Revealing temporal stay patterns in human mobility using large‐scale mobile phone location data
暂无分享,去创建一个
Xiping Yang | Zhiyuan Zhao | Ling Yin | Yang Xu | Zhixiang Fang | Junyi Li | Z. Fang | Yang Xu | Xiping Yang | Ling Yin | Junyi Li | Zhiyuan Zhao
[1] A. Condeço-Melhorado,et al. City dynamics through Twitter: Relationships between land use and spatiotemporal demographics , 2018 .
[2] Jari Saramäki,et al. Temporal motifs in time-dependent networks , 2011, ArXiv.
[3] O. Löfgren. Everyday Life, Anthropology of , 2015 .
[4] Z. Fang,et al. Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data , 2019, Journal of Transport Geography.
[5] Qingquan Li,et al. Another Tale of Two Cities: Understanding Human Activity Space Using Actively Tracked Cellphone Location Data , 2016, Geographies of Mobility.
[6] Xi Liu,et al. Revealing daily travel patterns and city structure with taxi trip data , 2013, ArXiv.
[7] Haoying Han,et al. Evaluating the effectiveness of urban growth boundaries using human mobility and activity records , 2015 .
[8] Enrique Frías-Martínez,et al. Uncovering the spatial structure of mobility networks , 2015, Nature Communications.
[9] S. Mei,et al. Space-time personalized short message service (SMS) for infectious disease control – Policies for precise public health , 2020 .
[10] Zbigniew Smoreda,et al. Unravelling daily human mobility motifs , 2013, Journal of The Royal Society Interface.
[11] Feng Zhen,et al. ICT, activity space–time and mobility: new insights, new models, new methodologies , 2018 .
[12] Marta C. González,et al. Origin-destination trips by purpose and time of day inferred from mobile phone data , 2015 .
[13] Paolo Santi,et al. Quantifying segregation in an integrated urban physical-social space , 2019, Journal of the Royal Society Interface.
[14] Martin Raubal,et al. Analyzing the distribution of human activity space from mobile phone usage: an individual and urban-oriented study , 2016, Int. J. Geogr. Inf. Sci..
[15] Dietmar Bauer,et al. Daily travel behavior: lessons from a week-long survey for the extraction of human mobility motifs related information , 2013, UrbComp '13.
[16] Joseph Ferreira,et al. Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore , 2017, IEEE Transactions on Big Data.
[17] B. Cornwell,et al. Patterns of everyday activities across social contexts , 2018, Proceedings of the National Academy of Sciences.
[18] C. Ratti,et al. Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .
[19] Tian Lan,et al. Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies , 2014 .
[20] Vania Bogorny,et al. SMSM: a similarity measure for trajectory stops and moves , 2019, Int. J. Geogr. Inf. Sci..
[21] Z. Fang,et al. Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China , 2018 .
[22] Hui Wang,et al. A new perspective on the temporal pattern of human activities in cities: The case of Shanghai , 2019, Cities.
[23] Li Gong,et al. Revealing travel patterns and city structure with taxi trip data , 2016 .
[24] Xinyue Ye,et al. Editorial: human dynamics in the mobile and big data era , 2016, Int. J. Geogr. Inf. Sci..
[25] Benny Karpatschof,et al. Human activity - contributions to the anthropological sciences from a perspective of activity theory , 2007, Inf. Res..
[26] Z. Fang,et al. Revealing the relationship of human convergence–divergence patterns and land use: A case study on Shenzhen City, China , 2019 .
[27] Song Gao,et al. Spatio-Temporal Analytics for Exploring Human Mobility Patterns and Urban Dynamics in the Mobile Age , 2015, Spatial Cogn. Comput..
[28] M. Barthelemy,et al. Human mobility: Models and applications , 2017, 1710.00004.
[29] Jean-François Paiement,et al. A Generative Model of Urban Activities from Cellular Data , 2018, IEEE Transactions on Intelligent Transportation Systems.
[30] Song Gao,et al. Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling , 2019, Comput. Environ. Urban Syst..
[31] Ling Yin,et al. Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns , 2017, Int. J. Geogr. Inf. Sci..
[32] Emilia Nercissians. THE ANTHROPOLOGY OF EVERYDAY LIFE , 2009 .
[33] Fan Zhang,et al. Identifying stops from mobile phone location data by introducing uncertain segments , 2018, Trans. GIS.
[34] Jakob Puchinger,et al. Inferring dynamic origin-destination flows by transport mode using mobile phone data , 2019, Transportation Research Part C: Emerging Technologies.
[35] Chaogui Kang,et al. Social Sensing: A New Approach to Understanding Our Socioeconomic Environments , 2015 .
[36] Ricardo Muñoz,et al. Land Use detection with cell phone data using topic models: Case Santiago, Chile , 2017, Comput. Environ. Urban Syst..
[37] Daniel A. Keim,et al. A framework for using self-organising maps to analyse spatio-temporal patterns, exemplified by analysis of mobile phone usage , 2010, J. Locat. Based Serv..
[38] Shih-Lung Shaw,et al. Understanding the New Human Dynamics in Smart Spaces and Places: Toward a Splatial Framework , 2019, Smart Spaces and Places.
[39] Andrew J Tatem,et al. Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine. , 2019, Journal of travel medicine.
[40] Yu Liu,et al. The promises of big data and small data for travel behavior (aka human mobility) analysis , 2016, Transportation research. Part C, Emerging technologies.
[41] Yongxi Gong,et al. Exploring the spatiotemporal structure of dynamic urban space using metro smart card records , 2017, Comput. Environ. Urban Syst..
[42] Yee Leung,et al. Applying mobile phone data to travel behaviour research: A literature review , 2017 .
[43] Dietmar Bauer,et al. Inferring land use from mobile phone activity , 2012, UrbComp '12.
[44] Peter Widhalm,et al. Discovering urban activity patterns in cell phone data , 2015, Transportation.
[45] P. Nas. Urban Anthropology , 1997 .
[46] D. Kalekin-Fishman. Sociology of everyday life , 2013 .
[47] Eran Toch,et al. Analyzing large-scale human mobility data: a survey of machine learning methods and applications , 2019, Knowledge and Information Systems.
[48] Ling Yin,et al. Mining Daily Activity Chains from Large-Scale Mobile Phone Location Data , 2020, Cities.
[49] Wei Tu,et al. Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns , 2017, Int. J. Geogr. Inf. Sci..
[50] Chenghu Zhou,et al. A new insight into land use classification based on aggregated mobile phone data , 2013, Int. J. Geogr. Inf. Sci..
[51] Yongping Zhang,et al. Understanding temporal pattern of human activities using Temporal Areas of Interest , 2018 .
[52] Chaogui Kang,et al. Understanding operation behaviors of taxicabs in cities by matrix factorization , 2016, Comput. Environ. Urban Syst..
[53] Margaret Martonosi,et al. Identifying Important Places in People's Lives from Cellular Network Data , 2011, Pervasive.
[54] Tianyang Bai,et al. Measuring the vibrancy of urban neighborhoods using mobile phone data with an improved PageRank algorithm , 2019, Trans. GIS.
[55] Qingquan Li,et al. Characterizing preferred motif choices and distance impacts , 2019, PloS one.
[56] O. Järv,et al. Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones , 2010 .
[57] Joy.J Jenniffer,et al. ANALYSING WORK TOUR MOTIFS FROM GPS TRAJECTORY DATA , 2018 .