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This research combines “big data” and “thick data” approaches to examine the correlation and causation between residential neighborhood features and people’s daily commuting and traveling patterns by integrating two datasets: household survey data and mobile phone data. We focus on “lilong” neighborhoods—a primary form of traditional residential neighborhood in central Shanghai. The characteristics of lilong neighborhoods are assessed using “thick data” from surveys in 105 lilongs, while residents’ daily activities are mapped out using “big data” from two weeks of mobile phone usage. We match these two datasets at neighborhood level based on their geospatial references. Four multinomial logistic regression models are developed to examine neighborhood effects on lilong residents’ daily activities. Our research confirms the major mechanisms of neighborhood effects and unravels their relative importance in shaping the patterns of residents’ daily activities. Conceptually, this study sheds new light on the understanding of how people’s life quality and wellbeing are affected by neighborhood characteristics through highlighting the importance of social interactions and the access to/quality of public facilities. Methodologically, incorporating household survey data (thick data) and mobile phone data (big data) is proven to be a novel and effective approach for examining neighborhood effects at a relatively large scale.

Understanding the spatiotemporal variation of high-efficiency ride-hailing orders (HROs) is helpful for transportation network companies (TNCs) to balance the income of drivers through reasonable order dispatch, and to alleviate the imbalance between supply and demand by improving the pricing mechanism, so as to promote the sustainable and healthy development of the ride-hailing industry and urban transportation. From the perspective of TNCs for order management, this study investigates the spatiotemporal variation of HROs and common ride-hailing orders (CROs) for ride-hailing services using the trip data of Didi Chuxing in Haikou, China. Ordinary least squares (OLS) and geographically weighted regression (GWR) models are established to examine the factors that affect the densities of HROs and CROs during different time periods, such as morning, evening, afternoon and night, with considering various built environment variables. The OLS models show that factors including road density, average travel time rate, companies and enterprises and transportation facilities have significant impacts on HROs and CROs for most periods. The results of the GWR models are consistent with the global regression results and show the local effects of the built environment on HROs and CROs in different regions.

Funding: National Natural Science Foundation of China (41925003), Beijing Social Science Foundation (18JZD029), UKRI’s Global Challenge Research Fund (No. ES/P011055/1) and Ministry of Education Key Projects of Philosophy and Social Sciences Research (No. 18JZD029). The outbreak of COVID-19 in China started at the end of December 2019. This led to a series of containment measurements to control the spread of COVID-19. Despite of the widely reported effects of these measures, inadequate attention has gone to their social impacts. The elderly, as one of the most susceptible populations, has experienced a considerable reduction in mobility. This paper explores the role mobility played and how the social environment influenced elderly mobility in the first 2 months of the COVID-19 outbreak. We surveyed 186 families with a total of 248 elderly people in Kunming. The results show that mobility improves the quality of daily living, such as access to grocery shopping, maintenance of outdoor activities for health cultivation and preserving social networks even during the pandemic. Four themes relating to social environment emerged from the data as elements influencing elderly mobility during the pandemic: social pressure, practice of the virtue of Xiao, the social norm of respecting the aged and the impacts of technological advances. Among them, the virtue of Xiao enabled the elderly to stay in place in the early phase of COVID-19 by fulfilling their needs for daily necessities and social interactions, whilst being less technology-savvy further excluded them socially by restraining them from restoring mobility after the lifting of travel restrictions. Postprint

Knowing how workers return to work is a key policymaking issue for economic recovery in the post-COVID-19 era. This paper uses country-wide time-series mobile phone big data (comparing monthly and annual figures), obtained between February 2019 and October 2019 and between February 2020 and October 2020, to discover the spatial patterns of rural migrant workers’ (RMWs’) return to work in China’s three urban agglomerations (UAs): the Beijing–Tianjin–Hebei Region, the Yangtze River Delta and the Pearl River Delta. Spatial patterns of RMWs’ return to work and how these patterns vary with location, city level and human attribute were investigated using the fine-scale social sensing related to post-pandemic human mobility. The results confirmed the multidimensional spatiotemporal differentiations, interaction effects between variable pairs and effects of the actual situation on the changing patterns of RMWs’ return to work. The spatial patterns of RMWs’ return to work in China’s major three UAs can be regarded as a comprehensive and complex interaction result accompanying the nationwide population redistribution, which was affected by various hidden factors. Our findings provide crucial implications and suggestions for data-informed policy decisions for a harmonious society in the post-COVID-19 era.

Since many parents travel separately for escorting and commuting, certain hidden daily car trips may have been ignored in previous research regarding parental escort behaviors. By defining an escort-space model using the spatial relationships between home, the workplace, and school, this study focuses on the daily modal split among parental chauffeurs using data from Qujing, China, while focusing on the effects of different escort-space models: spatial aggregation, job-housing separation and school-housing separation. The descriptive statistics of parental chauffeurs’ travel mode choices under the influences of these three escort-space models are presented. The statistical results demonstrate that the modal splits of parental chauffeurs perform significantly differently under these three escort-space models. Furthermore, the determinants of the daily travel mode of parental chauffeurs, including escort-spaces and other selected variables, are investigated using a multinomial logit model. A model without the escort-space model is also presented for comparison. The results show that the model with the escort-space model has a more significant goodness-of-fit than the model without the escort-space model. Both the job-housing separation and school-housing separation of parental chauffeurs result in the increase of car trips, while the usage amount of car in daily journeys is higher than that in escort trips. Moreover, car ownership, bike ownership, household income, residential location, age, gender, income, and education level all significantly impact the daily travel mode choices of parental chauffeurs. These findings can help policymakers create suitable policies to reduce excessive car trips by parental chauffeurs.

Egalitarian thought has a long tradition in Chinese history. Synthesized with the socialist ideology, it was practiced nationwide in the first decades of Communist China. Alongside the well-reported quality of life improvements that the recent economic liberalisation reforms have brought about, Chinese people are experiencing an increasingly serious polarization between the rich and the poor. Consequently, an egalitarian tendency has represented itself within contemporary policy and popular discourse. This paper aims to explore how egalitarian thought has influenced public awareness of social inequities by using the case of public attitudes towards the Beijing congestion charge, which is currently still at its planning stage. Nine focus groups, with a total of 73 participants, were undertaken with residents living in different areas of the city. Results show that the perceptions of social inequities are significantly different between low-income and high-income people. Due to egalitarian thinking, low-income people expect the privileges of the rich to be abolished, however, they do not pay much attention to a wider redistribution of wealth and other social resources. By contrast, richer people tend to deny to a wider population the privileges they themselves received, and, to some extent, they are reluctant to accept policy outcomes that may favour those who are in lower social positions. The resentment against the rich, as another by-product of egalitarianism, considerably exaggerates actual social inequalities, thereby intensifying the feelings of being unequally treated.