The DOI matches its Crossref record; 60 of the 61 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
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
Cited by
Sorted by citations
Passive crowdsourcing of social media in environmental research: A systematic map
Ghermandi, A., & Sinclair, M. M. (2019) · Global Environmental Change · doi:10.1016/j.gloenvcha.2019.02.003 · Cited by 365
Tackling climate change with machine learning
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
Portraying the spatial dynamics of urban vibrancy using multisource urban big data
Tu, W., Zhu, T., Xia, J., Zhou, Y., Lai, Y., Jiang, J., & Li, Q. (2019) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2019.101428 · Cited by 268
Self-citationMultiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic
Kang, Y., Gao, S., Liang, Y., Li, M., Rao, J., & Kruse, J. (2020) · Scientific Data · doi:10.1038/s41597-020-00734-5 · Cited by 270
Spatial variations in urban public ridership derived from GPS trajectories and smart card data
Tu, W., Cao, R., Yue, Y., Zhou, B., Li, Q., & Li, Q. (2018) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2018.04.013 · Cited by 251
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
