a b s t r a c tThe outbreak of the 2019 novel coronavirus disease has caused more than 100,000 people to be infected and has caused thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations, and has caused widespread concern around the globe. In the fight against COVID-19, geographic information systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multisource big data, rapid visualization of epidemic information, spatial tracking of COVID-19, prediction of regional transmission, identification of the spatial allocation of risk and selection of the control level, balance and management of the supply and demand of medical resources, social-emotional guidance and panic elimination, the provision of solid spatial information support for decision-making about COVID-19 prevention and control, measures formulation, and assessment of the effectiveness of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against COVID-19, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy to provide accurate information for rapid social management. Additionally, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.

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43Land use classification is essential for urban planning. Urban land use types can be 44 differentiated either by their physical characteristics (such as reflectivity and texture) 45 or social functions. Remote sensing techniques have been recognized as a vital 46 method for urban land use classification because of their ability to capture the 47 physical characteristics of land use. Although significant progress has been achieved 48 in remote sensing methods designed for urban land use classification, most techniques 49 focus on physical characteristics, whereas knowledge of social functions is not 50 adequately used. Owing to the wide usage of mobile phones, the activities of residents, 51 which can be retrieved from the mobile phone data, can be determined in order to 52 indicate the social function of land use. This could bring about the opportunity to 53 derive land use information from mobile phone data. To verify the application of this 54 new data source to urban land use classification, we first construct a time series of 55 aggregated mobile phone data to characterize land use types. This time series is 56composed of two aspects: the hourly relative pattern, and the total call volume. A 57 semi-supervised fuzzy c-means clustering approach is then applied to infer the land 58 use types. The method is validated using mobile phone data collected in Singapore. 59Land use is determined with a detection rate of 58.03%. An analysis of the land use 60 classification results shows that the accuracy decreases as the heterogeneity of land 61 use increases, and increases as the density of cell phone towers increases. 62

Inadequate water quality can mean that water is unsuitable for a variety of human uses, thus exacerbating freshwater scarcity. Previous large-scale water scarcity assessments mostly focused on the availability of sufficient freshwater quantity for providing supplies, but neglected the quality constraints on water usability. Here we report a comprehensive nationwide water scarcity assessment in China, which explicitly includes quality requirements for human water uses. We highlight the necessity of incorporating water scarcity assessment at multiple temporal and geographic scales. Our results show that inadequate water quality exacerbates China's water scarcity, which is unevenly distributed across the country. North China often suffers water scarcity throughout the year, whereas South China, despite sufficient quantities, experiences seasonal water scarcity due to inadequate quality. Over half of the population are affected by water scarcity, pointing to an urgent need for improving freshwater quantity and quality management to cope with water scarcity.

Understanding the spatio-temporal dynamics of urban development at regional and global scales is increasingly important for urban planning, policy decision making and resource use and conservation. Continuous satellite derived observations of anthropogenic lighting signal at night provide consistent and efficient proxy measures of demographic and socioeconomic dynamics in the urbanization process. Previous studies have demonstrated significant positive correlations between the nocturnal light brightness, mainly derived from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS), and population and economic variables. Quantitative measurements of artificial lighting emissions at night therefore can be indicative of the overall degree of socioeconomic development at regional to country levels. The spatio-temporal characteristics of anthropogenic night-time lighting, potentially connected to the dynamic patterns of spatially expanding human settlement and economic activities during the urban expansion process, however, has received less attention largely because of diversity of both socioeconomic activity and urban forms. Based upon the quadratic relationship between the pixel-level night-time light radiance and corresponding brightness gradient (i.e. the rate of maximum local change) at the local scale, we here proposed a spatially explicit approach for partitioning DMSP/OLS night-time light images into five types of night-time lighting areas for individual cities: low, medium-low, medium, medium-high and high, generally associated with urban sub-areas experienced distinctly different forms and human activity. At the country scale, our findings suggest that significant rises are commonly found in these five types of night-time lighting areas with different growth rates across 271 China's cities from 1992 to 2012. At the urban scale, however, five types of night-time lighting areas show various trends for individual cities in relation to the urban size and development levels. The marked increase in high night-time lighting area is highly prevalent in most of China's cities with rapid urbanization over the past 21 years while significantly decreased low and medium-low night-time lighting areas are most likely to occur in large and extra-large cities. Moreover, the transition between different types of night-time lighting areas could further portray the spatiotemporal characteristics of urban development. Analyzing results indicate that the spatial expansions of gradually intensified night-time light brightness correspond geographically with the rural-urban gradients following a stepwise transition of night-time light brightness during the urban expansion.

Remotely sensed measurements of anthropogenic nocturnal lighting have been extensively used for studying human settlements and socioeconomic dynamics. Considerable efforts have been devoted to build the connections between nightlight signals and demographic and economic variables at local to global scales in order to obtain observationally based estimates for human activity. Recently, the first cloud-free composite of global nighttime light data derived from the Suomi National Polar orbiting Partnership (Suomi-NPP) with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor yields an increasingly clearer view of the earth surface on nights. The responses to socioeconomic activity and the potential utility of VIIRS nighttime light data, however, still remain less well understood. In this study, we examined the quantitative relationships between VIIRS nightlight-derived indices and socioeconomic variables at fine and local scales. Our results suggest that the total night radiance has significant positive associations with two airport performance indicators -passenger traffic and aircraft movement. In addition, there is a strong correlation with four urbanization variables -human population, gross domestic product, electric power consumption and paved road area. Our findings suggest that VIIRS nightlight data could be more indicative of socioeconomic dynamics and may provide insights into the potential applications for studying human settlement and urbanization processes based on anthropogenic nocturnal lighting.

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