Contextualizing human dynamics: Understanding the semantics of movement trajectories with Wi-Fi data

Travel Behaviour and Society - Tập 25 - Trang 183-192 - 2021
Luning Li1, Xiang Chen2, Qiang Li1, Xiaoyue Tan1,3, Jin Chen1, Dawei Wang4, Pengfei Jia5
1State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2Department of Geography, University of Connecticut, Storrs, CT 06269, USA
3Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
4Department of Urban Planning and Design, The University of Hong Kong, Hong Kong SAR, China
5China Academy of Urban Planning and Design, Beijing, 100044, China

Tài liệu tham khảo

Aizawa, 2003, An information-theoretic perspective of tf-idf measures, Inf. Process. Manage., 39, 45, 10.1016/S0306-4573(02)00021-3

Andrienko, 2009, Interactive visual clustering of large collections of trajectories, 3

Axhausen, 2002, Observing the rhythms of daily life: A six-week travel diary, Transportation, 29, 95, 10.1023/A:1014247822322

Barabasi, 2005, The origin of bursts and heavy tails in human dynamics, Nature, 435, 207, 10.1038/nature03459

Calabrese, 2013, Understanding individual mobility patterns from urban sensing data: a mobile phone trace example, Transport. Res. Part C: Emerg. Technol., 26, 301, 10.1016/j.trc.2012.09.009

Calabrese, 2010, Eigenplaces: Segmenting space through digital signatures, IEEE Pervasive Comput., 9, 78, 10.1109/MPRV.2009.62

Chen, 2014, Does food environment influence food choices? A geographical analysis through “tweets”, Appl. Geogr., 51, 82, 10.1016/j.apgeog.2014.04.003

Chiang, 2015, Assessing travel motivations of cultural tourists: A factor-cluster segmentation analysis, J. Informat. Optimizat. Sci., 36, 269, 10.1080/02522667.2014.996028

Clarke, 1981, Error and uncertainty in travel surveys, Transportation, 10, 105, 10.1007/BF00165261

Cunche, 2014, Linking wireless devices using information contained in Wi-Fi probe requests, Pervasive Mob. Comput., 11, 56, 10.1016/j.pmcj.2013.04.001

Dodge, 2012, Movement similarity assessment using symbolic representation of trajectories, Int. J. Geograph. Informat. Sci., 26, 1563, 10.1080/13658816.2011.630003

Fabrikant, 2001, Formalizing semantic spaces for information access, Ann. Assoc. Am. Geogr., 91, 263, 10.1111/0004-5608.00242

Finch, 1939, Geographical science and social philosophy, Ann. Assoc. Am. Geogr., 29, 1, 10.1080/00045603909357185

Huang, 2016, Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us?, Int. J. Geograph. Informat. Sci., 30, 1873, 10.1080/13658816.2016.1145225

Kraemer, 2020, The effect of human mobility and control measures on the COVID-19 epidemic in China, Science, 368, 493, 10.1126/science.abb4218

Laube, 2014

Li, 2019, Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data, Comput. Environ. Urban Syst., 77, 01346, 10.1016/j.compenvurbsys.2019.101346

Meneses, F., and Moreira, A., 2012. Large scale movement analysis from WiFi based location data. In: 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN): Sydney, IEEE: 1-9. 10.1109/IPIN.2012.6418885.

Pas, 1995, Intrapersonal variability in daily urban travel behavior: some additional evidence, Transportation, 22, 135, 10.1007/BF01099436

Roehl, 1992, Risk perceptions and pleasure travel: An exploratory analysis, J. Travel Res., 30, 17, 10.1177/004728759203000403

Shaw, 2016, Editorial: Human dynamics in the mobile and big data era, Int. J. Geograph. Informat. Sci., 30, 1687, 10.1080/13658816.2016.1164317

Shen, 2016, A framework for identifying activity groups from individual space-time profiles, Int. J. Geograph. Informat. Sci., 30, 1785, 10.1080/13658816.2016.1139119

Siła-Nowicka, 2016, Analysis of human mobility patterns from GPS trajectories and contextual information, Int. J. Geograph. Informat. Sci., 30, 881, 10.1080/13658816.2015.1100731

Traunmueller, 2018, Digital footprints: Using WiFi probe and locational data to analyze human mobility trajectories in cities, Comput. Environ. Urban Syst., 72, 4, 10.1016/j.compenvurbsys.2018.07.006

Vanhoef, M., et al., 2016. Why MAC address randomization is not enough: An analysis of Wi-Fi network discovery mechanisms. In: Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security. Xi'an: ACM, 413-424.

Van Moorter, 2010, Identifying movement states from location data using cluster analysis, J. Wildl. Manag., 74, 588, 10.2193/2009-155

Vogel, 2011, Understanding bike-sharing systems using data mining: Exploring activity patterns, Procedia-Soc. Behav. Sci., 20, 514, 10.1016/j.sbspro.2011.08.058

Wang, 2018, Space, function, and vitality in historic areas: The tourismification process and spatial order of Shichahai in Beijing, Int. J. Tourism Res., 20, 335, 10.1002/jtr.2185

Wang, 2013, Measuring human queues using wifi signals, 235

Wu, 2019, Inferring demographics from human trajectories and geographical context, Comput. Environ. Urban Syst., 77, 10.1016/j.compenvurbsys.2019.101368

Xiao, 2011, Integrated Wi-Fi fingerprinting and inertial sensing for indoor positioning, 1

Zha, 2016, Unfolding large-scale online collaborative human dynamics, Proc. Natl. Acad. Sci., 113, 14627, 10.1073/pnas.1601670113

Zhou, 2015, Understanding spatiotemporal patterns of biking behavior by analyzing massive bike sharing data in Chicago, PLoS ONE, 10, 10.1371/journal.pone.0137922