The DOI matches its Crossref record; 89 of the 90 references it cites were found in a registry or an index.
The scales of human mobility
Open accessAbstract
There is a contradiction at the heart of our current understanding of individual and collective mobility patterns. On the one hand, a highly influential body of literature on human mobility driven by analyses of massive empirical datasets finds that human movements show no evidence of characteristic spatial scales. There, human mobility is described as scale free 1-3. On the other hand, geographically, the concept of scale-referring to meaningful levels of description from individual buildings to neighbourhoods, cities, regions and countries-is central for the description of various aspects of human behaviour, such as socioeconomic interactions, or political and cultural dynamics 4,5. Here we resolve this apparent paradox by showing that day-to-day human mobility does indeed contain meaningful scales, corresponding to spatial 'containers' that restrict mobility behaviour. The scale-free results arise from aggregating displacements across containers. We present a simple model-which given a person's trajectory-infers their neighbourhood, city and so on, as well as the sizes of these geographical containers. We find that the containers-characterizing the trajectories of more than 700,000 individuals-do indeed have typical sizes. We show that our model is also able to generate highly realistic trajectories and provides a way to understand the differences in mobility behaviour across countries, gender groups and urban-rural areas.
Overview
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
Verification
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?
The cited source says what the claim attributes to it.
In the citing paper
“Still, human mobility is known to follow certain universal principles, such as scaling laws of mobility (Alessandretti et al. 2020;Pappalardo et al. 2016;Song et al. 2010b), which suggests that our findings may reflect more general behavioral regularities.”
From Visitation patterns across mobility groups: wandering, commuting, and exploring (2026) · cites this paper as Alessandretti et al. 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Power-Law Distributions in Empirical Data
Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009) · SIAM Review · doi:10.1137/070710111 · Cited by 8,019
Power-law distributions in empirical data
Clauset, A. J., Shalizi, C. R., & Newman, M. E. J. (2018) · Figshare · doi:10.1184/r1/6586835.v1 · Cited by 6,710
Understanding individual human mobility patterns
González, M. C., Hidalgo, C. A., & Barabási, A.-Ĺ. (2008) · Nature · doi:10.1038/nature06958 · Cited by 6,186
Newman (2005) · doi:10.1080/00107510500052444 · Cited by 5,749
Location and Land Use: Toward a General Theory of Land Rent
Alonso, W. (1964) · Cited by 3,820
Alonso, W. (1964) · Harvard University Press eBooks · doi:10.4159/harvard.9780674730854 · Cited by 3,673
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 effect of human mobility and control measures on the COVID-19 epidemic in China
Kraemer, M. U. G., Yang, C.-H., Gutiérrez, B., Wu, C., Klein, B., Pigott, D. M., open COVID-19 data working group, du Plessis, L., Faria, N. R., Li, R., et al. (2020) · Science · doi:10.1126/science.abb4218 · Cited by 2,987
On the Move: Mobility in the Modern Western World
Cresswell, T. (2006) · Cited by 2,468
The scaling laws of human travel
Brockmann, D., Hufnagel, L., & Geisel, T. (2006) · Nature · doi:10.1038/nature04292 · Cited by 2,182
Research works printed but not linked: 4
- Larsen, J. & Urry, J. Mobilities, Networks, Geographies (Routledge, 2016).
- Alessandretti, L., Sapiezynski, P., Sekara, V., Lehmann, S. & Baronchelli, A. Evidence for a conserved quantity in human mobility. Nat. Hum. Behav. 2, 485–491 (2018). · DOI
- Batty, M. in Hierarchy in Natural and Social Sciences (ed. Pumain, D.) 143–168 (Springer, 2006).
- Train, K. E. Discrete Choice Methods with Simulation (Cambridge Univ. Press, 2009).
Cited by
Sorted by citations
Items 201–300 of 361
Huang, S., Du, Y., Yi, J., Zhao, Z., Lu, M., Hu, D., Wang, N., Qian, J., & Tu, W. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104910 · Cited by 4
Experienced economic segregation and associated mental health inequalities across urbanicity
Zhou, Y., & Lü, Y. (2025) · Social Science & Medicine · doi:10.1016/j.socscimed.2025.118813 · Cited by 3
Disentangling the impacts of collective mobility of residents and non-residents on burglary levels
Chen, T., BOWERS, K. J., & Cheng, T. (2026) · Computational Urban Science · doi:10.1007/s43762-025-00234-5 · Cited by 3
Revealing the fractal and self-similarity of realistic collective human mobility
Jiang, J., Xu, Z., Zhang, Z., Zhang, J., Liu, K., & Kong, H. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.129232 · Cited by 3
Lu, J., Zhou, S., & Xu, Y. (2025) · Applied Geography · doi:10.1016/j.apgeog.2025.103609 · Cited by 3
CityChrone: an Interactive Platform for Transport Network Analysis and Planning in Urban Systems
Biazzo, I. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-93413-2_64 · Cited by 3
Tian, Y., Yao, X. A., Madden, M., & Grundstein, A. (2023) · Journal of Geographical Systems · doi:10.1007/s10109-023-00424-x · Cited by 3
Manaf, M., Amri, E., Syafri, S., & Aksa, K. (2024) · International Review for Spatial Planning and Sustainable Development · doi:10.14246/irspsd.12.4_159 · Cited by 3
Urban-EPR: a universal model for simulating individual human mobility within intra-urban areas
Jin, X., Liu, K., Cao, Z., Yin, L., Luo, Y., & Zhao, X. (2025) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2025.2456558 · Cited by 3
Zhong, Z., Hideki Takayasu, C., & Takayasu, M. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.013235 · Cited by 3
Sadeghi, M., Karimi, M., Rabiei‐Dastjerdi, H., & Sarkar, D. (2025) · Cartography and Geographic Information Science · doi:10.1080/15230406.2025.2492670 · Cited by 3
Alessandretti, L., & Szell, M. (2025) · Understanding complex systems · doi:10.1007/978-3-031-82666-5_4 · Cited by 3
Self-citationMethods for Travel Pattern Analysis Using Large-Scale Passive Data
Breyer, N. (2021) · Linköping studies in science and technology. Dissertations · doi:10.3384/diss.diva-175347 · Cited by 3
Emergence, survival, and segregation of competing gangs
Pérez-Martínez, H., Mingueza, F. B., Soriano‐Paños, D., Gómez‐Gardeñes, J., & Floría, L. M. (2022) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0084972 · Cited by 3
A human mobility dataset collected via LBSLab
Zhang, Y., Gong, Q., Chen, Y., Xiao, Y., Wang, X., Hui, P., & Fu, X. (2023) · Data in Brief · doi:10.1016/j.dib.2023.108898 · Cited by 3
Exploring the interplay between population profile and optimal routes in U.S. cities
Ortega, D. P., & Korutcheva, E. (2025) · Scientific Reports · doi:10.1038/s41598-025-00308-8 · Cited by 4
Unveiling the hidden effects of multilevel administrative boundaries on human mobility
Yu, L., Zhao, P., Zhang, M., & Feng, Y. (2025) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2025.104337 · Cited by 2
Ge, D., Niu, B., Sun, P., & Li, Y. (2026) · Cities · doi:10.1016/j.cities.2026.106826 · Cited by 2
Herrero-Zamorano, M. T., Navío-Marco, J., Bujidos-Casado, M., & Mendieta-Aragón, A. (2026) · Telecommunications Policy · doi:10.1016/j.telpol.2026.103205 · Cited by 2
Assessing the impact of spatial scales in modelling urban mobility pattern
Huynh, H. N. (2026) · Journal of Computational Science · doi:10.1016/j.jocs.2026.102910 · Cited by 2
Biazzo, I., Nezhadhaghighi, M. G., & Ramezanpour, A. (2021) · Journal of Physics Complexity · doi:10.1088/2632-072x/ac266d · Cited by 2
Rural Residents’ Travel Behaviour in Mega-City Regions
Zhao, P., & Hu, H. (2022) · Population, regional development and transport · doi:10.1007/978-981-19-6498-5_8 · Cited by 2
The Exponential Scaling Law for Taxi Mobility Patterns
Li, M., Guo, G., & Zhang, X. (2023) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2023.3330111 · Cited by 2
Computational social science in regional analysis and the European real estate market
Gabrielli, L., Sulis, P., Fontana, M., Signorelli, S., Vespe, M., & Lavalle, C. (2024) · Regional Studies · doi:10.1080/00343404.2024.2329238 · Cited by 2
Data-driven anatomy of hierarchical migration patterns in the United States
Yan, X., Han, H., Su, X., & Fan, C. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103825 · Cited by 2
Vanni, F. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.115175 · Cited by 2
Quasi-critical dynamics in large-scale social systems regulated by sudden events
Guo, P., Xu, Y., Guo, S., Tian, Y., & Sun, P. (2024) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0218422 · Cited by 2
de Vries, I., & Davies, T. (2024) · Criminology · doi:10.1111/1745-9125.12381 · Cited by 2
Rames, C. L., Rhoads, D., Meseguer‐Artola, A., Lozano, S., Borge‐Holthoefer, J., & Solé‐Ribalta, A. (2024) · Cities · doi:10.1016/j.cities.2024.105655 · Cited by 2
Understanding Individual-Space Relationships to Inform and Enhance Location-Based Applications
Amichi, L., Thakur, G. M., & Christopher, S. C. (2024) · ACM SIGSPATIAL Workshop Recommendations for Location-based Services and Social Networks · doi:10.1145/3681773.3699694 · Cited by 2
Chen, L., & Lu, Y. (2025) · Travel Behaviour and Society · doi:10.1016/j.tbs.2025.101031 · Cited by 2
Zeng, X., Song, Y., & Zhu, D. (2025) · Transactions in Urban Data Science and Technology · doi:10.1177/27541231251333348 · Cited by 2
Introducing Friction of Space into the Geography of Cultural Consumption
Biferale, L., Crociata, A., Mori, L. R., Chiappetta, C., & Bruno, M. (2025) · Urban Science · doi:10.3390/urbansci9080316 · Cited by 2
Bigdata-based investigation of heterogeneous migration and population distribution
Fan, Z. W., Xiong, L., & Zheng, B. (2021) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/ac4624 · Cited by 2
Samuelsson, K., Brandt, S. A., Barthel, S., Linder, N., Lim, N. J., Hallman, D., & Giusti, M. (2024) · Geography and sustainability · doi:10.1016/j.geosus.2024.05.002 · Cited by 2
Anonymised human location data in England for urban mobility research
Zhong, C., Zhou, Z., Aslam, N. S., Wang, Y., & Enaya, A. (2025) · Scientific Data · doi:10.1038/s41597-025-06323-8 · Cited by 3
A cost opportunity model for human mobility
Wang, Y., Liu, E., Zhao, D., Niu, X., Wang, X., & Lv, Y.-Y. (2024) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2024.129847 · Cited by 1
Downscaling spatial interaction with socioeconomic attributes
Tang, C., Dong, L., Guo, H., Wang, X., Chen, X., Dong, Q., & Liu, Y. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00487-w · Cited by 1
Hong, Y., Xin, Y., Dirmeier, S., Pérez‐Cruz, F., & Raubal, M. (2025) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2025.101398 · Cited by 3
Job loss disrupts individuals’ mobility and their exploratory patterns
Centellegher, S., Nadai, M. D., Tonin, M., Lepri, B., & Lucchini, L. (2025) · iScience · doi:10.1016/j.isci.2025.112892 · Cited by 1
Exploring the Effect of Spatial Scales in Studying Urban Mobility Pattern
Huynh, H. N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97629-2_5 · Cited by 1
Continuous-time process for human contact dynamics
Persoons, R., D'Alessandro, M., & Mieghem, P. V. (2025) · Physical review. E · doi:10.1103/lt46-nrh4 · Cited by 1
Navigating the safe and just operating space for urban systems: A cross-scale landscape approach
Bian, H., Wu, J., Jia, R., Wang, L., Zhu, Z., Wei, M., & Gao, J. (2025) · Geography and sustainability · doi:10.1016/j.geosus.2025.100352 · Cited by 1
A nonlinear opportunity benefit model for human mobility prediction
Liu, E., Wang, Y., Zhao, D., & Lu, X. B. (2025) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2025.130867 · Cited by 1
Mobile phone application data for activity plan generation
Tozluoğlu, Ç., Liao, Y. J., & Sprei, F. (2025) · Transportation · doi:10.1007/s11116-025-10679-6 · Cited by 1
Ascione, C., & Valdano, E. (2025) · Physical Review Research · doi:10.1103/32b8-x9z5 · Cited by 1
Analysing polycentricity: challenges and a roadmap
Ao, X., Schwanen, T., & Wójcik, D. (2025) · Regional Studies · doi:10.1080/00343404.2025.2587810 · Cited by 1
Probabilistic space-time prisms in road networks with travel time uncertainties
Chen, H.-P., Li, Z.-C., & Chen, B. Y. (2026) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2025.104544 · Cited by 1
A hybrid deep learning model for human mobility prediction
Liu, E., Wang, X., Wang, Y., Zhao, D., Niu, X., & Lu, X. (2026) · Journal of Physics Complexity · doi:10.1088/2632-072x/ae35ba · Cited by 1
Luo, X., Zhang, W., Chai, Y., & Xie, J. (2026) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi15020056 · Cited by 1
Transmission lineage dynamics and the detection of viral importation in emerging epidemics
Tsui, J. L.-H., Sambaturu, P., Pena, R. E., Too, L., Gutierrez, B., Inward, R. P. D., Kraemer, M. U. G., du Plessis, L., & Pybus, O. G. (2026) · Epidemics · doi:10.1016/j.epidem.2026.100893 · Cited by 2
Loreto, V., Servedio, V. D. P., & Tria, F. (2026) · doi:10.1093/9780191834615.001.0001 · Cited by 1
Massive Retail Location Choice as a Human‐Flow‐Covering Problem
Jiang, H., Li, Y., Lu, Q., Liu, Y., Xu, L., & Zhang, H. (2026) · Geographical Analysis · doi:10.1111/gean.70036 · Cited by 1
Estimating traffic from crowdsourced location activity: A new data source perspective
Törő, O., Esztergár‐Kiss, D., Schmöcker, J., & Tettamanti, T. (2026) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2026.101973 · Cited by 1
Multilayer urban commuting networks reveal inequality in epidemic dynamics in cities
Chen, Y., Zeng, L., Tang, M., Liu, Y., Jin, Z., & Macau, E. E. N. (2026) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2026.131584 · Cited by 1
Wang, J., Yang, C., Yong, J., Xu, X., Wang, H., Koshizuka, N., Fukushima, S., Shibasaki, R., & Jiang, R. (2026) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2026.105862 · Cited by 2
A Multi-Center Method for Estimating Individual Activity Spaces with Visit Probability
Wang, J., Yuan, Z., Wang, Y., & Chen, B. Y. (2020) · International Conference on Information Technology: IoT and Smart City (ICIT) · doi:10.1145/3446999.3447003 · Cited by 1
A long-term travel delay measurement study based on multi-modal human mobility data
Fang, Z., Wang, G., Yang, Y., Zhang, F., Wang, Y., & Zhang, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-19394-z · Cited by 1
A Management Method of Multi-Granularity Dimensions for Spatiotemporal Data
Cao, W., Liu, W., Tong, X., Wang, J., Peng, F., Tian, Y., & Zhu, J. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi12040148 · Cited by 1
Zheng, X. (2023) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.324072 · Cited by 1
Modeling Human Mobility Based on Temporal Characteristics
Xie, Y., Yu, Z., Zhang, Y., & Guo, B. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00089 · Cited by 1
Decoding Urban Dynamics: Contextual Insights from Human Meta-Mobility Patterns
Oh, S., Joo, S., Kim, S., & Kim, M. (2024) · Systems · doi:10.3390/systems12080313 · Cited by 1
A Laplacian Evolution Approach to Identify the Source-Sink Pattern of Human Mobility in Urban
Zhang, P., Cong, W., Shi, Y., Yang, J., & Wang, Q. (2024) · International Conference on Pattern Recognition Systems (ICPRS) · doi:10.1109/icprs62101.2024.10677813 · Cited by 1
Dynamics of collective information processing for risk encoding in social networks during crises
Fan, C. H., Wu, F., & Mostafavi, A. (2025) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2025.105451 · Cited by 1
Investigation on the Spatio-Temporal Mobility and Smartphone Usage of College Students
Lai, X., Zhao, J., Dong, L., Li, B., & Rau, P. P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06053-3_12 · Cited by 1
An implementation of a multilayer network model for the Covid-19 pandemic: A Costa Rica study
Calvo, J. G., Sánchez, F., Barboza, L. A., García, Y. E., & Vásquez, P. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023024 · Cited by 1
Interaction Theory of Hazard-Target SystemPreprint
Ge, J., Zhang, Y., Xu, K., Li, J., Yao, X., Wu, C., Li, S.-Y., Yan, F., Zhang, J., & Xu, Q. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04340 · Cited by 1
Assessing the epidemic impact of protests during the COVID-19 pandemicPreprint
Hong, I., Ferreira, L. N., Rutherford, A., & Cebrián, M. (2022) · medRxiv · doi:10.1101/2022.10.10.22280896 · Cited by 1
Towards algorithmic city planning: data-driven indicators for policies and planning
Kaufmann, T. (2022) · Digital Repository Service (Northeastern University) · doi:10.17760/d20467300 · Cited by 1
Urban bicycle networks, existing and synthetically grown
Szell, M. (2021) · IT University Of Copenhagen (IT University of Copenhagen) · doi:10.5281/zenodo.5083048 · Cited by 1
Transmission lineage dynamics and the detection of viral importation in emerging epidemicsPreprint
Tsui, J. L.-H., Sambaturu, P., Pena, R. E., Too, L., Gutiérrez, B., Inward, R. P. D., Kraemer, M. U. G., du Plessis, L., & Pybus, O. G. (2025) · medRxiv · doi:10.1101/2025.03.05.25323408 · Cited by 1
Visitation patterns across mobility groups: wandering, commuting, and exploring
Hołubowska, O., Smolak, K., Brum-Bastos, V., & Sila-Nowicka, K. (2026) · Journal of Geographical Systems · doi:10.1007/s10109-026-00501-x
A Mobility Model for Synthetic Travel Demand from Sparse Individual Traces
Liao, Y. J., Ek, K., Wennerberg, E., Yeh, S., & Gil, J. (2021) · Chalmers Research (Chalmers University of Technology)
Conceptions of space in active living research
Kajosaari, A. (2021) · Aaltodoc (Aalto University)
Framework for Network-Constrained Target TrackingPreprint
Vial, A., Hendeby, G., Daamen, W., van Arem, B., & Hoogendoorn, S. P. (2022) · doi:10.31219/osf.io/y7qf3
赵, 鹏., 胡, 昊., 曾, 良., 陈, 军., & 叶, 信. (2022) · SCIENTIA SINICA Terrae · doi:10.1360/n072022-0047
Samuelsson, K., Brandt, S. A., Barthel, S., Linder, N., Lim, N. J., & Giusti, M. (2023) · doi:10.31219/osf.io/2m3st
Brelsford, C. M., & Jones, A. (2021) · Lawrence Berkeley National Laboratory · doi:10.2172/2007465
Predictive infrequent activities
Fan, Y. (2024) · Nature Cities · doi:10.1038/s44284-024-00056-2
Senapati, A., Mertel, A., Schlechte-Wełnicz, W., & Calabrese, J. M. (2024) · Mathematics · doi:10.3390/math12132065
Zhu, C., Susskind, J., Chernicoff, W., Cong, C., & Berger, A. M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4895796
Kassan, S., Mnakri, B., Nagellen, T., Guyard, F., & Tosic, T. (2024) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc61514.2024.10592448
Lévy Flight and the Interpersonal Distance of a Pedestrian in a Crowd
Barillé, R. (2024) · Applied Sciences · doi:10.3390/app14177585
Kane, M. J., Gilani, O., Khusainova, E., & Urbanek, S. (2024) · Applied Network Science · doi:10.1007/s41109-024-00659-7
Urban Interior Boundaries Delimitation Respecting to Human Mobility
Wei, Y., & Zhao, X. (2024) · IEEE Access · doi:10.1109/access.2024.3487921
Pattern detection in the vehicular activity of bus rapid transit systems
Martínez-González, J. U., Riascos, A. P., & Mateos, J. L. (2024) · PLoS ONE · doi:10.1371/journal.pone.0312541
An Innovative Transportation Vehicle Design: Hyper Speed Transportation System
Pirli, A. A., & Ersoy, S. (2024) · Proceedings in automotive engineering · doi:10.1007/978-3-031-77635-9_14
Gunn, E. O., & Deinne, C. E. (2024) · Journal of Transportation Technologies · doi:10.4236/jtts.2025.151004
An evolution model for the mobility of urban societal classes based on complex networks
Zhou, Y., Yang, L., Lei, J., Wu, R.-J., & Kong, Y.-X. (2025) · Journal of Physics Complexity · doi:10.1088/2632-072x/adaa45
Puspita, I. B., Kurniawan, A., & Marfai, M. A. (2025) · Journal of Disaster Research · doi:10.20965/jdr.2025.p0089
Mapping the Convergence of Social Physics and Geospatial Analysis: A Bibliometric Study
Rai, A. (2024) · Springer geography · doi:10.1007/978-3-031-77596-3_2
Statistical Analysis of Human Mobility in Online Communities
Wang, Y., Yu, Z., Zhang, Y., Hu, S., & Guo, B. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-2373-0_7
Data-Driven Travel Analysis for Mobility Tracking
Hong, Y., Xin, Y., Martin, H., Bucher, D., & Raubal, M. (2025) · Journal of Information Organization · doi:10.6025/jio/2025/15/1/18-33
Gender variation in active mobility: experience from Johannesburg South Africa
Aderibigbe, O.-O. B., & Gumbo, T. (2025) · Frontiers in Built Environment · doi:10.3389/fbuil.2025.1603221
Wu, R., Wen, Y., Wu, X., Huang, C., Fu, Q., Hu, Q., Wang, H., Wu, Y., & Zhang, S. (2025) · Environmental Research Infrastructure and Sustainability · doi:10.1088/2634-4505/aded34
The Spatial Determinants of Academic Aspirations: Evidence From Sweden
Lecoursonnais, M., Brandén, M., & Rosenqvist, E. (2025) · Population Space and Place · doi:10.1002/psp.70082
Community aware temporal network generation
Girardini, N. A., Longa, A., Trebucchi, G., Cencetti, G., Passerini, A., & Lepri, B. (2025) · Applied Network Science · doi:10.1007/s41109-025-00731-w
Dead Ends, Lost Time: Rural Mobility ConstraintsPreprint
Liu, J., Zhao, P., Liu, Z., Jiang, S., Wang, H., Zhao, H., Li, Z., Tang, Y., & Liu, Q. (2025) · Research Square · doi:10.21203/rs.3.rs-6871842/v1
Learning the Node Distribution of Biased Waypoint Mobility using Mixture Density Networks
Zhao, S., & Gao, W. (2025) · International Conference on Computer Communication and Artificial Intelligence (CCAI) · doi:10.1109/ccai65422.2025.11189455
Metadata
- Authors
- Alessandretti, L., Aslak, U., & Lehmann, S.
- Affiliations
- University of Copenhagen (DK)
- Corresponding author
- Lehmann, Sune
- Venue
- Nature
- Published
- 2020-11-18
- Volume
- 587
- Issue
- 7834
- Pages
- 402-407
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3098208509
- Cited by
- 361
- References
- 90
- Citations by year
- 2026: 74 · 2025: 71 · 2024: 76 · 2023: 48 · 2022: 55 · 2021: 34 · 2020: 3
- FWCI
- 31.45
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Urban Studies › Urban Design and Spatial Analysis
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- human mobility, spatial scale, neighborhood size, city size, single-cell trajectory analysis, power-law distribution, mobility behavior, urban-rural differences, gender differences
- MeSH terms
- Built Environment, Denmark, Humans, Models, Theoretical, Reproducibility of Results, Human Migration
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