ArticleNature communications2021
A Deep Gravity model for mobility flows generation.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Outfitting the quest for spatial spread of infections: A review of mobility datasets for population health modelling.Infectious Disease Modelling · 2026Review
- One rule does not fit all: Deviations from universality in human mobility modeling.PNAS nexus · 2026Article
- Structural inequality shapes the daily rhythm of stationarity and mobility.Nature communications · 2026Article
- Deep learning four decades of human migration.Nature · 2026Article
- Simulation of long-term spatio-temporal environmental dynamics using a unified benchmark of neighbor augmenting, LSTM and graph attention models.Scientific reports · 2026Article
- A Global Intra-city Commuting Origin-Destination Flow Dataset for Urban Sustainable Development.Scientific data · 2026Article
- Uncovering latent urban mobility patterns via smart-card and survey data fusion.Nature communications · 2026Article
- Event-based spatiotemporal networks for modelling emergent phenomena in complex systems.Nature communications · 2026Article
- Risk mapping novel respiratory pathogens with large-scale dynamic contact networks.Communications medicine · 2026Article
- Distance-amplified power-law distributions better characterize human long-distance travel.Scientific reports · 2026Article
- Article
- From crisis to new routine: shifts in urban shopping mobility and socioeconomic inequality during and after the COVID-19 shock.Frontiers in big data · 2026Article
- Regime-adaptive partial differential equations for interpretable multi-ethnic urban modeling.Scientific reports · 2025Article
- Predicting human mobility flows in cities using deep learning on satellite imagery.Nature communications · 2025Article
- The effective intervening opportunity model for seasonal human mobility.Innovation (Cambridge (Mass.)) · 2025Article
- Article
- Mixing individual and collective behaviors to predict out-of-routine mobility.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Human mobility is well described by closed-form gravity-like models learned automatically from data.Nature communications · 2025Article
- Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations.Nature communications · 2024Article
- Drivers of global tourism carbon emissions.Nature communications · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model's prediction with explainable AI techniques.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.