Spatial patterns and determinant factors of population flow networks in China: Analysis on Tencent Location Big Data

Zhang, Weili; Chong, Zhaohui; Li, Xiaojian; Nie, Gui-bo

doi:10.1016/j.cities.2020.102640

Cited by 108 publications

(48 citation statements)

References 44 publications

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“…Liu et al (2017) insisted that the region that can provide more and higher career platforms will show great advantages to gather experts [37]. The roles of these factors have been the primarily and prevalently career-related drivers, which is an economic and institutional environment that may affect the behaviors in the process of making and fulfilling relocation decisions [38]. Obviously, the professional opportunity is highly positive correlation by China's hierarchical science and technology (S&T) system [39], with ample resource and premium research organizations.…”

Section: Academica Opportunity and Location Choicementioning

confidence: 99%

Location Choice of Overseas High-Level Young Returned Talents in China

2020

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International talent’s mobility has increased over the last decade, especially in the wake of globalization and knowledge economy. China no longer exclusively shows an asymmetrical flow to developed countries, but it often turns out to be the most desirable destination of brain circulation for some overseas high-level young returned talents. In this context, based on the data obtained from the “Thousand Youth Talents Plan” (2011–2016) publicized by the Central Millennium Plan Office, the methods of the Moran’s I index, the Getis-Ord Gi* index, and the Poisson regression model are adopted in this study to analyze the location choice of overseas high-level young returned talents in China and its driving factors. The spatial configuration shows up that the United States is the most important origin overseas and China’s municipalities or provincial capitals are the most remarkable destinations. Moreover, the analysis reveals the distribution by academic majors is uneven, which may be caused by knowledge priority and legacies of China’s Soviet-style innovation system. The main analysis is extended with the Poisson regression model to estimate the significance of the influence factors. The empirical results show that the factors in order of importance are academic opportunity, urban amenity, and place attachment. Concretely, the most important factors for location choice of overseas high-level young returned talents, namely research and development (R&D) investment, internal convenience and hierarchy level in science and technology (S&T) system, external accessibility, transportation accessibility, university endowment, and health care are the substantial secondary factors, while place-oriented talent agglomeration and prior alumni connection play a minor role.

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Section: Academica Opportunity and Location Choicementioning

confidence: 99%

Location Choice of Overseas High-Level Young Returned Talents in China

2020

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“…Ye et al [25] found that a significant rich-club phenomenon occurred in Chinese population movements during the 2015 Spring Festival. Zhang et al [26] used Tencent location big data to explore population movement characteristics among 234 cities in China. However, previous research has mainly focused on temporal changes in migration, especially patterns on holidays, and has not explored gradual trends and influencing factors.…”

Section: Introductionmentioning

confidence: 99%

Evolution Characteristics and Regional Roles’ Influencing Factors of Interprovincial Population Mobility Network in China

Fang

Liu

2021

Complexity

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This paper analyses the evolutionary characteristics of the interprovincial population mobility network structure in China and explores the roles of provinces from 2010 to 2015. By constructing the interprovincial population mobility network, we examine the provinces’ functions during periods of population mobility through population mobility diversity, population mobility, and population mobility intermediation. The results show that the coverage and tightness of the interprovincial population mobility network were influenced by national economic development, increasing steadily from 2010 to 2014 and then suddenly falling in 2015. The regions that played essential roles in the province’s mobility diversity and intermediation showed a dispersion trend. In contrast, areas that played a vital role in the inflows or outflows were relatively concentrated and stable. Additionally, this work further explores how economic factors, e.g., GDP, residents’ consumption levels, total population, unemployment rate, and consumer price index, are used as independent variables to analyse the provinces’ roles in the interprovincial population mobility network. The analysis shows that the inflow or outflow volumes are easily affected by the five indices. These five indices are significantly related to network role indicators to different extents.

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“…e 2016 Report of China's Floating Population Development estimated that China's floating population reached 247 million in 2015, accounting for 18% of the total population. In addition, the direction of Chinese population flow has changed in recent years, with the labor force, especially migrant workers, returning from eastern coastal cities to central and western cities, and laborand resource-intensive industries transferring to central and western regions [4].…”

Section: Introductionmentioning

confidence: 99%

Network Structure of Intercity Trips by Chinese Residents under Different Travel Modes: A Case Study of the Spring Festival Travel Rush

2021

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With the advent of big data, the use of network data to characterize travel has gradually become a trend. Tencent Migration big data can fully, dynamically, immediately, and visually record the trajectories of population migrations with location-based service technology. Here, the daily population flow data of 346 cities during the Spring Festival travel rush in China were combined with different travel modes to measure the spatial structure and spatial patterns of an intercity trip network of Chinese residents. These data were then used for a comprehensive depiction of the complex relationships between the population flows of cities. The results showed that there were obvious differences in the characteristics of urban networks from the perspective of different modes of travel. The intercity flow of aviation trips showed a core-periphery structure with national hub cities as the core distribution. Trips by train showed a core-periphery structure with cities along the national railway artery as the core. This gradually decreased toward hinterland cities. Moreover, the intercity flow of highway trips indicated a spatial pattern of strong local aggregation that matched the population scale.

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Spatial patterns and determinant factors of population flow networks in China: Analysis on Tencent Location Big Data

Cited by 108 publications

References 44 publications

Location Choice of Overseas High-Level Young Returned Talents in China

Location Choice of Overseas High-Level Young Returned Talents in China

Evolution Characteristics and Regional Roles’ Influencing Factors of Interprovincial Population Mobility Network in China

Network Structure of Intercity Trips by Chinese Residents under Different Travel Modes: A Case Study of the Spring Festival Travel Rush

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