The inter-city linkage heat data provided by Baidu Migration is employed as a characterization of inter-city linkages in order to facilitate the study of the network linkage characteristics and hierarchical structure ...The inter-city linkage heat data provided by Baidu Migration is employed as a characterization of inter-city linkages in order to facilitate the study of the network linkage characteristics and hierarchical structure of urban agglomeration in the Greater Bay Area through the use of social network analysis method.This is the inaugural application of big data based on location services in the study of urban agglomeration network structure,which represents a novel research perspective on this topic.The study reveals that the density of network linkages in the Greater Bay Area urban agglomeration has reached 100%,indicating a mature network-like spatial structure.This structure has given rise to three distinct communities:Shenzhen-Dongguan-Huizhou,Guangzhou-Foshan-Zhaoqing,and Zhuhai-Zhongshan-Jiangmen.Additionally,cities within the Greater Bay Area urban agglomeration play different roles,suggesting that varying development strategies may be necessary to achieve staggered development.The study demonstrates that large datasets represented by LBS can offer novel insights and methodologies for the examination of urban agglomeration network structures,contingent on the appropriate mining and processing of the data.展开更多
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
Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefor...Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefore,this paper analyzes the spatial interaction between urban scale hierarchy and urban networks in China from 2019 to 2023,drawing on Baidu migration data and employing a spatial simultaneous equation model.The results reveal a significant positive spatial correlation between cities with higher hierarchy and those with greater network centrality.Within a static framework,we identify a positive interaction between urban scale hierarchy and urban network centrality,while their spatial cross-effects manifest as negative neighborhood interactions based on geographical distance and positive cross-scale interactions shaped by network connections.Within a dynamic framework,changes in urban scale hierarchy and urban networks are mutually reinforcing,thereby widening disparities within the urban hierarchy.Furthermore,an increase in a city’s network centrality had a dampening effect on the population growth of neighboring cities and network-connected cities.This study enhances understanding of the spatial organisation of urban systems and offers insights for coordinated regional development.展开更多
Since December 2019,the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade,national policies and the natural environment.To closely monitor the emergence of new COVID-...Since December 2019,the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade,national policies and the natural environment.To closely monitor the emergence of new COVID-19 clusters and ensure high prediction accuracy,we develop a new prediction framework for studying the spread of epidemic on networks based on partial differential equations(PDEs),which captures epidemic diffusion along the edges of a network driven by population flow data.In this paper,we focus on the effect of the population movement on the spread of COVID-19 in several cities from different geographic regions in China for describing the transmission characteristics of COVID-19.Experiment results show that the PDE model obtains relatively good prediction results compared with several typical mathematical models.Furthermore,we study the effectiveness of intervention measures,such as traffic lockdowns and social distancing,which provides a new approach for quantifying the effectiveness of the government policies toward controlling COVID-19 via the adaptive parameters of the model.To our knowledge,this work is the first attempt to apply the PDE model on networks with Baidu Migration Data for COVID-19 prediction.展开更多
文摘The inter-city linkage heat data provided by Baidu Migration is employed as a characterization of inter-city linkages in order to facilitate the study of the network linkage characteristics and hierarchical structure of urban agglomeration in the Greater Bay Area through the use of social network analysis method.This is the inaugural application of big data based on location services in the study of urban agglomeration network structure,which represents a novel research perspective on this topic.The study reveals that the density of network linkages in the Greater Bay Area urban agglomeration has reached 100%,indicating a mature network-like spatial structure.This structure has given rise to three distinct communities:Shenzhen-Dongguan-Huizhou,Guangzhou-Foshan-Zhaoqing,and Zhuhai-Zhongshan-Jiangmen.Additionally,cities within the Greater Bay Area urban agglomeration play different roles,suggesting that varying development strategies may be necessary to achieve staggered development.The study demonstrates that large datasets represented by LBS can offer novel insights and methodologies for the examination of urban agglomeration network structures,contingent on the appropriate mining and processing of the data.
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Under the auspices of the National Natural Science Foundation of China(No.42371222,41971167)Fundamental Scientific Research Funds of Central China Normal University(No.CCNU24ZZ120)。
文摘Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefore,this paper analyzes the spatial interaction between urban scale hierarchy and urban networks in China from 2019 to 2023,drawing on Baidu migration data and employing a spatial simultaneous equation model.The results reveal a significant positive spatial correlation between cities with higher hierarchy and those with greater network centrality.Within a static framework,we identify a positive interaction between urban scale hierarchy and urban network centrality,while their spatial cross-effects manifest as negative neighborhood interactions based on geographical distance and positive cross-scale interactions shaped by network connections.Within a dynamic framework,changes in urban scale hierarchy and urban networks are mutually reinforcing,thereby widening disparities within the urban hierarchy.Furthermore,an increase in a city’s network centrality had a dampening effect on the population growth of neighboring cities and network-connected cities.This study enhances understanding of the spatial organisation of urban systems and offers insights for coordinated regional development.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61672298,61873326,and 61802155)the Philosophy Social Science Research Key Project Fund of Jiangsu University(Grant No.2018SJZDI142)。
文摘Since December 2019,the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade,national policies and the natural environment.To closely monitor the emergence of new COVID-19 clusters and ensure high prediction accuracy,we develop a new prediction framework for studying the spread of epidemic on networks based on partial differential equations(PDEs),which captures epidemic diffusion along the edges of a network driven by population flow data.In this paper,we focus on the effect of the population movement on the spread of COVID-19 in several cities from different geographic regions in China for describing the transmission characteristics of COVID-19.Experiment results show that the PDE model obtains relatively good prediction results compared with several typical mathematical models.Furthermore,we study the effectiveness of intervention measures,such as traffic lockdowns and social distancing,which provides a new approach for quantifying the effectiveness of the government policies toward controlling COVID-19 via the adaptive parameters of the model.To our knowledge,this work is the first attempt to apply the PDE model on networks with Baidu Migration Data for COVID-19 prediction.
