The period around Chinese New Year is the most active period of national popula-tion movement in China,providing a natural experiment to examine the character-istics of population flow and interregional connections.Ba...The period around Chinese New Year is the most active period of national popula-tion movement in China,providing a natural experiment to examine the character-istics of population flow and interregional connections.Based on Baidu migration big data from the 2022 and 2023 Spring Festival travel rush,this study analyses over 2.7 billion population flow records from 293 prefecture-level cities and 4 munici-palities over 80 days.From the perspectives of external connections and concentra-tion levels,this study investigates the characteristics and agglomeration features of population mobility at the provincial level.This study reveals that the average daily passenger flow during the 2023 Spring Festival travel rush significantly increased compared to 2022,and the proportion of interprovincial population flow in each province also increased,indicating a rebound in the scale and openness of popula-tion mobility after the COVID-19 pandemic.Guangdong Province is the most active in terms of population mobility,attracting both domestic and out-of-province popu-lations.Provinces with active interprovincial migration are mainly concentrated in the central and eastern regions,with all provinces in the Yangtze River Delta being major employment hubs.Interprovincial migrant populations not only have a large scale and high proportion but also diverse source regions.Central provinces such as Henan,Anhui,and Hunan are major labour exporters.Western,North,and North-east China mainly experience intraprovincial population flow,with interprovincial mobility mostly occurring within provinces in the same region.In contrast,border provinces such as Xinjiang and Tibet have smaller population flows,are less attrac-tive for populations from other provinces,and have lower proportions of local popu-lations leaving,indicating a need for enhanced external connections.展开更多
Developing a comprehensive understanding of inter-city interactions is crucial for regional planning.We therefore examined spatiotemporal patterns of population migration across the Qinghai-Tibet Plateau(QTP)using mig...Developing a comprehensive understanding of inter-city interactions is crucial for regional planning.We therefore examined spatiotemporal patterns of population migration across the Qinghai-Tibet Plateau(QTP)using migration big data from Tencent for the period between 2015 and 2019.We initially used decomposition and breakpoint detection methods to examine time-series migration data and to identify the two seasons with the strongest and weakest population migration levels,between June 18th and August 18th and between October 8th and February 15th,respectively.Population migration within the former period was 2.03 times that seen in the latter.We then used a variety of network analysis methods to examine population flow directions as well as the importance of each individual city in migration.The two capital cities on the QTP,Lhasa and Xining,form centers for population migration and are also transfer hubs through which migrants from other cities off the plateau enter and leave this region.Data show that these two cities contribute more than 35%of total population migration.The majority of migrants tend to move within the province,particularly during the weakest migration season.We also utilized interactive relationship force and radiation models to examine the interaction strength and the radiating energy of each individual city.Results show that Lhasa and Xining exhibit the strongest interactions with other cities and have the largest radiating energies.Indeed,the radiating energy of the QTP cities correlates with their gross domestic product(GDP)(Pearson correlation coefficient:0.754 in the weakest migration season,WMS versus 0.737 in the strongest migration season,SMS),while changes in radiating energy correlate with the tourism-related revenue(Pearson correlation coefficient:0.685).These outcomes suggest that level of economic development and level of tourism are the two most important factors driving the QTP population migration.The results of this analysis provide critical clarification guidance regarding huge QTP development differences.展开更多
基金Major Program of National Fund of Philosophy and Social Science of China:Research on Precision Management of Public Services Driven by Big Data(Project No.:20&ZD113).
文摘The period around Chinese New Year is the most active period of national popula-tion movement in China,providing a natural experiment to examine the character-istics of population flow and interregional connections.Based on Baidu migration big data from the 2022 and 2023 Spring Festival travel rush,this study analyses over 2.7 billion population flow records from 293 prefecture-level cities and 4 munici-palities over 80 days.From the perspectives of external connections and concentra-tion levels,this study investigates the characteristics and agglomeration features of population mobility at the provincial level.This study reveals that the average daily passenger flow during the 2023 Spring Festival travel rush significantly increased compared to 2022,and the proportion of interprovincial population flow in each province also increased,indicating a rebound in the scale and openness of popula-tion mobility after the COVID-19 pandemic.Guangdong Province is the most active in terms of population mobility,attracting both domestic and out-of-province popu-lations.Provinces with active interprovincial migration are mainly concentrated in the central and eastern regions,with all provinces in the Yangtze River Delta being major employment hubs.Interprovincial migrant populations not only have a large scale and high proportion but also diverse source regions.Central provinces such as Henan,Anhui,and Hunan are major labour exporters.Western,North,and North-east China mainly experience intraprovincial population flow,with interprovincial mobility mostly occurring within provinces in the same region.In contrast,border provinces such as Xinjiang and Tibet have smaller population flows,are less attrac-tive for populations from other provinces,and have lower proportions of local popu-lations leaving,indicating a need for enhanced external connections.
基金National Natural Science Foundation of China(41590845)Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19040501)+2 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20040401)National Key Research and Development Program of China(2017YFB0503605)National Key Research and Development Program of China(2017YFC1503003)。
文摘Developing a comprehensive understanding of inter-city interactions is crucial for regional planning.We therefore examined spatiotemporal patterns of population migration across the Qinghai-Tibet Plateau(QTP)using migration big data from Tencent for the period between 2015 and 2019.We initially used decomposition and breakpoint detection methods to examine time-series migration data and to identify the two seasons with the strongest and weakest population migration levels,between June 18th and August 18th and between October 8th and February 15th,respectively.Population migration within the former period was 2.03 times that seen in the latter.We then used a variety of network analysis methods to examine population flow directions as well as the importance of each individual city in migration.The two capital cities on the QTP,Lhasa and Xining,form centers for population migration and are also transfer hubs through which migrants from other cities off the plateau enter and leave this region.Data show that these two cities contribute more than 35%of total population migration.The majority of migrants tend to move within the province,particularly during the weakest migration season.We also utilized interactive relationship force and radiation models to examine the interaction strength and the radiating energy of each individual city.Results show that Lhasa and Xining exhibit the strongest interactions with other cities and have the largest radiating energies.Indeed,the radiating energy of the QTP cities correlates with their gross domestic product(GDP)(Pearson correlation coefficient:0.754 in the weakest migration season,WMS versus 0.737 in the strongest migration season,SMS),while changes in radiating energy correlate with the tourism-related revenue(Pearson correlation coefficient:0.685).These outcomes suggest that level of economic development and level of tourism are the two most important factors driving the QTP population migration.The results of this analysis provide critical clarification guidance regarding huge QTP development differences.
