Spatiotemporal Patterns of Population Mobility and Its Determinants in Chinese Cities Based on Travel Big Data

Analyzing post-COVID-19 demographic and mobility changes in Andalusia using mobile phone data

This work studies changes in the demographics of the different spatial units that make up the Andalusia region in Spain throughout the year 2021, with the aim of seeing the progressive recovery of the population after the COVID-19 pandemic. Mobile phone data from Origin-Destination matrices has been used, due to the ease of obtaining updated information quickly and constantly. A methodology has been developed to transform the number of travelers into an estimated population without biases, and an interpolation function has been used to take into account all the data available in the year 2021. Results show a direct link between the demographic changes in Andalusia and the removal of the mobility restrictions caused by the COVID-19 pandemic, with an increase of non-related work mobility and a decrease of static population. Travel distances between home and work places are also affected, with an increase of long trips after the end of the mobility restrictions. In addition, different patterns have been visualized, such as the concentration of commuting in the metropolitan areas of the region during working days, the population growth in rural areas during weekends, or the population displacement to coastal areas in summer.

A study on the influencing factors and response strategies for young teachers from Taiwan to teach in universities in China: a push-pull-mooring model perspective

Introduction

A growing number of Taiwanese teachers are choosing to teach at universities in mainland China, but their jobs are not always stable. Therefore, this study aims to investigate the factors infuencing young teachers from Taiwan to teach in universities in China.

Methods

Twenty-seven young teachers from Taiwan with master's or doctoral degrees who were willing to apply to work at universities in China and who were already teaching in China were invited to conduct in-depth interviews to collect research data.The interview data were coded and analyzed according to the Push-Pull-Mooring (PPM) Model.

Results and discussion

The results showed that the understanding of mainland Chinese universities among young Taiwanese teachers is not entirely consistent. Taiwanese teachers who previously studied in mainland China have a more comprehensive understanding of mainland Chinese universities, and some teachers have gained a superfcial understanding through academic exchanges between the two sides and information shared by friends.However,still,7% of the teachers have no understanding at all. Most young Taiwanese teachers indicate that they do not understand the talent recruitment policies of mainland Chinese universities. The push factors that infuence young teachers from Taiwan to teach at mainland universities are: Oversupply of teachers in Taiwan, poor environment for higher education in Taiwan, poor articulation of the cross-strait academic system, and four aspects of teacher retirement and re-employment in Taiwan. The pull factors are: Benefcial policies, salary, living environment, educational advantages and cultural dissemination in 5 areas. Mooring factors are divided into 3 aspects: personal factors, environmental factors and social factors.

How do urban socio-economic characteristics shape a city's social recovery? An empirical study of COVID-19 shocks in China

The COVID-19 pandemic outbreak significantly challenged the cities' abilities to recover from shocks, and cities' responses have widely differed. Understanding these disparate responses has been insufficient, especially from a social recovery perspective. In this study, we propose the concept of social recovery and develop a comprehensive perspective on how a city's socioeconomic characteristics affect it. The analytical framework is applied to 296 prefecture-level cities in China, with social recovery measured by the changes in intercity intensity between the pre-pandemic baseline (2019 Q1 and Q2) and the period in which the pandemic slightly abated (2020 Q1 and Q2) through anonymized location-based big data. The results indicate that the social recovery of Chinese cities during the COVID-19 pandemic are significantly spatially correlated. Cities with larger populations, a higher proportion of GDP in the secondary industry, higher road density or more adequate medical resources tend to recover socially better. Moreover, these municipal characteristics have significant spatial spillover effects. Specifically, city size, government intervention and industrial structure show negative spillover effects on neighboring areas while information dissemination efficiency, road density, and the number of community health services per capita have positive spillover. This study fills the knowledge gap regarding the different performances of cities when they face pandemic shocks. The assessment of a city's social recovery is an insight into the theoretical framework of vulnerability that aids in translating it into urban resilience. Hence our findings provide practice implications for China and beyond as the interest in urban-resilience development surges around the post-pandemic world.

Exploring the raison d'etre behind metric selection in network analysis: a systematic review

Network analysis is a useful tool to analyse the interactions and structure of graphs that represent the relationships among entities, such as sectors within an urban system. Connecting entities in this way is vital in understanding the complexity of the modern world, and how to navigate these complexities during an event. However, the field of network analysis has grown rapidly since the 1970s to produce a vast array of available metrics that describe different graph properties. This diversity allows network analysis to be applied across myriad research domains and contexts, however widespread applications have produced polysemic metrics. Challenges arise in identifying which method of network analysis to adopt, which metrics to choose, and how many are suitable. This paper undertakes a structured review of literature to provide clarity on raison d'etre behind metric selection and suggests a way forward for applied network analysis. It is essential that future studies explicitly report the rationale behind metric choice and describe how the mathematics relates to target concepts and themes. An exploratory metric analysis is an important step in identifying the most important metrics and understanding redundant ones. Finally, where applicable, one should select an optimal number of metrics that describe the network both locally and globally, so as to understand the interactions and structure as holistically as possible.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-022-00476-w.

Identification of Metropolitan Area Boundaries Based on Comprehensive Spatial Linkages of Cities: A Case Study of the Beijing–Tianjin–Hebei Region

As a regional management unit to solve "urban diseases,” metropolitan areas are gradually attracting widespread attention. How to objectively and accurately delineate the boundaries of a metropolitan area is the primary prerequisite for carrying out targeted studies and precisely formulating regional planning measures. However, the existing methods for delineating metropolitan area boundaries have problems, such as high data acquisition costs, subjectivity, and a single perspective of urban linkage. To address the above problems, we propose a “bottom-up” approach to metropolitan area boundary delineation based on urban comprehensive spatial linkages. We used only publicly available data to construct a directionally weighted network of urban spatial linkages, and applied community detection algorithms to delineate metropolitan area boundaries. Taking the Beijing–Tianjin–Hebei region as a case study area, the method’s validity was confirmed. The results showed the following: (1) Eight metropolitan areas were delineated within the region, with two types of metropolitan areas: “Inter-municipal” and “single-city”. (2) The overall accuracy of the delineation results reached 83.41%, which is highly consistent with their corresponding isochrone maps. (3) Most metropolitan areas were observed to have an obvious “central–peripheral” structure, with only the JingJinLang metropolitan area being a polycentric mature metropolitan area, whereas the other metropolitan areas remained in the initial stage of development, with Zhangjiakou and Chengde not yet having formed metropolitan areas. This study’s methodology highlights the basic criteria of “inter-city spatial linkage” as the foundation for boundary delineation, avoiding the inaccuracy caused by the subjective selection of boundary thresholds, and can also accurately determine the developmental stage and internal spatial structure of metropolitan areas. Our method can provide new perspectives for regional boundary delineation and spatial planning policy formulation.

The Impact of Place of Origin on International and Domestic Graduates’ Mobility in China

This article addresses the cumulative effect of graduate migration and opportunities for career development. Using data from an online survey of 756 master’s-level graduates educated in China and the UK, it examines their geographical mobility patterns and reveals significant differences between Chinese students who graduated from domestic universities and those who were educated abroad. Spatial autocorrelation analysis shows that international returnees, who usually had more privileged family backgrounds, clustered in China’s highly developed core cities of the Bohai Economic Rim and Yangtze River Delta regions, such as Beijing and Shanghai, while domestic graduates tended to work and live in less affluent medium-sized cities around these regions. Women international graduates were more mobile than their men counterparts. Our results provide new evidence that draws attention to migration’s role in graduate career development opportunities and highlights inherent economic discrimination within China, which is perpetuated by the national residency permit system — Hukou. The case of Chinese graduates shows that the mobility patterns of international and domestic graduates are influenced by and contribute to growing regional inequalities for career development in China.

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.

Big data insight on global mobility during the Covid-19 pandemic lockdown

The Covid-19 pandemic that began in the city of Wuhan in China has caused a huge number of deaths worldwide. Countries have introduced spatial restrictions on movement and social distancing in response to the rapid rate of SARS-Cov-2 transmission among its populations. Research originality lies in the taken global perspective revealing indication of significant relationships between changes in mobility and the number of Covid-19 cases. The study uncovers a time offset between the two applied databases, Google Mobility and John Hopkins University, influencing correlations between mobility and pandemic development. Analyses reveals a link between the introduction of lockdown and the number of new Covid-19 cases. Types of mobility with the most significant impact on the development of the pandemic are "retail and recreation areas", "transit stations", "workplaces" "groceries and pharmacies". The difference in the correlation between the lockdown introduced and the number of SARS-COV-2 cases is 81%, when using a 14-day weighted average compared to the 7-day average. Moreover, the study reveals a strong geographical diversity in human mobility and its impact on the number of new Covid-19 cases.

Spatiotemporal Exploration of Chinese Spring Festival Population Flow Patterns and Their Determinants Based on Spatial Interaction Model

Large-scale population flow reshapes the economic landscape and is affected by unbalanced urban development. The exploration of migration patterns and their determinants is therefore crucial to reveal unbalanced urban development. However, low-resolution migration datasets and insufficient consideration of interactive differences have limited such exploration. Accordingly, based on 2019 Chinese Spring Festival travel-related big data from the AMAP platform, we used social network analysis (SNA) methods to accurately reveal population flow patterns. Then, with consideration of the spatial heterogeneity of interactive patterns, we used spatially weighted interactive models (SWIMs), which were improved by the incorporation of weightings into the global Poisson gravity model, to efficiently quantify the effect of socioeconomic factors on migration patterns. These SWIMs generated the local characteristics of the interactions and quantified results that were more regionally consistent than those generated by other spatial interaction models. The migration patterns had a spatially vertical structure, with the city development level being highly consistent with the flow intensity; for example, the first-level developments of Beijing, Shanghai, Chengdu, Guangzhou, Shenzhen, and Chongqing occupied a core position. A spatially horizontal structure was also formed, comprising 16 closely related city communities. Moreover, the quantified impact results indicated that migration pattern variation was significantly related to the population, value-added primary and secondary industry, the average wage, foreign capital, pension insurance, and certain aspects of unbalanced urban development. These findings can help policymakers to guide population migration, rationally allocate industrial infrastructure, and balance urban development.

Role of Environmental Temperature on the Attack rate and Case fatality rate of Coronavirus Disease 2019 (COVID-19) Pandemic

SARS-CoV-2 is a zoonotic Betacoronavirus causing the devastating COVID-19 pandemic. More than twelve million COVID-19 cases and 500 thousand fatalities have been reported in 216 countries. Although SARS-CoV-2 originated in China, comparatively fewer people have been affected in other Asian countries than in Europe and the USA. This study examined the hypothesis that lower temperature may increase the spread of SARS-CoV-2 by comparing attack rate and case fatality rate (until 21 March 2020) to mean temperature in January-February 2020. The attack rate was highest in Luxembourg followed by Italy and Switzerland. There was a significant (p = 0.02) correlation between decreased attack rate and increased environmental temperature. The case fatality rate was highest in Italy followed by Iran and Spain. There was no significant correlation between the case fatality rate and temperature. This study indicates that lower temperature may increase SARS-CoV-2 transmission (measured as an increased attack rate), but there is no evidence that temperature affects the severity of the disease (measured as case fatality rate). However, there are clearly other factors that affect the transmission of SARS-CoV-2, and many of these may be sensitive to interventions, e.g. through increased public awareness and public health response.