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.

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

Focal mechanism and dynamic rupture process of the Wenchaun M s 8.0 earthquake in Sichuan province on 12 May 2008 were obtained by inverting long period seismic data from the Global Seismic Network (GSN), and characteristics of the co-seismic displacement field near the fault were quantitatively analyzed based on the inverted results to investigate the mechanism causing disaster. A finite fault model with given focal mechanism and vertical components of the long period P-waves from 21 stations with evenly azimuthal coverage were adopted in the inversion. From the inverted results as well as aftershock distribution, the causative fault of the great Wenchuan earthquake was confirmed to be a fault of strike 225°/dip 39°/rake 120°, indicating that the earthquake was mainly a thrust event with right-lateral strike-slip component. The released scalar seismic moment was estimated to be about 9.4×10 20 -2.0×10 21 Nm, yielding moment magnitude of M w 7.9-8.1. The great Wenchuan earthquake occurred on a fault more than 300 km long, and had a complicated rupture process of about 90 s duration time. The slip distribution was highly inhomogeneous with the average slip of about 2.4 m. Four slip-patches broke the ground surface. Two of them were underneath the regions of Wenchuan-Yingxiu and Beichuan, respectively, with the first being around the hypocenter (rupture initiation point), where the largest slip was about 7.3 m, and the second being underneath Beichuan and extending to Pingwu, where the largest slip was about 5.6 m. The other two slip-patches had smaller sizes, one having the maximum slip of 1.8 m and lying underneath the north of Kangding, and the other having the maximum slip of 0.7 m and lying underneath the northeast of Qingchuan. Average and maximum stress drops over the whole fault plane were estimated to be 18 MPa and 53 MPa, respectively. In addition, the co-seismic displacement field near the fault was analyzed. The results indicate that the features of the co-seismic displacement field were coincident with those of the intensity distribution in the meizoseismal area, implying that the large-scale, large-amplitude and surface-broken thrust dislocation should be responsible for the serious disaster in the near fault area.Wenchuan earthquake, earthquake rupture process, co-seismic displacement As reported by China Seismograph Network Center (CSNC), an earthquake of M s 8.0 occurred near Yingxiu town (31.0°N, 103.4°E, focal depth: 15 km) of Wenchuan County, Sichuan Province, at 14: 28: 04 (Beijing Time), 12 May 2008. The earthquake resulted in large-scale landslides and debris flows, silting of rivers, and more than 3000 barrier lakes (Satellite images in Figures 1 (a), (b) and (c)), and seriously damaged more than one hundred of cities and towns. A large number of buildings, including houses, roads and bridges (Satellite images in Figures 1(d) and (e)), were destroyed or collapsed, causing nearly 90000 dead and missing.