[1] J Bao, T He, S Ruan et al. Planning bike lanes based on sharing-bikes’ trajectories. In: KDD ’17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 1377-1386(2017).

[2] P Barbera, G Rivero. Understanding the political representativeness of Twitter users. Social Science Computer Review, 33, 712-729(2014).

[3] S E Bauer, S E Wagner, J Burch et al. A case-referent study: Light at night and breast cancer risk in Georgia. International Journal of Health Geographics, 12, 23(2013).

[4] J Blumenstock, G Cadamuro, R On. Predicting poverty and wealth from mobile phone metadata. Science, 350, 1073-1076(2015).

[5] Y M Chen, B B Wang, X P Liu et al. Mapping the spatial disparities in urban health care services using taxi trajectories data. Transactions in GIS, 22, 602-615(2018).

[6] Z Q Chen,, B L Yu, Y J Hu et al. Estimating house vacancy rate in metropolitan areas using NPP-VIIRS nighttime light composite data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2188-2197(2015).

[7] . More than 60 drug-related vehicles were seized and 15 people were forcibly detoxified in Wuhan, Hubei province(2017). http://news.cctv.com/2017/08/26/ARTIOTOcTGJBrguuAH58pC0U170826.shtml

[8] M Diao, Y Zhu, Jr J Ferreira et al. Inferring individual daily activities from mobile phone traces: A Boston example. Environment and Planning B-Planning & Design, 43, 920-940(2015).

[9] C D Elvidge, K E Baugh, E A Kihn et al. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18, 1373-1379(1997).

[10] J Fan. Perspective on the development process of Human-Economic Geography and regional development studies: On the evolution of the field in the Institute of Geographic Sciences and Natural Resources Research of CAS. Progress in Geography, 30, 387-396(2011).

[11] Q Gao, Q Li, Y Yue et al. Exploring changes in the spatial distribution of the low-to-moderate income group using transit smart card data. Computers, Environment & Urban Systems, 72, 68-77(2018).

[12] T Gebru, J Krause, Y Wang et al. Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proceedings of the National Academy of Sciences of the United States of America, 114, 13108-13113(2017).

[13] R G Golledge, R J Stimson. Spatial Behavior: A Geographic Perspective, 620(1997).

[14] C Y He, Z F Liu, J Tian et al. Urban expansion dynamics and natural habitat loss in China: A multiscale landscape perspective. Global Change Biology, 20, 2886-2902(2014).

[15] T F He, J Bao, R Y Li et al. Detecting vehicle illegal parking events using sharing bikes’ trajectories. In: KDD ‘18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: ACM, 340-349(2018).

[16] M Helbich, Y Yao, Y Liu et al. Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Environment International, 126, 107-117(2019).

[17] N Jean, M Burke, M Xie et al. Combining satellite imagery and machine learning to predict poverty. Science, 353, 790-794(2016).

[18] C Kang, S Sobolevsky, Y Liu et al. Exploring human movements in Singapore: A comparative analysis based on mobile phone and taxicab usages. In: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Chicago, IL(2013).

[19] D Lazer, A Pentland, L Adamic et al. Computational social science. Science, 323, 721-723(2009).

[20] D Li. On the formation of geomatics and its cross-century development. World Sci-Tech R & D, 5, 1-8(1996).

[21] D Li, X Li. An overview on data mining of nighttime light remote sensing. Acta Geodaeticaet Cartographica Sinica, 44, 591-601(2015).

[22] D Li, J Ma, Z Shao. The application of spatial temporal big data. Satellite Application, 9, 7-11(2015).

[23] D Li, X Zhao, X Li. Remote sensing of human beings: A perspective from nighttime light. Geo-spatial Information Science, 19, 69-79(2016).

[24] X Li, D Li. Can night-time light images play a role in evaluating the Syrian Crisis?. International Journal of Remote Sensing, 35, 6648-6661(2014).

[25] X Li, S Liu, M Jendryke et al. Night-time light dynamics during the Iraqi Civil War. Remote Sensing, 10, 858(2018). https://doi.org/10.3390/rs10060858

[26] P A Longley, M F Goodchild, D J Maguire et al. Geographic Information Systems: Principles, Techniques, Management and Applications. 2nd ed. New York: John Wiley & Sons, 401(2005).

[27] L McDowell. Understanding diversity: The problem of theory. In: Johnston R J, Taylor P J, Watts M J (eds.), Geographies of Global Change: Remapping the World in the Late Twentieth Century. Oxford: Blackwell, 280-294(1995).

[28] D Schuler. Social computing. Communications of the ACM, 37, 28-29(1994).

[29] A Signorini, A M Segre, P M Polgreen. The use of Twitter to track levels of disease activity and public concern in the U.S. during the Influenza A H1N1 pandemic. PloS One, 6, e19467(2011).

[30] W Tu, J Cao, Y Yue et al. Coupling mobile phone and social media data: A new approach to understanding urban functions and diurnal patterns. International Journal of Geographical Information Science, 31, 2331-2358(2017).

[31] Y D Wang, H Li, T Wang et al. Modeling urban air quality trend surface using media data. Geomatics and Information Science of Wuhan University, 42, 14-20(2017).

[32] R E Williams. Selling a geographical information system to government policy makers. Papers from the 1987 Annual Conference of the Urban and Regional Information Systems Association. Des Plaines, IL: Urisa(1987).

[33] F W Witmer, J O Loughlin. Detecting the effects of wars in the Caucasus regions of Russia and Georgia using radiometrically normalized DMSP-OLS nighttime lights imagery. GIScience & Remote Sensing, 48, 478-500(2011).

[34] Remote sensing research on the Syrian Conflict has attracted high attention from the global media. Retrieved on March 16, 2015(2015). http://news.whu.edu.cn/info/1002/42972.htm

[35] L Xiao, L Liu, G Song et al. Journey-to-crime distances of residential burglars in China disentangled: Origin and destination effects. International Journal of Geo-Information, 7, 325(2018).

[36] Y Xiao, D Wang, . Exploring the disparities in park access through mobile phone data: Evidence from Shanghai, China. Landscape and Urban Planning, 181, 80-91(2019).

[37] H R Yang, F Dobruszkes, J E Wang et al. Comparing China’s urban systems in high-speed railway and airline networks. Journal of Transport Geography, 68, 233-244(2018).

[38] L Yin, Q M Cheng, Z X Wang et al. ‘Big data’ for pedestrian volume: Exploring the use of Google Street View images for pedestrian counts. Applied Geography, 63, 337-345(2015).

[39] B L Yu, T Lian, Y X Huang et al. Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement. International Journal of Geographical Information Science, 33, 687-706(2019).

[40] B L Yu, S Shu, H X Liu et al. Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: A case study of China. International Journal of Geographical Information Science, 28, 2328-2355(2014).

[41] H Yu, S-L Shaw. Exploring potential human activities in physical and virtual spaces: A spatio-temporal GIS approach. International Journal of Geographical Information Science, 22, 409-430(2008).

[42] Y Yuan, J Liu, Y M Chen et al. Poverty measurement of urban internal space based on remote sensing images and online rental information: A case study of the city core of Guangzhou. Human Geography, 33, 60-67(2018).

[43] B J Zhang. Analysis of the inter-annual variation of nighttime lights in the most affected area of Wenchuan earthquake from 2003 to 2013. Journal of Catastrophology, 33, 12-18(2018).

[44] Y L Zhang, R C Dong. Impacts of street-visible greenery on housing prices: Evidence from a hedonic price model and a massive street view image dataset in Beijing. International Journal of Geo-Information, 7, 104(2018).

[45] X Z Zhao, B L Yu, Y Liu et al. NPP-VIIRS DNB daily data in natural disaster assessment: Evidence from selected case studies. Remote Sensing, 10, 1526(2018).

[46] Y Zheng, L Capra, O Wolfson et al. Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 5, 38(2014). http://dx.doi.org/10.1145/2629592