COVID-19 is linked to changes in the time-space dimension of human mobility

• URL: http://arxiv.org/abs/2201.06527v3
• Date: Thu, 27 Jul 2023 16:48:06 GMT
• Title: COVID-19 is linked to changes in the time-space dimension of human mobility
• Authors: Clodomir Santana, Federico Botta, Hugo Barbosa, Filippo Privitera, Ronaldo Menezes and Riccardo Di Clemente
• Abstract summary: During coronavirus disease 2019 pandemic, mobility patterns were reshaped. During lockdowns restrictions, the decrease of spatial mobility is interwoven with the emergence of asynchronous mobility dynamics. In rural and low-income areas, the spatial mobility dimension suffered a more considerable disruption when compared with urbanized and high-income areas.
• Score: 0.2544539499281092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Socio-economic constructs and urban topology are crucial drivers of human mobility patterns. During the coronavirus disease 2019 pandemic, these patterns were reshaped in their components: the spatial dimension represented by the daily travelled distance, and the temporal dimension expressed as the synchronization time of commuting routines. Here, leveraging location-based data from de-identified mobile phone users, we observed that, during lockdowns restrictions, the decrease of spatial mobility is interwoven with the emergence of asynchronous mobility dynamics. The lifting of restriction in urban mobility allowed a faster recovery of the spatial dimension compared with the temporal one. Moreover, the recovery in mobility was different depending on urbanization levels and economic stratification. In rural and low-income areas, the spatial mobility dimension suffered a more considerable disruption when compared with urbanized and high-income areas. In contrast, the temporal dimension was more affected in urbanized and high-income areas than in rural and low-income areas.

Related papers

• Learning Multi-Modal Mobility Dynamics for Generalized Next Location Recommendation [51.00494428978262]
  We leverage multi-modal spatial-temporal knowledge to characterize mobility dynamics for the location recommendation task.<n>First, we construct a unified spatial-temporal relational graph (STRG) for multi-modal representation.<n>Second, we design a gating mechanism to fuse spatial-temporal graph representations of different modalities.
  arXiv  Detail & Related papers  (2025-12-27T14:23:04Z)
• Street network sub-patterns and travel mode [0.5670066649802191]
  We classified the built environment of nine U.S. metropolitan areas using structural indicators such as density, connectivity, and spatial configuration.<n>The resulting morphological types were linked to mobility patterns through descriptive statistics, marginal effects estimation, and post hoc statistical testing.<n>These results suggest that urban form should be treated as a key variable in mobility planning and provide empirical support for incorporating spatial typologies into sustainable urban policy design.
  arXiv  Detail & Related papers  (2025-07-25T19:49:51Z)
• Exploring the effect of spatial scales in studying urban mobility pattern [0.0]
  This paper explores the impact of spatial scales on the performance of the gravity model in explaining urban mobility patterns using public transport flow data in Singapore.<n>Results indicate the existence of an optimal intermediate spatial scale at which the gravity model performs best.
  arXiv  Detail & Related papers  (2025-06-20T05:39:39Z)
• Graph Network Modeling Techniques for Visualizing Human Mobility Patterns [8.537183852577687]
  We develop a methodology by embedding graphs into a continuous space, which alleviates issues related to fast graph matching, graph time-series modeling, and visualization of mobility dynamics. We demonstrate how mobility data collected from trajectories could be transformed into network structures and patterns of mobility flow changes, and can be used for downstream tasks reporting approx 40% decrease in error on average in matched graphs vs unmatched ones.
  arXiv  Detail & Related papers  (2025-04-04T02:21:44Z)
• InclusiViz: Visual Analytics of Human Mobility Data for Understanding and Mitigating Urban Segregation [41.758626973743525]
  InclusiViz is a novel visual analytics system for multi-level analysis of urban segregation.<n>We developed a deep learning model to predict mobility patterns across social groups using environmental features, augmented with explainable AI.<n>The system integrates innovative visualizations that allow users to explore segregation patterns from broad overviews to fine-grained detail.
  arXiv  Detail & Related papers  (2025-01-07T07:50:36Z)
• COLA: Cross-city Mobility Transformer for Human Trajectory Simulation [44.157114416533915]
  We develop a Cross-city mObiLity trAnsformer (COLA) with a dedicated model-agnostic transfer framework. COLA divides the Transformer into the private modules for city-specific characteristics and the shared modules for city-universal mobility patterns. Our implemented cross-city baselines have demonstrated its superiority and effectiveness.
  arXiv  Detail & Related papers  (2024-03-04T07:45:29Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Differences of communication activity and mobility patterns between urban and rural people [0.0]
  This study uses the call detail records (CDRs) to analyze the social communication and mobility patterns of people. Results show that in the urban areas people have high calling activity but low mobility, while in the rural areas they show the opposite behaviour. Age and gender of individuals have also been observed to play a role in the seasonal patterns differently in urban and rural areas.
  arXiv  Detail & Related papers  (2023-11-22T19:10:14Z)
• Mobility Segregation Dynamics and Residual Isolation During Pandemic Interventions [0.0]
  We study the reorganisation of mobility segregation networks due to external shocks during pandemics. We build on anonymised and privacy-preserved mobility data in four cities: Bogota, Jakarta, London, and New York. We find that the first lockdowns induced considerable increases in mobility segregation in each city, while loosening mobility restrictions did not necessarily diminished isolation between different socioeconomic groups.
  arXiv  Detail & Related papers  (2023-10-05T14:08:44Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• Multi-Temporal Relationship Inference in Urban Areas [75.86026742632528]
  Finding temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. We propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation learning and further handle the relationship sparsity.
  arXiv  Detail & Related papers  (2023-06-15T07:48:32Z)
• Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments [110.79706180350507]
  We show that our proposed loss can be used to define temporal-temporal baryechecenters as Fr'teche means duality. Experiments on handwritten letters and brain imaging data confirm our theoretical findings.
  arXiv  Detail & Related papers  (2022-03-11T09:46:22Z)
• Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition [62.46544616232238]
  Previous motion recognition methods have achieved promising performance through the tightly coupled multi-temporal representation. We propose to decouple and recouple caused caused representation for RGB-D-based motion recognition.
  arXiv  Detail & Related papers  (2021-12-16T18:59:47Z)
• Mobility signatures: a tool for characterizing cities using intercity mobility flows [1.1602089225841632]
  We introduce the mobility signature as a tool for understanding how a city is embedded in the wider mobility network. We demonstrate the potential of the mobility signature approach through two applications that build on mobile-phone-based data from Finland.
  arXiv  Detail & Related papers  (2021-12-03T08:53:58Z)
• Exploring the spatiotemporal heterogeneity in the relationship between human mobility and COVID-19 prevalence using dynamic time warping [0.0]
  Previous studies have revealed the correlation between human mobility and COVID-19 cases. This study aims to identify heterogeneities in the relationship between human mobility and COVID-19 cases in U.S. counties.
  arXiv  Detail & Related papers  (2021-09-28T14:38:29Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• Multiple abrupt phase transitions in urban transport congestion [0.0]
  We show that the location of the onset of congestion changes according to the considered urban area. We introduce a family of planar road network models composed of a dense urban center connected to an arboreal periphery.
  arXiv  Detail & Related papers  (2020-05-26T17:58:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.