The DOI matches its Crossref record; 79 of the 82 references it cites were found in a registry or an index.
Clustering daily patterns of human activities in the city
Open accessOverview
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
“Spatial schemas are stable neural representations of such environmental regularities, similar to how statistical regularities form priors or expectations in visual perception 28,47,211,212 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [211]
Citations
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
(1997) · Naming the Mind: How Psychology Found its Language · doi:10.4135/9781446221815.n7 · Cited by 15,148
Jolliffe, I. T. (2005) · Wiley eBooks · doi:10.1002/0470013192.bsa501 · Cited by 14,522
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Becker, G. S. (1991) · doi:10.4159/9780674020665 · Cited by 10,565
A Theory of the Allocation of Time
Becker, G. S. (1965) · The Economic Journal · doi:10.2307/2228949 · Cited by 10,354
Research works printed but not linked: 1
- Freud S (1953) Collected papers, vol IV. vol v. 1–5. Hogarth Press and The Institute of Psychoanalysis, London
Cited by
Sorted by citations
Items 201–283 of 283
Zhi, Y., Liu, Y., Wang, S., Deng, M., Gao, J., & Li, H. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.7253 · Cited by 4
Identifying the daily activity-travel patterns of rural residents: A case study of Guiyang, China
He, Y., He, M., Liu, Y., & Qian, Q. (2025) · Habitat International · doi:10.1016/j.habitatint.2025.103529 · Cited by 3
Li, D., Zhang, T., & Li, D. (2025) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2025.104498 · Cited by 3
Michalitsi-Psarrou, A., Παπαγεωργίου, Ι. Λ., Ntanos, C., & Psarras, J. E. (2021) · Applied Sciences · doi:10.3390/app11146530 · Cited by 3
Carpio‐Pinedo, J. (2020) · EURE (Santiago) · doi:10.7764/eure.47.140.01 · Cited by 3
Nocturnal Vs. Diurnal: Relationship between Land Use and Visit Time Patterns in Commercial Areas
Oh, D., & Park, I. K. (2022) · Applied Spatial Analysis and Policy · doi:10.1007/s12061-022-09480-0 · Cited by 3
Cai, J., & Kwan, M. (2025) · Annals of the American Association of Geographers · doi:10.1080/24694452.2024.2447504 · Cited by 3
Region Partition Using User Mobility Patterns Based on Topic Model
Feng, X., Tu, L., Huang, B., & Yin, X. (2013) · IEEE International Conference on Computational Science and Engineering (CSE) · doi:10.1109/cse.2013.78 · Cited by 3
The Origin of Heterogeneity in Human Mobility Ranges.
Pappalardo, L. (2016) · EDBT/ICDT Workshops · Cited by 3
Hafezi, M. H. (2018) · DalSpace (Dalhousie University) · Cited by 3
de Brito, D. E. F., Assunção, R., Souza, R. C. S. N. P., & Meira, W. (2020) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3423405 · Cited by 3
A Fuzzy Generalisation of the Hamming Distance for Temporal Sequences
Moreau, C., Devogèle, T., de Runz, C., Peralta, V., Moreau, E., & Étienne, L. (2021) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz45933.2021.9494445 · Cited by 3
Urban Energy Flux: Human Mobility as a Predictor for Spatial ChangesPreprint
Mohammadi, N., & Taylor, J. E. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.01239 · Cited by 3
Zhao, R., & Gao, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-14843-x · Cited by 2
Liu, K., Lin, Z., Zhu, K., Yin, L., Zheng, J., Feng, Y., Wang, S., & Zhang, Y. (2025) · Computational Urban Science · doi:10.1007/s43762-025-00219-4 · Cited by 2
Hidden patterns of urban mixing across five global cities
Fan, Z., Loo, B. P. Y., Duarte, F., Ratti, C., & Moro, E. (2026) · Nature Cities · doi:10.1038/s44284-026-00477-1 · Cited by 2
Learning Urban Nightlife Routines from Mobile Data
Pozo, A., Phan, T., & Gática-Pérez, D. (2020) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3428361.3428396 · Cited by 2
Exploring the Latent Manifold of City Patterns
Agoub, A., & Kada, M. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10100683 · Cited by 2
Lee, S.-Y., Bae, M.-C., & Joo, H.-S. (2023) · 국토계획 · doi:10.17208/jkpa.2023.04.58.2.5 · Cited by 2
Wang, Z., Hu, Z., Ma, L., & Luo, W. (2023) · Journal of Urban Planning and Development · doi:10.1061/jupddm.upeng-4403 · Cited by 2
Khayati, S., Widyawan, W., & Pratama, A. R. (2024) · doi:10.1109/icitee62483.2024.10808218 · Cited by 2
Imran, M. A., & Hyun, K. K. (2025) · Travel Behaviour and Society · doi:10.1016/j.tbs.2025.101035 · Cited by 2
Lun, M., Wang, P., Wu, S., Zhang, H., Cheng, S., & Lu, F. (2025) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi14080302 · Cited by 2
‘Surprise’ Detection in Human Pattern-of-Life Behaviour
Qarout, Y., & Lowe, D. (2018) · International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) · doi:10.1109/ict-dm.2018.8636382 · Cited by 2
Intermittent decoherence blockade in a chiral ring environmentPreprint
Lorenzo, S., Longhi, S., Cabot, A., Zambrini, R., & Giorgi, G. L. (2020) · DIGITAL.CSIC (Spanish National Research Council (CSIC)) · doi:10.13039/501100011033 · Cited by 4
Sequential Patterns of Daily Human Activity Extracted from Person Trip Survey Data
Wang, W., Osaragi, T., & Tagashira, M. (2021) · The urban book series · doi:10.1007/978-3-030-76059-5_14 · Cited by 2
Ranking locations in a city via the collective home-work relations in human mobility data
He, Y., Zhao, C., & Zeng, A. (2022) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2022.128283 · Cited by 2
Perceiving spatiotemporal traffic anomalies from sparse representation-modeled city dynamics
Gao, J., Zheng, D., & Yang, S. (2020) · Personal and Ubiquitous Computing · doi:10.1007/s00779-020-01474-4 · Cited by 3
The 15-Minute City Quantified Using Mobility DataPreprint
Abbiasov, T., Heine, C., Glaeser, E. L., Ratti, C., Sabouri, S., Miranda, A. S., & Santi, P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4306706 · Cited by 2
Digital society as a determining factor in mobility, urban dynamics and current cities structure
Luna, V. M. C. (2021) · doi:10.36443/10259/6996 · Cited by 2
An Activity-Based Spatial–temporal Community Electricity Vulnerability Assessment Framework
Xia, C., Hu, Y., & Chen, J. (2023) · Environmental science and engineering · doi:10.1007/978-981-19-9822-5_280 · Cited by 1
Peng, J., Li, J., Yang, H. J., & Sun, L. (2025) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi14040148 · Cited by 1
A data-driven end-to-end framework for traffic estimation with cross-network generalization
Shin, Y., Ma, W., & Menéndez, M. (2026) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2026.105816 · Cited by 1
Kashihara, S. (2018) · EPI International Journal of Engineering · doi:10.25042/epi-ije.022018.10 · Cited by 1
Macroscopic Patterns in Sparse Location Data: Identifying Mobility Prototypes
Lüders, B. K., & Ruppel, P. (2018) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice.2018.00027 · Cited by 1
Space-time modeling of urban population daily travel-activity patterns using GPS trajectory data
Scholz, R. W. (2018) · Cited by 1
Clustering Human Mobility with Multiple Spaces
Hu, H., Lin, H., & Chiang, Y. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020890 · Cited by 1
Clustering in Before-After Studies of ITS Measures
Gundlegård, D., & Grumert, E. F. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422340 · Cited by 1
Exploring 2-D temporal patterns of individual mobility using non-negative matrix factorization
Yin, B., Leurent, F. M., Chevalier, P., & Liu, L. (2024) · Transportation research procedia · doi:10.1016/j.trpro.2024.02.012 · Cited by 1
Analysing Work Tour Motifs From Gps Trajectory Data
(2018) · i-manager s Journal on Communication Engineering and Systems · doi:10.26634/jcs.7.2.14442 · Cited by 1
DHUM: A Dynamic Human Urban Mobility Model for Smart Public Transport
Saravanan, M., & Kumar, P. S. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-1758-7_3 · Cited by 1
Data-Driven Location Annotation for Fleet Mobility Modeling.
Guidotti, R., Nanni, M., & Sbolgi, F. (2020) · CINECA IRIS Institutial research information system (University of Pisa) · Cited by 1
Undirected Bipartite Networks as an Alternative Methodology to Probabilistic Exploration
Martínez-Cerdá, J.-F., & Torrent‐Sellens, J. (2017) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-5225-2814-2.ch005 · Cited by 1
Ubiquitous and Pervasive Computing - New Trends and Opportunities
Righi, R. D. R. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.100785 · Cited by 1
Mapping of Social Functions in a Smart City When Considering Sparse Knowledge
Zinman, O., & Lerner, B. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.104901 · Cited by 1
Consistency of Social Sensing Signatures Across Major US CitiesPreprint
Soliman, A., Soltani, K., Padmanabhan, A., & Wang, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.04373 · Cited by 1
Methodology for Mining, Discovering and Analyzing Semantic Human Mobility BehaviorsPreprint
Moreau, C., Devogèle, T., Étienne, L., Peralta, V., & de Runz, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.04767 · Cited by 1
Learning Analysis Behavior in SQL WorkloadsPreprint
Moreau, C., & Peralta, V. (2021) · HAL · Cited by 1
Human Mobility and Predictability enriched by Social Phenomena Information (extended abstract)Preprint
Ponieman, N. B., Salles, A., & Sarraute, C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1311.5123
The location selection of ground-based promotion in city using human mobility data
Chen, S., & Huang, B. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3488-x
Uncovering individual's mobility patterns from GPS dataset
Lin, M. (2014) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/61741
Caraban, A., Tranquada, S., Liao, S., Greicy, K., Schöning, J., & Campos, P. (2018) · European Conference on Cognitive Ergonomics · doi:10.1145/3232078.3232097
Mobile Data Application in Mobile Network
Jiang, H., Chen, Q., Zeng, Y., & Li, D. (2019) · Information fusion and data science · doi:10.1007/978-3-030-16503-1_4
A statistical approach for studying urban human dynamics
Santa, F. (2018) · Tesis Doctorals en Xarxa (Consorci de Serveis Universitaris de Catalunya) · doi:10.6035/14123.2018.738830
Space-Time Drift Point Detection in Mobility Patterns
Souza, R. C. S. N. P., Oliveira, D. M., de Brito, D. E. F., Assunção, R., & Meira, W. (2019) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3360721
Miura, M., Minamikawa, A., Kimura, R., & Ono, C. (2020) · IEEE International Conference on Technology and Entrepreneurship (ICTE) · doi:10.1109/icte-v50708.2020.9114369
Analysis of complex socio-economical systems
Bokányi, E. (2019) · Eötvös Loránd Tudományegyetem · doi:10.15476/elte.2019.054
Modelling Place Visit Probability Sequences during Trajectory Data Gaps Based on Movement History
Ren, C., Tang, L.-L., Long, J., Kan, Z.-H., & Yang, X. (2021) · ISPRS Int. J. Geo Inf. · doi:10.3390/ijgi10070456
Routine mining on location sequences
Yan, Y., Pauchet, A., & Knippel, A. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.231
Modeling Crowdedness of Emergency Departments Leveraging Crowdsensing Mobility Data
Shou, T., Wang, Z., Shi, S., Yang, D., Zhou, B., Wang, C., & Chen, L. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00082
Developing an area classification system from mobile phone usage data
Thongphasook, N. (2017) · doi:10.58837/chula.the.2017.165
Travel-Activity Patterns Identification Using Graph Edit Distance
Tran, Y., Hashimoto, N., Ando, T., Toshihisa, S. A. T. O., & Miura, T. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422489
Federated Learning assisted framework to periodically identify user communities in urban space
de Souza, C. D. T., de Rezende, J. F., & Campos, C. A. V. (2024) · Ad Hoc Networks · doi:10.1016/j.adhoc.2024.103589
Xia, J. (2024) · ISOCARP World Planning Congress · doi:10.47472/kgieclfu
Activity-Travel Pattern Recognition of Individuals Using a Deep Short Text Clustering Algorithm
Mirzahossein, H., Bakhtiari, A., Shafidokht, E., & Erdoğan, S. (2025) · Iranian Journal of Science and Technology Transactions of Civil Engineering · doi:10.1007/s40996-025-01902-6
Exploring population spatiotemporal structure of cities with cellular data: A case study of Beijing
Lai, J., Zhang, Y., Liu, D., & Wang, C. (2025) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2025.104353
Semukhina, O., Bae, J., Korotchenko, S., & Copeland, C. (2025) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2025.102473
A Relative Fuzzy Hamming-based Similarity for Temporal Semantic Sequences
Merakchi, H., de Runz, C., Devogèle, T., & Peralta, V. (2025) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz62266.2025.11152160
DIRECT IMPACT OF ICT ON URBAN ECONOMIC GROWTH BY DEMONSTRATING AGENT-BASED MODELING FOR SMART CITIES
Mshvidobadze, T. I. (2025) · Social Economics · doi:10.26565/2524-2547-2025-69-03
Yang, Y., Pentland, A., & Moro, E. (2023) · Figshare · doi:10.6084/m9.figshare.22949295
Cebeillac, A. (2018) · theses.fr (ABES) · doi:10.70675/5d547c6fzffe0z4316zb36cz89231dffc829
Beyond brightness: How lighting configuration and urban context shape nighttime park use
Cheng, S., Guo, S., Yang, Q., Niu, J., & Shen, X. (2026) · Sustainable Cities and Society · doi:10.1016/j.scs.2026.107435
Individual weekly activity sequence generation framework based on activity pattern dynamics
Yang, C., Zhu, S., Chen, M., Yu, C., & Yuan, Q. (2026) · International Journal of Transportation Science and Technology · doi:10.1016/j.ijtst.2025.12.003
Multi-Day Activity Pattern Inference Using Constrained Gaussian Mixture Model (GMM) Classification
Kannam, N., Allahviranloo, M., & Vatin, L. A. R. (2026) · Urban Science · doi:10.3390/urbansci10060331
Fisu, A. A., Maulidi, C., Umar, F., Natsir, R., Andi Fathussalam, B., & Marzaman, L. U. (2026) · Urban Planning and Transport Research · doi:10.1080/21650020.2026.2708423
Activity-travel diary survey in an emerging country: constraints, design strategy and way forward
Nayak, S., & Pandit, D. (2026) · Transportation in Developing Economies · doi:10.1007/s40890-026-00281-5
Using Statewide Human Mobility Data to Screen for Future Non-Motorized Traffic Monitoring LocationsPreprint
Dhakal, S., Bian, R., & Tolford, T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7412884
Learning Latent Behavioral Structures from Daily Activity Sequences for Travel Demand SegmentationPreprint
Ye, L., Guo, Y., & He, B. Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7447433
Metadata
- Authors
- Jiang, S., Ferreira, J., & González, M. C.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Jiang, Shan
- Ferreira, Joseph
- González, Marta C.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2012-04-20
- Volume
- 25
- Issue
- 3
- Pages
- 478-510
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2088722350
- Cited by
- 282
- References
- 82
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 18 · 2023: 20 · 2022: 22 · 2021: 35 · 2020: 32 · 2019: 21 · 2018: 22 · 2017: 27 · 2016: 23 · 2015: 14 · 2014: 10 · 2013: 7 · 2012: 4
- FWCI
- 29.36
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-sa
- www.springer.com/tdm (tdm)
- Locations
- Data Mining and Knowledge Discovery (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- U.S. Department of Transportation
- Austrian Institute of Technology
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Transportation › Transportation Planning and Optimization
- Social Sciences › Transportation › Urban Transport and Accessibility
- Keywords
- daily activity patterns, sociodemographic factors, urban transportation planning, emergency response, Chicago metropolitan area
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