The rich-club phenomenon of China's population flow network during the country's spring festival

Wei, Ye; Song, Wei; Xiu, Chunliang; Zhao, Zhongxing

doi:10.1016/j.apgeog.2018.05.009

Cited by 78 publications

(54 citation statements)

References 29 publications

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“…365 Population mobility in the Spring Festival of 2020 was significantly 366 lower than that during the Spring Festival of 2019 (see Figure 11 .) due 367 to the quarantine measure, which prevented the wider spread of the epi-368 demic ( Hu, 2019 ;Wei et al, 2018 ). However, Figure 11 shows that pop-369 ulation mobility has increased since February 17 (the 24 th of the first 370 lunar month of 2020).…”

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confidence: 99%

COVID-19: Challenges to GIS with Big Data

Zhou

Pei

et al. 2020

Geography and Sustainability

450

341

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a b s t r a c tThe outbreak of the 2019 novel coronavirus disease has caused more than 100,000 people to be infected and has caused thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations, and has caused widespread concern around the globe. In the fight against COVID-19, geographic information systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multisource big data, rapid visualization of epidemic information, spatial tracking of COVID-19, prediction of regional transmission, identification of the spatial allocation of risk and selection of the control level, balance and management of the supply and demand of medical resources, social-emotional guidance and panic elimination, the provision of solid spatial information support for decision-making about COVID-19 prevention and control, measures formulation, and assessment of the effectiveness of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against COVID-19, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy to provide accurate information for rapid social management. Additionally, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.

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confidence: 99%

COVID-19: Challenges to GIS with Big Data

Zhou

Pei

et al. 2020

Geography and Sustainability

450

341

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“…Although we can obtain only a small part of Sina Weibo's records for academic research, due to data acquisition restrictions, the number of Sina Weibo posts in each city has a high positive correlation with China's urban population (China Statistic Yearbooks 2015), as shown in Figure 1b, proving that the obtained data represent a reasonable spatial sampling. As observed from the Tencent and Baidu LBS data, population flows usually reach a trough on the third day after the Spring Festival, and the population intercity flow before and after this day is reversed in direction and highly symmetrical in magnitude [23,30]. Therefore, the typical SFT period in 2015 includes two periods: February 7-February 21 is the leaving period (two weeks), when the majority of migrants leave the city where they work and go back to their hometown for family reunion, and February 22-March 7 is the return period (two weeks), when people return to the cities where they live after celebrating Spring Festival.…”

Section: Chinese Spring Festival Travelmentioning

confidence: 72%

“…The current research based on SFT is based mainly on LBS data provided by Baidu and Tencent in 2015 and 2016 [23,29,30]. Therefore, we use the Sina Weibo data obtained through the API provided by the Sina Weibo platform in 2015 to conduct research, which is convenient for comparison with other related research, published urban economic statistics, and China's 13th Five-Year Development Plan.…”

Section: Chinese Spring Festival Travelmentioning

confidence: 99%

Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival

et al. 2020

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Regional development differences are a universal problem in the economic development process of countries around the world. In recent decades, China has experienced rapid urban development since the implementation of the reform and opening-up policy. However, development differs across regions, triggering the migration of laborers from underdeveloped areas to developed areas. The interaction between regional development differences and Spring Festival has formed the world’s largest cyclical migration phenomenon, Spring Festival travel. Studying the migration pattern from public spatiotemporal behavior can contribute to understanding the differences in regional development. This paper proposes a geospatial network analytical framework to quantitatively characterize the imbalance of urban/regional development based on Spring Festival travel from the perspectives of complex network science and geospatial science. Firstly, the urban development difference is explored based on the intercity population flow difference ratio, PageRank algorithm, and attractiveness index. Secondly, the community detection method and rich-club coefficient are applied to further observe the spatial interactions between cities. Finally, the regional importance index and attractiveness index are used to reveal the regional development imbalance. The methods and findings can be used for urban planning, poverty alleviation, and population studies.

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“…In other words, population flows build networks between territorial areas. In this case, although the population flow network exists in a physical space, the often discussed population flows are just types of projections of a real network in certain spatial scales [12]. Population flows data can be divided into two basic types according to their spatial scales: those occurring at intracity level, such as commuting flow networks [13], and those taking place at intercity or regional level, such as migration flow networks [14,15].…”

Section: Data Applicabilitymentioning

confidence: 99%

Urban Network and Regions in China: An Analysis of Daily Migration with Complex Networks Model

et al. 2020

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This paper analyzed urban network and regions in China using a complex network model. Data of daily migration among 348 prefectural-level cities from the Baidu Map location-based service (LBS) Open Platform were used to calculate urban network metrics and to delineate boundaries of urban regions. Results show that urban network in China displays an obvious hierarchy in terms of attracting and distributing population and controlling regional interaction. Regional integration has become increasingly prominent, as administrative boundaries and natural barriers no longer have strong impacts on urban connections. Overall, 18 urban regions were identified according to urban connectivity, and the degree of urban connection is higher among cities in the same urban region. Due to geographical proximity and close interaction, several provincial capital cities form an urban region with cities from neighboring provinces instead of those from the same province. Identification of urban region boundaries is of significant importance for sustainable development and policymaking on the demarcation of urban economic zones, urban agglomerations, and future adjustment of provincial administrative boundaries in China.

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The rich-club phenomenon of China's population flow network during the country's spring festival

Cited by 78 publications

References 29 publications

COVID-19: Challenges to GIS with Big Data

COVID-19: Challenges to GIS with Big Data

Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival

Urban Network and Regions in China: An Analysis of Daily Migration with Complex Networks Model

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