. Such large-scale population flows not only reflect the unbalanced development of the regional economy in China but also has created a population flow network between Chinese cities. To build the population flow network, we derived raw data from the Baidu Map Spring Festival Population Migration Data (Baidu Migration Data) platform developed by the Baidu search engine. The basic data element is the number of people traveling to and from 337 cities every day during CSF period in 2015 and the data set comprises forty 337×337 directed and weighted matrices.
What is the rich-club phenomenon?
The phenomenon of influential people becoming friends and forming groups is known as society’s rich-club phenomenon. In network sciences, the concept of a “rich-club” refers to the subgroups of important or influential (rich) nodes that preferentially and intensely interact with one another (Zhou & Mondragón, 2004; Colizza et al., 2006). “Rich” nodes not only form a cohesive group among themselves but they also maintain connections to “poor” nodes. The rich-club coefficient, which is an effective tool to measure the rich-club phenomenon, was first put forward by Zhou and Mondragón (2004) in the context of the Internet. However, early-stage research on the rich-club coefficient was based on undirected and unweighted networks.
Opsahl et al. (2008) proposed a weighted rich-club coefficient to take into account the weights when selecting rich nodes in order to define the relationship between rich nodes and to perform a weighted network analysis. According to Opsahl et al. (2008), there two kinds of rich-club coefficients in a weighted network: global rich-club coefficient and local rich-club coefficient. The global rich-club coefficient can help to determine whether there is a rich club phenomenon and which nodes are the rich club members in a network, and the local rich-club coefficient can measure how each individual node is related to the rich nodes (Opsahl et al. 2008).
The inter-city PFN is a typical directed weighted geographical network, with unevenly distributed population flows among cities (nodes) which themselves vary greatly in influence. The crucial roles played by those “influential” cities need to be recognized in the network analysis. Spatially, these prominent cities dominate the connections within their respective local regions or communities. Collectively, they also exert enormous influence over the entire network and largely shape the network structure. The examination of interactions between the prominent nodes is thus essential for understanding the spatial and organizational structure and dynamics of the PFN. Specifically, this research was designed to identify and examine the rich-club phenomenon in China’s PFN during the country’s Spring Festival, based mainly on the weighted rich-club coefficient.
What did we find?
Methodologically, we employed the global rich-club coefficient of the weighted network and used the local rich-club coefficient, assortativity, and community detection as complementary measures in this study to examine the structure of PFN during CSF and the rich-club phenomenon thoroughly.
Results of this global rich-club coefficient uncovered the existence of a significant rich-club phenomenon in the PFN. Six cities including Beijing, Shanghai, Suzhou, Guangzhou, Shenzhen, and Dongguan are identified as rich-club members. And, the value of assortativity (-0.216) shows that the network is a dissortative network. We, therefore, conclude that the network has typical oligarchy characteristics. Subsequently, our local rich-club coefficient analysis reveals that most (77.66%) of the nodes in the network prefer to connect to rich nodes rather than non-rich nodes. Community detection demonstrates that the network presents obvious geographical agglomeration characteristics and rich-club cities also play a dominant local role in their own “responsible” communities.
These findings suggest that China’s PFN during the country’s Spring Festival is a specific kind of hub-and-spoke network where the rich-club cities are hubs and their interconnections form the backbones. We also identify some specific problems (e.g., the community in southwest China lacks its own rich-club cities resulting in its less integration into the national transportation and migration network). Being the first to discuss rich-club issues of population flow networks in China, findings of this research may be useful for a wide spectrum of applications ranging from population migration, urbanization and traffic infrastructure planning in China.
These findings are described in the article entitled The rich-club phenomenon of China’s population flow network during the country’s spring festival, recently published in the journal Applied Geography. This work was conducted by Ye Wei from Northeast Normal University, Wei Song from the University of Louisville, Chunliang Xiu from Northeastern University, and Ziyu Zhao from Jilin University.
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