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Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns
Abstract
Understanding urban functions and their relationships with human activities has great implications for smart and sustainable urban development. In this study, we present a novel approach to uncovering urban functions by aggregating human activities inferred from mobile phone positioning and social media data. First, the homes and workplaces (of travelers) are estimated from mobile phone positioning data to annotate the activities conducted at these locations. The remaining activities (such as shopping, schooling, transportation, recreation and entertainment) are labeled using a hidden Markov model with social knowledge learned from social media check-in data over a lengthy period. By aggregating identified human activities, hourly urban functions are inferred, and the diurnal dynamics of those functions are revealed. An empirical analysis was conducted for the case of Shenzhen, China. The results indicate that the proposed approach can capture citywide dynamics of both human activities and urban functions. It also suggests that although many urban areas have been officially labeled with a single land-use type, they may provide different functions over time depending on the types and range of human activities. The study demonstrates that combining different data on human activities could yield an improved understanding of urban functions, which would benefit short-term urban decision-making and long-term urban policy making.
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“There is also work on inferring people's travel modes and destinations from social media or various mobile phone sensors such as GPS (transportation mode detection), e.g., [156,798].”
From Tackling climate change with machine learning (2023) · cites this paper as [798]
Citations
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Photogrammetric Engineering and Remote Sensing
Nicol, J., King, B., Quattrochi, D., Dowman, I. J., Ehlers, M., & Xiaoli, D. (2007) · UCL Discovery (University College London) · Cited by 9,325
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
Viterbi, A. J. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1054010 · Cited by 6,954
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,186
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234
The Geography of Transport Systems
Rodrigue, J. (2020) · Routledge eBooks · doi:10.4324/9780429346323 · Cited by 3,060
Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., & Portugali, Y. (2012) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2012-01703-3 · Cited by 2,424
Geomatics and Information Science of Wuhan University
Hu, S., Deren, L., Liu, Y., & Li, D. (2006) · Wuhan Daxue xuebao. Xinxi kexue ban · Cited by 1,049
Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis
Ratti, C., Frenchman, D., Pulselli, R. M., & Williams, S. (2006) · Environment and Planning B Planning and Design · doi:10.1068/b32047 · Cited by 863
Annals of the American association of geographers
Paterson, G. (2016) · interactions · Cited by 762
The Geography of Transport Systems
Rodrigue, J. (2013) · Routledge eBooks · doi:10.4324/9780203371183 · Cited by 747
Understanding individual mobility patterns from urban sensing data: A mobile phone trace example
Calabrese, F., Diao, M., Lorenzo, G. D., Ferreira, J., & Ratti, C. (2012) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2012.09.009 · Cited by 645
The promises of big data and small data for travel behavior (aka human mobility) analysis
Chen, C., Ma, J., Susilo, Y. O., Liu, Y., & Wang, M. (2016) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2016.04.005 · Cited by 611
Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy
Yue, Y., Zhuang, Y., Yeh, A. G. O., Xie, J.-Y., Ma, C., & Li, Q. (2016) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2016.1220561 · Cited by 610
Self-citationUnravelling daily human mobility motifs
Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., & González, M. C. (2013) · Journal of The Royal Society Interface · doi:10.1098/rsif.2013.0246 · Cited by 585
What is a hidden Markov model?
Eddy, S. R. (2004) · Nature Biotechnology · doi:10.1038/nbt1004-1315 · Cited by 563
Detecting the dynamics of urban structure through spatial network analysis
Zhong, C., Arisona, S. M., Huang, X., Batty, M., & Schmitt, G. (2014) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2014.914521 · Cited by 524
From mobile phone data to the spatial structure of cities
Louail, T., Lenormand, M., Cantú-Ros, O. G., Picornell, M., Herranz, R., Frı́as, M., Ramasco, J. J., & Barthélemy, M. (2014) · Scientific Reports · doi:10.1038/srep05276 · Cited by 457
Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai
Liu, Y., Wang, F., Xiao, Y., & Gao, S. (2012) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2012.02.012 · Cited by 425
A new insight into land use classification based on aggregated mobile phone data
Pei, T., Sobolevsky, S. L., Ratti, C., Shaw, S., Li, T., & Zhou, C. (2014) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2014.913794 · Cited by 397
Self-citationUrban Sensing Using Mobile Phone Network Data: A Survey of Research
Calabrese, F., Ferrari, L., & Blondel, V. D. (2014) · ACM Computing Surveys · doi:10.1145/2655691 · Cited by 327
Tu, W., Li, Q., Fang, Z., Shaw, S., Zhou, B., & Chang, X. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.10.004 · Cited by 325
Self-citationClustering daily patterns of human activities in the city
Jiang, S., Ferreira, J., & González, M. C. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0264-z · Cited by 282
Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks
Sevtsuk, A., & Ratti, C. (2010) · Journal of Urban Technology · doi:10.1080/10630731003597322 · Cited by 266
Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network
Reades, J., Calabrese, F., & Ratti, C. (2009) · Environment and Planning B Planning and Design · doi:10.1068/b34133t · Cited by 263
Inferring trip purposes and uncovering travel patterns from taxi trajectory data
Li, G., Liu, X., Wu, L., & Liu, Y. (2015) · Cartography and Geographic Information Science · doi:10.1080/15230406.2015.1014424 · Cited by 254
Correlating mobile phone usage and travel behavior – A case study of Harbin, China
Yuan, Y., Raubal, M., & Liu, Y. (2011) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2011.07.003 · Cited by 251
Rabari, C., & Storper, M. (2015) · Cambridge Journal of Regions Economy and Society · doi:10.1093/cjres/rsu021 · Cited by 236
Discovering urban activity patterns in cell phone data
Widhalm, P., Yang, Y., Ulm, M. A., Athavale, S. M., & González, M. C. (2015) · Transportation · doi:10.1007/s11116-015-9598-x · Cited by 236
Crowdsourcing urban form and function
Crooks, A. T., Pfoser, D., Jenkins, A., Croitoru, A., Stefanidis, A., Smith, D. A., Καραγιώργου, Σ., Efentakis, A., & Lamprianidis, G. (2015) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2014.977905 · Cited by 233
Gong, P., & Howarth, P. J. (1990) · Cited by 232
Impacts of weather on public transport ridership: Results from mining data from different sources
Zhou, M., Wang, D., Li, Q., Yue, Y., Tu, W., & Cao, R. (2016) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2016.12.001 · Cited by 220
Self-citationYue, Y., Lan, T., Yeh, A. G. O., & Li, Q. (2014) · Travel Behaviour and Society · doi:10.1016/j.tbs.2013.12.002 · Cited by 216
Self-citationIntra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data
Wu, L., Zhi, Y., Sui, Z., & Liu, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097010 · Cited by 212
Spectral clustering for sensing urban land use using Twitter activity
Frías-Martínez, V., & Frı́as-Martı́nez, E. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.06.019 · Cited by 208
The Geotemporal Demographics of Twitter Usage
Longley, P., Adnan, M., & Lansley, G. (2015) · Environment and Planning A Economy and Space · doi:10.1068/a130122p · Cited by 189
Huang, Q., & Wong, D. W. S. (2016) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2016.1145225 · Cited by 183
Research challenges and opportunities in mapping social media and Big Data
Tsou, M. (2015) · Cartography and Geographic Information Science · doi:10.1080/15230406.2015.1059251 · Cited by 181
Ahas, R., Aasa, A., Yuan, Y., Raubal, M., Smoreda, Z., Liu, Y. J., Ziemlicki, C., Tiru, M., & Zook, M. (2015) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2015.1063151 · Cited by 173
An Advanced Systematic Literature Review on Spatiotemporal Analyses of T witter Data
Steiger, E., de Albuquerque, J. P., & Zipf, A. (2015) · Transactions in GIS · doi:10.1111/tgis.12132 · Cited by 165
Urban sensing: Using smartphones for transportation mode classification
Shin, D., Aliaga, D. G., Tunçer, B., Arisona, S. M., Kim, S., Zünd, D., & Schmitt, G. (2014) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2014.07.011 · Cited by 138
Automated urban land-use classification with remote sensing
Hu, S., & Wang, L. (2012) · International Journal of Remote Sensing · doi:10.1080/01431161.2012.714510 · Cited by 130
Xu, Y., Shaw, S., Zhao, Z., Yin, L., Lu, F., Chen, J., Fang, Z., & Li, Q. (2016) · Annals of the American Association of Geographers · doi:10.1080/00045608.2015.1120147 · Cited by 121
Self-citationInferring individual daily activities from mobile phone traces: A Boston example
Diao, M., Zhu, Y., Ferreira, J., & Ratti, C. (2015) · Environment and Planning B Planning and Design · doi:10.1177/0265813515600896 · Cited by 108
Batty, M. (2009) · Elsevier eBooks · doi:10.1016/b978-008044910-4.01092-0 · Cited by 103
Inferring Urban Land Use Using Large-Scale Social Media Check-in Data
Zhan, X., Ukkusuri, S. V., & Zhu, F. (2014) · Networks and Spatial Economics · doi:10.1007/s11067-014-9264-4 · Cited by 100
Comparing and modelling land use organization in cities
Lenormand, M., Picornell, M., Cantú-Ros, O. G., Louail, T., Herranz, R., Barthélemy, M., Frı́as, M., Miguel, M. S., & Ramasco, J. J. (2015) · Royal Society Open Science · doi:10.1098/rsos.150449 · Cited by 98
Space–Time Analysis: Concepts, Quantitative Methods, and Future Directions
Li, A., Tsou, M., Crook, S. E. S., Chun, Y., Spitzberg, B. H., Gawron, J. M., & Gupta, D. K. (2015) · Annals of the Association of American Geographers · doi:10.1080/00045608.2015.1064510 · Cited by 91
Linking cyber and physical spaces through community detection and clustering in social media feeds
Croitoru, A., Wayant, N. M., Crooks, A. T., Radzikowski, J., & Stefanidis, A. (2014) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2014.11.002 · Cited by 70
Mapping collective human activity in an urban environment based on mobile phone data
Sagl, G., Delmelle, E. M., & Delmelle, E. C. (2014) · Cartography and Geographic Information Science · doi:10.1080/15230406.2014.888958 · Cited by 67
Crowdsourcing functions of the living city from Twitter and Foursquare data
Zhou, X., & Zhang, L. (2016) · Cartography and Geographic Information Science · doi:10.1080/15230406.2015.1128852 · Cited by 65
Stability of a hierarchical clustering
Smith, S. P., & Dubes, R. C. (1980) · Pattern Recognition · doi:10.1016/0031-3203(80)90042-4 · Cited by 46
Batty, M. (2010) · Environment and Planning B Planning and Design · doi:10.1068/b3704ed · Cited by 45
A network Kernel Density Estimation for linear features in space–time analysis of big trace data
Tang, L., Kan, Z., Zhang, X., Sun, F., Yang, X., & Li, Q. (2015) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2015.1119279 · Cited by 42
Self-citationMobile phone usage in complex urban systems: a space–time, aggregated human activity study
Tranos, E., & Nijkamp, P. J. (2015) · Journal of Geographical Systems · doi:10.1007/s10109-015-0211-9 · Cited by 36
From where do tweets originate?
Huang, Q., Cao, G., & Wang, C. (2014) · doi:10.1145/2755492.2755494 · Cited by 32
A temporal-contextual analysis of urban dynamics using location-based data
Grinberger, A. Y., & Shoval, N. (2015) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2015.1049951 · Cited by 15
From Geomatics to Urban Informatics
Li, Q. (2017) · doi:10.13203/j.whugis20160200 · Cited by 9
Self-citationInferring individual physical locations with social friendships
Zhou, M., Tu, W., Li, Q., Yue, Y., & Chang, X. (2015) · International Conference on Geoinformatics · doi:10.1109/geoinformatics.2015.7378565 · Cited by 4
Self-citationCOMPARISON OF URBAN HUMAN MOVEMENTS INFERRING FROM MULTI-SOURCE SPATIAL-TEMPORAL DATA
Cao, R., Tu, W., Cao, J., & Li, Q. (2016) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xli-b2-471-2016 · Cited by 3
Self-citation
Research works printed but not linked: 5
- 10.1145/2658850 · DOI
- Cao J., 2017, Journal of Geo-Information Science, 19, 467
- China City Statistical Yearbook, 2013. Beijing:China statistical press.
- Chirag R., 2015, Cambridge Journal of Regions, Economy and Society,, 25, 27
- Zhou M., 2015, Proceedings of geoinfomatics 2015, 1
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Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
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Self-citation
Metadata
- Authors
- Tu, W., Cao, J., Yue, Y., Shaw, S.-L., Zhou, M., Wang, Z., Chang, X., Xu, Y., & Li, Q.
- Affiliations
- Shenzhen University (CN)
- Wuhan University (CN)
- University of Tennessee at Knoxville (US)
- Hong Kong Baptist University (HK)
- Singapore-MIT Alliance for Research and Technology (SG)
- Corresponding author
- Yue, Yang
- Venue
- International Journal of Geographical Information Science
- Publisher
- Informa UK Limited
- Published
- 2017-07-31
- Volume
- 31
- Issue
- 12
- Pages
- 2331-2358
- ISSN
- 0269-3798 (ISSN-L)
- 1365-8816
- 1362-3087
- OpenAlex
- W2741078791
- Cited by
- 298
- References
- 61
- Citations by year
- 2026: 25 · 2025: 33 · 2024: 33 · 2023: 29 · 2022: 44 · 2021: 43 · 2020: 42 · 2019: 29 · 2018: 18 · 2017: 2
- FWCI
- 36.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Geographical Information Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China (41401444, 41671387, 91546106)
- the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (KF-2016-02-009, KF-2016-02-010)
- Shenzhen Scientific Research and Development Funding Program (CXZZ20150504141623042)
- Nature Science Foundation of Shenzhen University (2016065)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Urban Studies › Urban Design and Spatial Analysis
- Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
- Keywords
- urban functions, diurnal patterns, human activities, social media check-ins, hidden Markov models, land use, activity inference, Shenzhen, urban decision-making
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