Changes in the time-space dimension of human mobility during the COVID-19 pandemic

Socio-economic constructs and urban topology are crucial drivers of human mobility patterns. During the COVID-19 pandemic, these patterns were re-shaped in their main two components: the spatial dimension represented by the daily travelled distance, and the temporal dimension expressed as the synchronisation time of commuting routines. 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 to the temporal one. Moreover, the recovery in mobility was different depending on urbanisation levels and economic stratification. In rural and low-income areas, the spatial mobility dimension suffered a more significant disruption when compared to urbanised and high-income areas. In contrast, the temporal dimension was more affected in urbanised and high-income areas than in rural and low-income areas.

[1]  R. Keil,et al.  Global Cities and the Spread of Infectious Disease: The Case of Severe Acute Respiratory Syndrome (SARS) in Toronto, Canada , 2006 .

[2]  J. Vanderplas Understanding the Lomb–Scargle Periodogram , 2017, 1703.09824.

[3]  Yoshihide Sekimoto,et al.  Non-Compulsory Measures Sufficiently Reduced Human Mobility in Japan during the COVID-19 Epidemic , 2020, 2005.09423.

[5]  Siddharth Gupta,et al.  The TimeGeo modeling framework for urban mobility without travel surveys , 2016, Proceedings of the National Academy of Sciences.

[6]  Satish V. Ukkusuri,et al.  Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data , 2021, ArXiv.

[7]  A. Vespignani,et al.  Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile , 2020, Nature Communications.

[8]  Martin Raubal,et al.  Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data , 2012, GIScience.

[9]  A. Sheikh,et al.  The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK , 2020, The Lancet Digital Health.

[10]  Marta C. González,et al.  The path most traveled: Travel demand estimation using big data resources , 2015, Transportation Research Part C: Emerging Technologies.

[11]  Munmun De Choudhury,et al.  Big data and the well-being of women and girls: applications on the social scientific frontier , 2017 .

[12]  Chaoming Song,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[13]  Marco De Nadai,et al.  Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle , 2020, Science Advances.

[14]  R. Keil,et al.  “Global cities and the spread of infectious disease: the case of Severe Acute Respiratory Syndrome (SARS) in Toronto, Canada” : from Urban Studies (2006) , 2017 .

[15]  V. Colizza,et al.  Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies , 2020, BMC Medicine.

[16]  Emanuele Massaro,et al.  Uncovering socioeconomic gaps in mobility reduction during the COVID-19 pandemic using location data , 2020, ArXiv.

[17]  Jure Leskovec,et al.  Mobility network models of COVID-19 explain inequities and inform reopening , 2020, Nature.

[18]  Oliva G. Cantu-Ros,et al.  Influence of sociodemographic characteristics on human mobility , 2018 .

[19]  Marta C. González,et al.  Mining urban lifestyles: urban computing, human behavior and recommender systems , 2019, ArXiv.

[20]  Xiubin Bruce Wang,et al.  COVID-19 and social distancing: Disparities in mobility adaptation between income groups , 2020, Transportation Research Interdisciplinary Perspectives.

[21]  Marta C. González,et al.  Sequences of purchases in credit card data reveal lifestyles in urban populations , 2017, Nature Communications.

[22]  I. Bogoch,et al.  Impact of a Public Policy Restricting Staff Mobility Between Nursing Homes in Ontario, Canada During the COVID-19 Pandemic. , 2021, Journal of the American Medical Directors Association.

[23]  Matteo Cinelli,et al.  Human mobility in response to COVID-19 in France, Italy and UK , 2020, Scientific Reports.

[24]  Carl-Johan Neiderud,et al.  How urbanization affects the epidemiology of emerging infectious diseases , 2015, Infection ecology & epidemiology.

[25]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[26]  Royce J. Wilson,et al.  Impacts of State-Level Policies on Social Distancing in the United States Using Aggregated Mobility Data during the COVID-19 Pandemic , 2020 .

[27]  S. Cutter,et al.  Urban-rural differences in COVID-19 exposures and outcomes in the South: A preliminary analysis of South Carolina , 2021, PloS one.

[28]  Lada A. Adamic,et al.  Computational Social Science , 2009, Science.

[29]  Henry Kautz,et al.  Uncovering the socioeconomic facets of human mobility , 2020, Scientific Reports.

[30]  David L. Smith,et al.  Quantifying the Impact of Human Mobility on Malaria , 2012, Science.

[31]  Alexey Siretskiy,et al.  Effects of the COVID-19 Pandemic on Population Mobility under Mild Policies: Causal Evidence from Sweden , 2020, 2004.09087.

[32]  Boniphace Kutela,et al.  Exploring geographical distribution of transportation research themes related to COVID-19 using text network approach , 2021, Sustainable Cities and Society.

[33]  Song Gao,et al.  Discovering Spatial Interaction Communities from Mobile Phone Data , 2013 .

[34]  Tobias Preis,et al.  Quantifying crowd size with mobile phone and Twitter data , 2015, Royal Society Open Science.

[35]  Christian Schneider,et al.  Spatiotemporal Patterns of Urban Human Mobility , 2012, Journal of Statistical Physics.

[36]  C. Buckee,et al.  Lockdowns result in changes in human mobility which may impact the epidemiologic dynamics of SARS-CoV-2 , 2021, Scientific Reports.

[37]  Zbigniew Smoreda,et al.  On the Use of Human Mobility Proxies for Modeling Epidemics , 2013, PLoS Comput. Biol..

[38]  Federico Botta,et al.  Rapid indicators of deprivation using grocery shopping data , 2021, Royal Society Open Science.

[39]  Carlo Ratti,et al.  Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data , 2013, PloS one.

[40]  Dietmar Bauer,et al.  Inferring land use from mobile phone activity , 2012, UrbComp '12.

[41]  Michael R. Springborn,et al.  Social distancing responses to COVID-19 emergency declarations strongly differentiated by income , 2020, Proceedings of the National Academy of Sciences.

[42]  S. Forbes,et al.  Covid-19, Flexible Working, and Implications for Gender Equality in the United Kingdom , 2021 .

[43]  Pengfei Wang,et al.  Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes , 2017, KDD.

[44]  Kentaro Toyama,et al.  Project Lachesis: Parsing and Modeling Location Histories , 2004, GIScience.

[45]  Dino Pedreschi,et al.  Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown , 2020, ArXiv.

[46]  Alex Pentland,et al.  The data-driven society. , 2013, Scientific American.

[47]  Yanyan Xu,et al.  Understanding vehicular routing behavior with location-based service data , 2021, EPJ Data Science.

[48]  Marta C. González,et al.  Big Data Fusion to Estimate Urban Fuel Consumption: A Case Study of Riyadh , 2017, ArXiv.

[49]  Lei Zhang,et al.  Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections , 2020, Proceedings of the National Academy of Sciences.

[50]  Zbigniew Smoreda,et al.  Using big data to study the link between human mobility and socio-economic development , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[51]  S. Lehmann,et al.  The scales of human mobility , 2020, Nature.

[52]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[53]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[54]  G. Madey,et al.  Uncovering individual and collective human dynamics from mobile phone records , 2007, 0710.2939.

[55]  Dirk Brockmann,et al.  COVID-19 lockdown induces disease-mitigating structural changes in mobility networks , 2020, Proceedings of the National Academy of Sciences.

[56]  J. Demongeot,et al.  Novel statistical approach for assessing the persistence of the circadian rhythms of social activity from telephone call detail records in older adults , 2020, Scientific Reports.

[57]  Song Gao,et al.  Spatio-Temporal Analytics for Exploring Human Mobility Patterns and Urban Dynamics in the Mobile Age , 2015, Spatial Cogn. Comput..

[58]  P. Bajardi,et al.  COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown , 2020, Scientific Data.

[59]  Tim Pateman,et al.  Rural and urban areas: comparing lives using rural/urban classifications , 2011 .

[60]  J. Patz,et al.  Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race , 2021, Proceedings of the National Academy of Sciences.

[61]  J. Gaudart,et al.  Using Mobile Phone Data to Predict the Spatial Spread of Cholera , 2015, Scientific Reports.

[62]  Xin Lu,et al.  Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data , 2018, International journal of epidemiology.

[63]  V. Colizza,et al.  Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study , 2020, The Lancet Digital Health.

[64]  Dino Pedreschi,et al.  Returners and explorers dichotomy in human mobility , 2015, Nature Communications.

[65]  Y. de Montjoye,et al.  Unique in the shopping mall: On the reidentifiability of credit card metadata , 2015, Science.

[66]  Rodrigo Francisquini,et al.  Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: A case study in the capitals of Brazil , 2021, Expert Systems with Applications.

[67]  Joseph Ferreira,et al.  Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore , 2017, IEEE Transactions on Big Data.

[68]  Jessica T Davis,et al.  The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak , 2020, Science.

[69]  Zbigniew Smoreda,et al.  Unravelling daily human mobility motifs , 2013, Journal of The Royal Society Interface.

[70]  Qingquan Li,et al.  Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: a modelling study using mobile phone data , 2020, The Lancet Digital Health.

[71]  T Chandola,et al.  The new UK National Statistics Socio-Economic Classification (NS-SEC); investigating social class differences in self-reported health status. , 2000, Journal of public health medicine.

[72]  A. L. Schmidt,et al.  Economic and social consequences of human mobility restrictions under COVID-19 , 2020, Proceedings of the National Academy of Sciences.

[73]  The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population , 2020 .

[74]  Ayyoob Sharifi,et al.  Are high-density districts more vulnerable to the COVID-19 pandemic? , 2021, Sustainable Cities and Society.

[75]  J. Bohannon Tracking People's Electronic Footprints , 2006, Science.

[76]  João Gama,et al.  Discovering locations and habits from human mobility data , 2020, Annals of Telecommunications.