Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows
Open accessAbstract
The spatial arrangement of urban hubs and centers and how individuals interact with these centers is a crucial problem with many applications ranging from urban planning to epidemiology. We utilize here in an unprecedented manner the large scale, real-time 'Oyster' card database of individual person movements in the London subway to reveal the structure and organization of the city. We show that patterns of intraurban movement are strongly heterogeneous in terms of volume, but not in terms of distance travelled, and that there is a polycentric structure composed of large flows organized around a limited number of activity centers. For smaller flows, the pattern of connections becomes richer and more complex and is not strictly hierarchical since it mixes different levels consisting of different orders of magnitude. This new understanding can shed light on the impact of new urban projects on the evolution of the polycentric configuration of a city and the dense structure of its centers and it provides an initial approach to modeling flows in an urban system.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
A city's spatial structure is characterized by multiple subregions connected via distribution networks.
“Recent findings indicate that a city's spatial structure is characterized by multiple subregions (such as urban zoning areas) connected via distribution networks (such as streets) 40,43,134,135 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [43]
Industrial cities can be classified using characteristic spatial patterns and common principles, such as an organization centred around nodes of economic activity.
“Urban data analytics further show that industrial cities can be classified using characteristic spatial patterns and common principles, such as an organization centred around nodes of economic activity 42,43,129,177 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [43]
These statistical commonalities of mobility patterns characterize the underlying structure of a city and navigational behaviours, and provide support for the notion of a spatial schema for an environment with key elements that can be generalized to other environments that belong to a superordinate category.
“Given that these statistical commonalities of mobility patterns characterize the underlying structure of a city 41,43 and navigational behaviours 34,117,178,179 , they provide support for the notion of a spatial schema for an environment with key elements that can be generalized to other environments that belong to a superordinate category 159 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [43]
Studies that focus on mathematical analyses of topology have helped us gain insight into spatial patterns that characterize different types of environments.
“Studies that focus on mathematical analyses of topology have helped us gain insight into spatial patterns that characterize different types of environments 43,135,208 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [43]
A dominant model of modern city form is the clustering of economic activities in urban hubs, known as polycentricity.
“For example, a dominant model of modern city form is the clustering of economic activities in urban hubs, known as polycentricity 43,209 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [43]
The patterns of individuals' movement to and from urban hubs across cities can be collected using GIS tools and phone-usage data.
“The patterns of individuals' movement to and from urban hubs across cities can be collected using GIS (geographic information system) tools and phone-usage data 40,43,177,210 .”
From From cognitive maps to spatial schemas (2023) · cites this paper as [43]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,017 · see citing works
The Spatial Economy: Cities, Regions, and International Trade
Fujita, M., Krugman, P., & Venables, A. J. (1999) · doi:10.7551/mitpress/6389.001.0001 · Cited by 4,788 · see citing works
Random graphs with arbitrary degree distributions and their applications
Newman, M. E. J., Strogatz, S. H., & Watts, D. J. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.64.026118 · Cited by 3,675 · see citing works
A critical point for random graphs with a given degree sequence
Molloy, M. S. O., & Reed, B. A. (1995) · Random Structures and Algorithms · doi:10.1002/rsa.3240060204 · Cited by 2,419 · see citing works
The scaling laws of human travel
Brockmann, D., Hufnagel, L., & Geisel, T. (2006) · Nature · doi:10.1038/nature04292 · Cited by 2,182 · see citing works
Modelling disease outbreaks in realistic urban social networks
Eubank, S., Güçlü, H., Kumar, V. S. A., MARATHE, M. V., Srinivasan, A., Toroczkai, Z., & Wang, N. (2004) · Nature · doi:10.1038/nature02541 · Cited by 2,091 · see citing works
Warf, B. (2010) · Encyclopedia of Geography · doi:10.4135/9781412939591.n1196 · Cited by 1,196 · see citing works
Anas, A., Arnott, R., & Small, K. A. (1997) · RePEc: Research Papers in Economics · Cited by 819 · see citing works
The Polycentric Urban Region: Towards a Research Agenda
Kloosterman, R. C., & Musterd, S. (2001) · Urban Studies · doi:10.1080/00420980120035259 · Cited by 597 · see citing works
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2009) · Nature · doi:10.1038/nature07850 · Cited by 493 · see citing works
Understanding the Spreading Patterns of Mobile Phone Viruses
Wang, P., González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2009) · Science · doi:10.1126/science.1167053 · Cited by 489 · see citing works
Alexander, C. (2013) · Routledge eBooks · doi:10.4324/9780203094235-18 · Cited by 476 · see citing works
Hall, P. G. (1966) · Medical Entomology and Zoology · Cited by 408 · see citing works
Geddes, P., LeGates, R., & Stout, F. (2021) · Routledge eBooks · doi:10.4324/9781003101079 · Cited by 348 · see citing works
Balcan, D., Hu, H., Goncalves, B., Bajardi, P., Poletto, C., Ramasco, J. J., Paolotti, D., Perra, N., Tizzoni, M., Van Den Broeck, W., et al. (2009) · PORTO Publications Open Repository TOrino (Politecnico di Torino) · Cited by 311 · see citing works
Balcan, D., Hu, H., Gonçalves, B., Bajardi, P., Poletto, C., Ramasco, J. J., Paolotti, D., Perra, N., Tizzoni, M., den Broeck, W. V., et al. (2009) · BMC Medicine · doi:10.1186/1741-7015-7-45 · Cited by 303 · see citing works
Friedmann, J. R. P., & Miller, J. J. (1965) · Journal of the American Institute of Planners · doi:10.1080/01944366508978185 · Cited by 282 · see citing works
Hierarchy in Natural and Social Sciences
Pumain, D. (2006) · Springer eBooks · doi:10.1007/1-4020-4127-6 · Cited by 261 · see citing works
The Structure of Interurban Traffic: A Weighted Network Analysis
Montis, A. D., Barthélemy, M., Chessa, A., & Vespignani, A. (2007) · Environment and Planning B Planning and Design · doi:10.1068/b32128 · Cited by 254 · see citing works
Self-citationBatty, M. (2005) · Cited by 234 · see citing works
Self-citationThurstain‐Goodwin, M., & Unwin, D. (2000) · Transactions in GIS · doi:10.1111/1467-9671.00058 · Cited by 156 · see citing works
Hierarchy in Cities and City Systems
Batty, M. (2006) · Springer eBooks · doi:10.1007/1-4020-4127-6_7 · Cited by 94 · see citing works
Self-citationPuebla, J. G., & Palomares, J. C. G. (2006) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2006.01.002 · Cited by 67 · see citing works
Wilson, A. G. (2014) · Routledge eBooks · doi:10.4324/9781315838045 · Cited by 49 · see citing works
The Polycentric Urban Region: Towards a Research Agenda
Kloosterman, S. M. R. C. (2001) · Urban Studies · doi:10.1080/00420980123921 · Cited by 43 · see citing works
Complex spatial systems : The modelling foundations of urban and regional analysis
Oosterhaven, J. (2002) · University of Groningen research database (University of Groningen / Centre for Information Technology) · Cited by 40 · see citing works
Flagged first, then by citations
4 of the 31 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [5]Fujita M Krugman P Venables A 2001 The Spatial Economy Cambridge,, MA The MIT Press
Cited by
Sorted by citations
Objective Criteria for the Evaluation of Clustering Methods
Rand, W. M. (1971) · Journal of the American Statistical Association · doi:10.1080/01621459.1971.10482356 · Cited by 6,493 · see citing works
Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
A universal model for mobility and migration patterns
Simini, F., González, M. C., Maritan, A., & Barabási, A.-Ĺ. (2012) · Nature · doi:10.1038/nature10856 · Cited by 1,480 · see citing works
Human mobility: Models and applications
Barbosa, H., Barthélemy, M., Ghoshal, G., James, C. R., Lenormand, M., Louail, T., de Menezes, R. P., Ramasco, J. J., Simini, F., & Tomasini, M. (2018) · Physics Reports · doi:10.1016/j.physrep.2018.01.001 · Cited by 1,032 · see citing works
Self-citationKees, J., Berry, C., Burton, S., & Sheehan, K. (2017) · Journal of Advertising · doi:10.1080/00913367.2016.1269304 · Cited by 902 · see citing works
Social Sensing: A New Approach to Understanding Our Socioeconomic Environments
Liu, Y., Liu, X., Gao, S., Gong, L., Kang, C., Zhi, Y., Chi, G., & Shi, L. (2015) · Annals of the Association of American Geographers · doi:10.1080/00045608.2015.1018773 · Cited by 804 · see citing works
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 · see citing works
Self-citationFrom 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 · see citing works
Self-citationRevealing travel patterns and city structure with taxi trip data
Liu, X., Gong, L., Gong, Y., & Liu, Y. (2015) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2015.01.016 · Cited by 409 · see citing works
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data
Liu, Y., Sui, Z., Kang, C., & Gao, Y. C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086026 · Cited by 393 · see citing works
Spatiotemporal Patterns of Urban Human Mobility
Hasan, S., Schneider, C., Ukkusuri, S. V., & González, M. C. (2012) · Journal of Statistical Physics · doi:10.1007/s10955-012-0645-0 · Cited by 282 · see citing works
Who Benefits from the "Sharing" Economy of Airbnb?
Giovanni Quattrone, A., Proserpio, D., Quercia, D., Capra, L., & Musolesi, M. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2874815 · Cited by 275 · see citing works
Long, Y., & Thill, J. (2015) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2015.02.005 · Cited by 265 · see citing works
A review of urban computing for mobile phone traces
Jiang, S., Fiore, G. A., Yang, Y., Ferreira, J., Frazzoli, E., & González, M. C. (2013) · ACM SIGKDD International Workshop on Urban Computing (UrbComp) · doi:10.1145/2505821.2505828 · Cited by 251 · see citing works
SHARED BICYCLES IN A CITY: A SIGNAL PROCESSING AND DATA ANALYSIS PERSPECTIVE
Borgnat, P., Abry, P., Flandrin, P., Robardet, C., Rouquier, J., & Eric Fleury, B. (2011) · Advances in Complex Systems · doi:10.1142/s0219525911002950 · Cited by 252 · see citing works
Big Data for Social Transportation
Zheng, X., Chen, W., Wang, P., Shen, D., Chen, S., Wang, X., Zhang, Q., & Yang, L. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2480157 · Cited by 243 · see citing works
Revealing travel patterns and city structure with taxi trip data
Liu, X., Li, G., Gong, Y., & Liu, Y. (2015) · RePEc: Research Papers in Economics · Cited by 246 · see citing works
Human mobility and socioeconomic status: Analysis of Singapore and Boston
Xu, Y., Belyi, A., Bojić, I., & Ratti, C. (2018) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2018.04.001 · Cited by 235 · see citing works
Rethinking big data: A review on the data quality and usage issues
Liu, J., Li, J., Li, W., & Wu, J. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.11.006 · Cited by 240 · see citing works
How congestion shapes cities: from mobility patterns to scaling
Louf, R., & Barthélemy, M. (2014) · Scientific Reports · doi:10.1038/srep05561 · Cited by 230 · see citing works
Self-citationDynamical Patterns of Cattle Trade Movements
Bajardi, P., Barrat, A., Natale, F., Savini, L., & Vittoria Colizza, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019869 · Cited by 214 · see citing works
Mapping Cilento: using geotagged social media data to characterize tourist flows in southern Italy
Chua, A., Servillo, L., Marcheggiani, E., & Moere, A. V. (2016) · Tourism Management · doi:10.1016/j.tourman.2016.06.013 · Cited by 216 · see citing works
Krol, S., Macrez, R., Docagne, F., Defer, G. L., Laurent, S., Rahman, M., Hajipour, M. J., Kehoe, P. G., & Mahmoudi, M. (2012) · Chemical Reviews · doi:10.1021/cr200472g · Cited by 217 · see citing works
Yue, 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 · see citing works
Hierarchical organization of urban mobility and its connection with city livability
Bassolas, A., Barbosa, H., Dickinson, B., Dotiwalla, X., Eastham, P., Gallotti, R., Ghoshal, G., Gipson, B. R., Hazarie, S. A., Kautz, H., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12809-y · Cited by 208 · see citing works
A city of cities: Measuring how 15-minutes urban accessibility shapes human mobility in Barcelona
Graells-Garrido, E., Serra-Burriel, F., Rowe, F., Cucchietti, F., & Reyes, P. (2021) · PLoS ONE · doi:10.1371/journal.pone.0250080 · Cited by 204 · see citing works
Uncovering the spatial structure of mobility networks
Louail, T., Lenormand, M., Picornell, M., García Cantú, O., Herranz, R., Frias-Martinez, E., Ramasco, J. J., & Barthelemy, M. (2015) · Nature Communications · doi:10.1038/ncomms7007 · Cited by 203 · see citing works
Self-citationKnockdown of MLO genes reduces susceptibility to powdery mildew in grapevine
Pessina, S., Lenzi, L., Perazzolli, M., Campa, M., Costa, L. D., Urso, S., Valè, G., Salamini, F., Velasco, R., & Malnoy, M. (2016) · Horticulture Research · doi:10.1038/hortres.2016.16 · Cited by 200 · see citing works
Universal predictability of mobility patterns in cities
Yan, X.-Y., Zhao, C., Fan, Y., Di, Z., & Wang, W.-X. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0834 · Cited by 197 · see citing works
Chen, T., Hui, E. C. M., Wu, J., Lang, W., & Li, X. (2019) · Habitat International · doi:10.1016/j.habitatint.2019.102005 · Cited by 196 · see citing works
Mahabir, R., Croitoru, A., Crooks, A. T., Agouris, P., & Stefanidis, A. (2018) · Urban Science · doi:10.3390/urbansci2010008 · Cited by 195 · see citing works
Revealing centrality in the spatial structure of cities from human activity patterns
Zhong, C., Schläpfer, M., Arisona, S. M., Batty, M., Ratti, C., & Schmitt, G. (2015) · Urban Studies · doi:10.1177/0042098015601599 · Cited by 192 · see citing works
Self-citationAgglomerations and the rise of urban network externalities
Burger, M., & Meijers, E. J. (2016) · Papers of the Regional Science Association · doi:10.1111/pirs.12223 · Cited by 187 · see citing works
Chopra, S. S., Dillon, T., Bilec, M. M., & Khanna, V. (2016) · Journal of The Royal Society Interface · doi:10.1098/rsif.2016.0113 · Cited by 183 · see citing works
Zhong, C., Batty, M., Manley, E., Wang, J., Wang, Z., Chen, F., & Schmitt, G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149222 · Cited by 175 · see citing works
Self-citationPark, S., Xu, Y., Jiang, L., Chen, Z., & Huang, S. (2020) · Annals of Tourism Research · doi:10.1016/j.annals.2020.102973 · Cited by 178 · see citing works
Predicting commuter flows in spatial networks using a radiation model based on temporal ranges
Ren, Y., Ercsey-Ravasz, M., Wang, P., González, M. C., & Toroczkai, Z. (2014) · Nature Communications · doi:10.1038/ncomms6347 · Cited by 177 · see citing works
Antibacterial activity of selected Malaysian honey
Zainol, M. I. B., Yusoff, K. M., & Yusof, M. Y. M. (2013) · BMC Complementary and Alternative Medicine · doi:10.1186/1472-6882-13-129 · Cited by 177 · see citing works
Understanding urban mobility patterns with a probabilistic tensor factorization framework
Sun, L., & Axhausen, K. W. (2016) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2016.06.011 · Cited by 175 · see citing works
Urban Mobility and Activity Space
Cagney, K. A., Cornwell, E. Y., Goldman, A. W., & Cai, L. (2020) · Annual Review of Sociology · doi:10.1146/annurev-soc-121919-054848 · Cited by 184 · see citing works
Unraveling the origin of exponential law in intra-urban human mobility
Liang, X., Zhao, J., Dong, L., & Xu, K. (2013) · Scientific Reports · doi:10.1038/srep02983 · Cited by 165 · see citing works
Diversity of individual mobility patterns and emergence of aggregated scaling laws
Yan, X.-Y., Han, X.-P., Wang, B.-H., & Zhou, T. (2013) · Scientific Reports · doi:10.1038/srep02678 · Cited by 160 · see citing works
Using mobility data as proxy for measuring urban vitality
Sulis, P., Manley, E., Zhong, C., & Batty, M. (2018) · Journal of Spatial Information Science · doi:10.5311/josis.2018.16.384 · Cited by 154 · see citing works
Self-citationHow congestion shapes cities: from mobility patterns to scaling.
Louf, R., & Barthelemy, M. (2014) · UCL Discovery (University College London) · Cited by 152 · see citing works
Modeling the Polycentric Transition of Cities
Louf, R., & Barthélemy, M. (2013) · Physical Review Letters · doi:10.1103/physrevlett.111.198702 · Cited by 146 · see citing works
Self-citationPolycentric urban development and urban thermal environment: A case of Hangzhou, China
Yue, W., Qiu, S., Hui, X., Xu, L., & Zhang, L. (2019) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2019.04.008 · Cited by 146 · see citing works
The Structure of Spatial Networks and Communities in Bicycle Sharing Systems
Austwick, M. Z., O’Brien, O., Strano, E., & Viana, M. P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074685 · Cited by 142 · see citing works
Evaluating the Privacy Risk of Location-Based Services
Freudiger, J., Shokri, R., & Hubaux, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-27576-0_3 · Cited by 141 · see citing works
Exploiting Foursquare and Cellular Data to Infer User Activity in Urban Environments
Noulas, A., Cecilia Mascolo, C., & Frı́as-Martı́nez, E. (2013) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2013.27 · Cited by 133 · see citing works
Inferring building functions from a probabilistic model using public transportation data
Zhong, C., Huang, X., Arisona, S. M., Schmitt, G., & Batty, M. (2014) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2014.07.004 · Cited by 131 · see citing works
Self-citationA Universal Model of Commuting Networks
Lenormand, M., Huet, S., Gargiulo, F., & Deffuant, G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045985 · Cited by 128 · see citing works
Renaissance of protein crystallization and precipitation in biopharmaceuticals purification
dos Santos, R., Carvalho, A. L., & Roque, A. C. A. (2016) · Biotechnology Advances · doi:10.1016/j.biotechadv.2016.11.005 · Cited by 125 · see citing works
Understanding mobility in a social petri dish
Szell, M., Sinatra, R., Petri, G., Stefan Thurner, A., & Latora, V. (2012) · Scientific Reports · doi:10.1038/srep00457 · Cited by 116 · see citing works
Zhang, Y., Marshall, S., & Manley, E. (2019) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2019.102485 · Cited by 116 · see citing works
From cognitive maps to spatial schemas
Farzanfar, D., Spiers, H. J., Moscovitch, M., & Rosenbaum, R. S. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-022-00655-9 · Cited by 115 · see citing works
A stochastic model of randomly accelerated walkers for human mobility
Gallotti, R., Bazzani, A., Rambaldi, S., & Barthélemy, M. (2016) · Nature Communications · doi:10.1038/ncomms12600 · Cited by 114 · see citing works
Self-citationSystematic review and meta-analysis of genetic risk factors for neuropathic pain
Veluchamy, A., Hébert, H. L., Meng, W., Palmer, C. N. A., & Smith, B. H. (2018) · Pain · doi:10.1097/j.pain.0000000000001164 · Cited by 112 · see citing works
Sun, Y., Fan, H., Li, M., & Zipf, A. (2015) · Environment and Planning B Planning and Design · doi:10.1177/0265813515617642 · Cited by 111 · see citing works
Extensive tissue-specific transcriptomic plasticity in maize primary roots upon water deficit
Opitz, N., Marcon, C., Paschold, A., Malik, W. A., Lithio, A., Brandt, R., Piepho, H., Nettleton, D. S., & Hochholdinger, F. (2015) · Journal of Experimental Botany · doi:10.1093/jxb/erv453 · Cited by 104 · see citing works
Saberi, M., Mahmassani, H. S., Brockmann, D., & Hosseini, A. (2016) · Transportation · doi:10.1007/s11116-016-9706-6 · Cited by 108 · see citing works
Quantifying the spatial homogeneity of urban road networks via graph neural networks
Xue, J., Jiang, N., Liang, S., Pang, Q., Yabe, T., Ukkusuri, S. V., & Ma, J. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00462-y · Cited by 105 · see citing works
Characterization of the MLO gene family in Rosaceae and gene expression analysis in Malus domestica
Pessina, S., Pavan, S., Catalano, D., Gallotta, A., Visser, R. G. F., Bai, Y., Malnoy, M., & Schouten, H. J. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-618 · Cited by 102 · see citing works
Detecting Urban Polycentric Structure from POI Data
Deng, Y., Liu, J., Liu, Y., & Luo, A. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8060283 · Cited by 102 · see citing works
Gan, Z., Yang, M., Feng, T., & Timmermans, H. J. P. (2018) · Transportation · doi:10.1007/s11116-018-9885-4 · Cited by 99 · see citing works
Improving the accessibility of urban transportation networks for people with disabilities
Ferrari, L., Berlingerio, M., Calabrese, F., & Reades, J. (2013) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2013.10.005 · Cited by 98 · see citing works
Predicting traffic volumes and estimating the effects of shocks in massive transportation systems
Silva, R., Kang, S. M., & Airoldi, E. M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1412908112 · Cited by 98 · see citing works
Self-citationFernández-Fueyo, E., Castanera, R., Ruiz-Dueñas, F. J., López-Lucendo, M. F., Ramírez, L., Pisabarro, A. G., & Martínez, A. T. (2014) · Fungal Genetics and Biology · doi:10.1016/j.fgb.2014.02.003 · Cited by 97 · see citing works
Analysis on spatiotemporal urban mobility based on online car-hailing data
Zhang, B., Chen, S., Ma, Y., Li, T., & Tang, K. (2019) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2019.102568 · Cited by 97 · see citing works
Schiller, L., Alava, J. J., Grove, J. S., Reck, G. K., & Pauly, D. F. (2014) · Aquatic Conservation Marine and Freshwater Ecosystems · doi:10.1002/aqc.2458 · Cited by 94 · see citing works
Yan, X., Sedykh, A., Wang, W., Zhao, X., Yan, B., & Zhu, H. (2019) · Nanoscale · doi:10.1039/c9nr00844f · Cited by 95 · see citing works
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 93 · see citing works
Self-citationRational Design to Improve Protein Thermostability: Recent Advances and Prospects
Yang, H., Liu, L., Li, J., Chen, J., & Du, G. (2015) · ChemBioEng Reviews · doi:10.1002/cben.201400032 · Cited by 93 · see citing works
Pelosi, C., Tinè, M. R., & Wurm, F. R. (2020) · European Polymer Journal · doi:10.1016/j.eurpolymj.2020.110079 · Cited by 92 · see citing works
Modification of the gravity model and application to the metropolitan Seoul subway system
Goh, S., Lee, K., Park, J. S., & Choi, M. Y. (2012) · Physical Review E · doi:10.1103/physreve.86.026102 · Cited by 92 · see citing works
Morphology of travel routes and the organization of cities
Lee, M., Barbosa, H., Youn, H., Holme, P., & Ghoshal, G. (2017) · Nature Communications · doi:10.1038/s41467-017-02374-7 · Cited by 88 · see citing works
Multi-scale spatio-temporal analysis of human mobility
Alessandretti, L., Sapieżyński, P., Lehmann, S., & Baronchelli, A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171686 · Cited by 87 · see citing works
Sobolevsky, S. L., Bojić, I., Belyi, A., Sitko, I., Hawelka, B., Arias, J. M., & Ratti, C. (2015) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2015.92 · Cited by 81 · see citing works
Ranking Station Importance With Human Mobility Patterns Using Subway Network Datasets
Xia, F., Wang, J., Kong, X., Zhang, D., & Wang, Z. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2920962 · Cited by 81 · see citing works
Reconstructing commuters network using machine learning and urban indicators
Spadon, G., de Carvalho, A. C. P. L. F., Rodrigues-Jr, J. F., & Alves, L. G. A. (2019) · Scientific Reports · doi:10.1038/s41598-019-48295-x · Cited by 81 · see citing works
Road network circuity in metropolitan areas
Giacomin, D. J., & Levinson, D. M. (2015) · Environment and Planning B Planning and Design · doi:10.1068/b130131p · Cited by 81 · see citing works
Predicting human mobility through the assimilation of social media traces into mobility models
Beiró, M. G., Panisson, A., Tizzoni, M., & Cattuto, C. (2016) · EPJ Data Science · doi:10.1140/epjds/s13688-016-0092-2 · Cited by 80 · see citing works
Zhang, S., Yang, Y., Zhen, F., Lobsang, T., & Li, Z. (2020) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2020.102938 · Cited by 79 · see citing works
Copy number variation detection using next generation sequencing read counts
Wang, H., Nettleton, D. S., & Ying, K. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-109 · Cited by 75 · see citing works
Lathia, N., & Capra, L. (2011) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2030112.2030152 · Cited by 74 · see citing works
Lathia, N., Smith-Clarke, C., Froehlich, J. E., & Capra, L. (2012) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2012.10.007 · Cited by 73 · see citing works
A universal opportunity model for human mobility
Liu, E., & Yan, X.-Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-61613-y · Cited by 73 · see citing works
Understanding taxi travel patterns
Cai, H., Zhan, X., Zhu, J., Jia, X., Chiu, A. S. F., & Xu, M. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2016.03.047 · Cited by 72 · see citing works
Zhou, Y., Fang, Z., Thill, J., Li, Q., & Li, Y. (2015) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2015.03.001 · Cited by 71 · see citing works
Hayfron-Benjamin, A., Obiri‐Yeboah, D., Ayisi-Addo, S., Siakwa, M., & Mupepi, S. C. (2018) · BMC Pediatrics · doi:10.1186/s12887-018-1330-5 · Cited by 69 · see citing works
Spatial structure and productivity in European regions
Ouwehand, W. M., Oort, F. G. V., & Cortinovis, N. (2021) · Regional Studies · doi:10.1080/00343404.2021.1950912 · Cited by 66 · see citing works
Cats, O., Wang, Q., & Zhao, Y. (2015) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2015.08.005 · Cited by 66 · see citing works
Cao, J., Li, Q., Tu, W., Gao, Q., Cao, R., & Zhong, C. (2021) · Cities · doi:10.1016/j.cities.2020.103077 · Cited by 65 · see citing works
Zhang, F., Li, Z., Li, N., & Fang, D. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.101671 · Cited by 64 · see citing works
Kong, X., Liu, Y., Wang, Y., Tong, D., & Zhang, J. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6020038 · Cited by 64 · see citing works
Mining mobility data to minimise travellers' spending on public transport
Lathia, N., & Capra, L. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020590 · Cited by 63 · see citing works
Identifying human mobility patterns using smart card data
Cats, O. (2023) · Transport Reviews · doi:10.1080/01441647.2023.2251688 · Cited by 61 · see citing works
Velocity-enhanced cooperation of moving agents playing public goods games
Cardillo, A., Meloni, S., Gómez‐Gardeñes, J., & Moreno, Y. (2012) · Physical Review E · doi:10.1103/physreve.85.067101 · Cited by 61 · see citing works
Distribution, abundance and habitat use of deep diving cetaceans in the North-East Atlantic
Rogan, E., Cañadas, A., Macleod, K., Santos, M. B., Mikkelsen, B., Uriarte, A., Canneyt, O. V., Vázquez, J. A., & Hammond, P. S. (2017) · Deep Sea Research Part II Topical Studies in Oceanography · doi:10.1016/j.dsr2.2017.03.015 · Cited by 60 · see citing works
Yu, W. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2834573 · Cited by 60 · see citing works
Schwanen, T. (2016) · Progress in Human Geography · doi:10.1177/0309132516628259 · Cited by 59 · see citing works
- Authors
- Roth, C., Kang, S. M., Batty, M., & Barthélemy, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- University College London (GB)
- Corresponding author
- Barthélemy, Marc
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-01-07
- Volume
- 6
- Issue
- 1
- Pages
- e15923
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3017563
- OpenAlex
- W2015480405
- Cited by
- 461
- References
- 31
- Citations by year
- 2026: 10 · 2025: 19 · 2024: 14 · 2023: 19 · 2022: 25 · 2021: 36 · 2020: 43 · 2019: 48 · 2018: 39 · 2017: 39 · 2016: 50 · 2015: 39 · 2014: 31 · 2013: 22 · 2012: 19
- FWCI
- 72.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Building and Construction › Urban Design and Spatial Analysis
- Social Sciences › Urban Studies › Global Urban Networks and Dynamics
- Keywords
- urban mobility, activity centers, flow heterogeneity, urban hubs
- MeSH terms
- City Planning, Humans, London, Population Dynamics, Urban Population
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
