[1] Ahas R, Aasa A, Yuan Y et al., 2015. Everyday space-time geog-raphies:using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn. International Journal of Geographical Information Science, 29(11):2017-2039. doi: 10.1080/13658816.2015.1063151
[2] Alexander L, Jiang S, Murga M et al., 2015. Origin-destination trips by purpose and time of day inferred from mobile phone data. Transportation Research Part C:Emerging Technologies, 58:240-250. doi: 10.1016/j.trc.2015.02.018
[3] Breaban M, Luchian H, 2011. A unifying criterion for unsuper-vised clustering and feature selection. Pattern Recognition, 44(4):854-865. doi: 10.1016/j.patcog.2010.10.006
[4] Calabrese F, Colonna M, Lovisolo P et al., 2011. Real-time urban monitoring using cell phones:a case study in Rome. IEEE Transactions on Intelligent Transportation Systems, 12(1):141-151. doi: 10.1109/tits.2010.2074196
[5] Calabrese F, Diao M, Di Lorenzo G et al., 2013. Understanding individual mobility patterns from urban sensing data:a mobile phone trace example. Transportation Research Part C:Emerging Technologies, 26:301-313. doi:10.1016/j.trc.2012. 09.009
[6] Calabrese F, Ferrari L, Blondel V D, 2015. Urban sensing using mobile phone network data:a survey of research. ACM Com-puting Surveys, 47(2):Article No. 25. doi: 10.1145/2655691
[7] Chen C, Ma J T, Susilo Y et al., 2016. The promises of big data and small data for travel behavior (aka human mobility) anal-ysis. Transportation Research Part C:Emerging Technologies, 68:285-299. doi: 10.1016/j.trc.2016.04.005
[8] Deville P, Linard C, Martin S et al., 2014. Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences of the United States of America, 111(45):15888-15893. doi: 10.1073/pnas.1408439111
[9] Diao M, Zhu Y, Ferreira J et al., 2016. Inferring individual daily activities from mobile phone traces:a Boston example. Envi-ronment and Planning B:Planning and Design, 43(5):920-940. doi: 10.1177/0265813515600896
[10] Dong H H, Wu M C, Ding X Q et al., 2015. Traffic zone division based on big data from mobile phone base stations. Transpor-tation Research Part C:Emerging Technologies, 58:278-291. doi: 10.1016/j.trc.2015.06.007
[11] Doyle J, Hung P, Farrell R et al., 2014. Population mobility dy-namics estimated from mobile telephony data. Journal of Urban Technology, 21(2):109-132. doi:10.1080/10630732. 2014.888904
[12] Epperlein J, Legierski J, Luckner M et al., 2018. The use of pres-ence data in modelling demand for transportation. arXiv:1802.03734. Available at:http://arxiv.org/abs/1802.03734.
[13] Ewing R, Hamidi S, 2015. Compactness versus sprawl:a review of recent evidence from the United States. Journal of Planning Literature, 30(4):413-432. doi: 10.1177/0885412215595439
[14] Gao S, 2015. Spatio-temporal analytics for exploring human mo-bility patterns and urban dynamics in the mobile age. Spatial Cognition and Computation, 15(2):86-114. doi:10.1080/1387 5868.2014.984300
[15] González M C, Hidalgo C A, Barabási A L, 2008. Understanding individual human mobility patterns. Nature, 453(7196):779-782. doi: 10.1038/nature06958
[16] Gordon P, Richardson H W, 1997. Are compact cities a desirable planning goal? Journal of the American Planning Association, 63(1):95-106. doi: 10.1080/01944369708975727
[17] Iqbal M S, Choudhury C F, Wang P et al., 2014. Development of origin-destination matrices using mobile phone call data. Transportation Research Part C:Emerging Technologies, 40:63-74. doi: 10.1016/j.trc.2014.01.002
[18] Järv O, Ahas R, Saluveer E et al., 2012. Mobile phones in a traffic flow:a geographical perspective to evening rush hour traffic analysis using call detail records. PLoS ONE, 7(11):e49171. doi: 10.1371/journal.pone.0049171
[19] Järv O, Ahas R, Witlox F, 2014. Understanding monthly variabil-ity in human activity spaces:a twelve-month study using mobile phone call detail records. Transportation Research Part C:Emerging Technologies, 38:122-135. doi:10.1016/j.trc. 2013.11.003
[20] Järv O, Tenkanen H, Toivonen T, 2017. Enhancing spatial accu-racy of mobile phone data using multi-temporal dasymetric interpolation. International Journal of Geographical Infor-mation Science, 31(8):1630-1651. doi:10.1080/13658816. 2017.1287369
[21] Jiang H, Li Q, Zhou X et al., 2017. A collective human mobility analysis method based on data usage detail records. Interna-tional Journal of Geographical Information Science, 31(12):2359-2381. doi: 10.1080/13658816.2017.1370715
[22] Kang C G, Ma X J, Tong D Q et al., 2012. Intra-urban human mobility patterns:an urban morphology perspective. Physica A:Statistical Mechanics and its Applications, 391(4):1702-1717. doi: 10.1016/j.physa.2011.11.005
[23] Kodinariya T M, Makwana P R, 2013. Review on determining number of Cluster in K-Means Clustering. International Journal of Advance Research in Computer Science and Man-agement Studies, 1(6):90-95.
[24] Langford M, 2006. Obtaining population estimates in non-census reporting zones:an evaluation of the 3-class dasymetric method. Computers, Environment and Urban Systems, 30(2):161-180. doi: 10.1016/j.compenvurbsys.2004.07.001
[25] Liu X, Gong L, Gong Y X et al., 2015. Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography, 43:78-90. doi: 10.1016/j.jtrangeo.2015.01.016
[26] Liu X, Kang C G, Gong L et al., 2016. Incorporating spatial in-teraction patterns in classifying and understanding urban land use. International Journal of Geographical Information Sci-ence, 30(2):334-350. doi: 10.1080/13658816.2015.1086923
[27] Liu Y, Wang F H, Xiao Y et al., 2012. Urban land uses and traffic ‘source-sink areas’:evidence from GPS-enabled taxi data in Shanghai. Landscape and Urban Planning, 106(1):73-87. doi: 10.1016/j.landurbplan.2012.02.012
[28] Long Y, 2016. Redefining Chinese city system with emerging new data. Applied Geography, 75:36-48. doi:10.1016/j. apgeog.2016.08.002
[29] Long Y, Liu L, 2016. Transformations of urban studies and plan-ning in the big/open data era:a review. International Journal of Image and Data Fusion, 7(4):295-308. doi: 10.1080/19479832.2016.1215355
[30] Louail T, Lenormand M, Cantu Ros O G et al., 2014. From mobile phone data to the spatial structure of cities. Scientific Reports, 4:5276. doi: 10.1038/srep05276
[31] Ma J T, Li H, Yuan F et al., 2013. Deriving operational origin-destination matrices from large scale mobile phone data. International Journal of Transportation Science and Technology, 2(3):183-204. doi:10.1260/2046-0430.2.3. 183
[32] MacQueen J, 1967. Some methods for classification and analysis of multivariate observations. In:Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probabil-ity. Berkeley, California:University of California Press, 281-297.
[33] Madhulatha T S, 2012. An overview on clustering methods. IOSR Journal of Engineering, 2(4):719-725. doi: 10.9790/3021-0204719725
[34] Ministry of Housing and Urban-Rural Development of the Peo-ple's Republic of China, 2015. City Developments Statistic Yearbook. Beijing:China Statistics Press. (In Chinese)
[35] Monsivais D, Bhattacharya K, Ghosh A et al., 2017. Seasonal and geographical impact on human resting periods. Scientific Re-ports, 7(1):10717. doi: 10.1038/s41598-017-11125-z
[36] Monsivais D, Ghosh A, Bhattacharya K et al., 2017. Tracking urban human activity from mobile phone calling patterns. PLoS Computational Biology, 13(11):e1005824. doi: 10.1371/journal.pcbi.1005824
[37] Pei T, Sobolevsky S, Ratti C et al., 2014. A new insight into land use classification based on aggregated mobile phone data. In-ternational Journal of Geographical Information Science, 28(9):1988-2007. doi: 10.1080/13658816.2014.913794
[38] Pinelli F, Di Lorenzo G, Calabrese F, 2015. Comparing urban sensing applications using event and network-driven mobile phone location data. In:Proceedings of the 16th IEEE Inter-national Conference on Mobile Data Management. Pittsburgh, PA, USA:IEEE, 219-226. doi:10.1109/MDM.2015. 33
[39] Reades J, Calabrese F, Ratti C, 2009. Eigenplaces:analysing cities using the space-time structure of the mobile phone network. Environment and Planning B:Planning and Design, 36(5):824-836. doi: 10.1068/b34133t
[40] Roth C, Kang S M, Batty M et al., 2011. Structure of urban movements:polycentric activity and entangled hierarchical flows. PLoS ONE, 6(1):e15923. doi:10.1371/journal.pone. 0015923
[41] Silva T H, Vaz De Melo P O S, Almeida J M et al., 2013. Social media as a source of sensing to study city dynamics and urban social behavior:approaches, models, and opportunities. In:Lecture Notes in Computer Science. Berlin Heidelberg:Springer-Verlag, 63-87. doi: 10.1007/978-3-642-45392-2_4
[42] Song C M, Qu Z H, Blumm N et al., 2010. Limits of predictability in human mobility. Science, 327(5968):1018-1021. doi: 10.1126/science.1177170
[43] Soria-Lara J A, Aguilera-Benavente F, Arranz-López A, 2016. Integrating land use and transport practice through spatial metrics. Transportation Research Part A:Policy and Practice, 91:330-345. doi: 10.1016/j.tra.2016.06.023
[44] Steenbruggen J, Tranos E, Nijkamp P, 2015. Data from mobile phone operators:a tool for smarter cities? Telecommunications Policy, 39(3-4):335-346. doi: 10.1016/j.telpol.2014.04.001
[45] Tian Jinling, Wang De, Xie Dongcan et al., 2017. Identifying the commuting features and patterns of typical employment areas in Shanghai using cellphone signaling data:a case study in Zhangjiang, Jinqiao and Lujiazui. Geographical Research, 36(1):134-148. doi: 10.11821/dlyj201701011
[46] Tu W, Cao J Z, Yue Y et al., 2017. Coupling mobile phone and social media data:a new approach to understanding urban functions and diurnal patterns. International Journal of Geo-graphical Information Science, 31(12):2331-2358. doi: 10.1080/13658816.2017.1356464
[47] Wang Bo, Zhen Feng, Zhang Hao, 2015. The dynamic changes of urban space-time activity and activity zoning based on check-in data in Sina Web. Scientia Geographica Sinica, 35(2):151-160. (in Chinese)
[48] Wang B, Zhen F, Qin X et al., 2018. GIS-based social spatial behavior studies:a case study in Nanjing University utilizing mobile data. In:Comprehensive Geographic Information Sys-tems. Oxford:Elsevier, 320-329. doi: 10.1016/B978-0-12-409548-9.09686-X
[49] Wang M L, 2014. Understanding Activity Location Choice with Mobile Phone Data. Washington:University of Washington.
[50] Wu C, Ye X Y, Ren F et al., 2018. Check-in behaviour and spatio-temporal vibrancy:an exploratory analysis in Shenzhen, China. Cities, 77:104-116. doi:10.1016/j.cities. 2018.01.017
[51] Yue Y, Zhuang Y, Yeh A G O et al., 2017. Measurements of POI-based mixed use and their relationships with neighbour-hood vibrancy. International Journal of Geographical Infor-mation Science, 31(4):658-675. doi:10.1080/13658816.2016. 1220561
[52] Zhai Y J, Wu H B, Fan H C et al., 2018. Using mobile signaling data to exam urban park service radius in Shanghai:methods and limitations. Computers, Environment and Urban Systems, 71:27-40. doi: 10.1016/j.compenvurbsys.2018.03.011