Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers-Big Data and cloud computingand reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.

The rapid development of digital platform businesses has facilitated the expansion of gig work in China and elsewhere in recent years. Now that IT-powered platforms have been used in part to free the capital from taking employer responsibilities, the capital’s toolkit for labor control has been significantly limited. Drawing on qualitative field research supplemented by quantitative data on Uber in China, this article provides a novel empirical account of the labor control of digital platforms, and more importantly, their effects on different types of workers. The authors have identified three crucial strategies that Uber has devised to control its drivers’ labor process: an incentive pay system, a customer evaluation system, and flexible work arrangements. These strategies will, however, demonstrate significant effects on drivers’ working hours and income only when we consider the different motivations of Uber drivers. Specifically, the working efforts of those who drive for Uber as their only source of income are responsive to incentive pay schemes and a platform’s evaluation system, but are not as responsive to work flexibility. The exact opposite is the case for drivers who have other jobs and sources of income.

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Along with China becoming an upper-middle-income country from a lowermiddle-income one after 2009, the happiness inequality in China has been enlarged. Based on the Chinese General Social Survey (CGSS) database (2003-2012), this paper investigates the determinants of the happiness inequality in China and explores what factors contribute to its enlargement after 2009. We find that a rise of income inequality as well as the population share of middle age cohorts can widen China's happiness inequality, while an increase in income or education level has a reducing impact. Owning a house and being in employment also have happiness inequality reducing impacts. A decomposition analysis shows that the deterioration of China's happiness inequality is mainly caused by coefficient effects, i.e., the relationships between happiness inequality and its influencing factors have changed, which reflects the dramatic change in the Chinese economy and society. Among the coefficient effects, regional heterogeneity plays an important role. Policies enhancing economic performance and education as well as reducing income inequality and regional inequality can help to reduce happiness inequality and improve social harmony in China.

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