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 work on inferring people's travel modes and destinations from social media or mobile phone sensors such as GPS.
“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]
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- 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
- Physical Sciences › Engineering › Building and Construction › 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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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.
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