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human mobility data in the COVID-19 pandemic:characteristics,applications,and challenges

International Journal of Digital Earth 2021年第9期14卷 1126-1147页

作者： Tao Hu Siqin Wang Bing She Mengxi Zhang Xiao Huang Yunhe Cui Jacob Khuri Yaxin Hu Xiaokang Fu Xiaoyue Wang Peixiao Wang Xinyan Zhu Shuming Bao Wendy Guan Zhenlong Li Department of Geography Oklahoma State UniversityStillwaterOKUSA Center for Geographic Analysis Harvard UniversityCambridgeMAUSA School of Earth and Environmental Sciences The University of QueenslandSt LuciaAustralia Institute for Social Research University of MichiganAnn ArborMIUSA School of Health Ball State UniversityMuncieINUSA Department of Geosciences University of ArkansasFayettevilleARUSA Department of Geography University of ConnecticutStorrsCTUSA Lake Erie College of Osteopathic Medicine Seton Hill UniversityGreensburgPAUSA School of Resource and Environmental Sciences Wuhan UniversityWuhanPeople’s Republic of China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China China Data Institute Ann ArborMIUSA Geoinformation and Big Data Research Laboratory Department of GeographyUniversity of South CarolinaColumbiaSCUSA

The COVID-19 pandemic poses unprecedented challenges around the world.Many studies have applied mobility data to explore spatiotemporal trends over time,investigate associations with other variables,and predict or simulate the spread of COVID-19.Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks.We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective.We identified three major sources of mobility data:public transit systems,mobile operators,and mobile phone applications.Four approaches have been commonly used to estimate human mobility:public transit-based flow,social activity patterns,index-based mobility data,and social media-derived mobility data.We compared mobility datasets’characteristics by assessing data privacy,quality,space–time coverage,high-performance data storage and processing,and accessibility.We also present challenges and future directions of using mobility data.This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.

关键词： COVID-19 public health human mobility open data mobile phone mobility index

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Resilience patterns of human mobility in response to extreme urban floods

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National Science Review 2023年第8期10卷 121-133页

作者： Junqing Tang Pengjun Zhao Zhaoya Gong Hongbo Zhao Fengjue Huang Jiaying Li Zhihe Chen Ling Yu Jun Chen School of Urban Planning and Design Shenzhen Graduate School Peking University 2. Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China Shenzhen Graduate SchoolPeking University School of Urban and Environmental Sciences Peking University Key Research Institute of Yellow River Civilization and Sustainable Development&Collaborative Innovation Center on Yellow River Civilization Henan Province and Ministry of Education Henan University 5. Key National Geomatics Center of China

Large-scale disasters can disproportionately impact different population groups, causing prominent disparity and inequality, especially for the vulnerable and marginalized. Here, we investigate the resilience of human mobility under the disturbance of the unprecedented ‘720’ Zhengzhou flood in China in 2021 using records of 1.32 billion mobile phone signaling generated by 4.35 million people. We find that although pluvial floods can trigger mobility reductions, the overall structural dynamics of mobility networks remain relatively stable. We also find that the low levels of mobility resilience in female, adolescent and older adult groups are mainly due to their insufficient capabilities to maintain business-as-usual travel frequency during the flood. Most importantly, we reveal three types of counter-intuitive, yet widely existing,resilience patterns of human mobility(namely, ‘reverse bathtub’, ‘ever-increasing’ and ‘ever-decreasing’ patterns), and demonstrate a universal mechanism of disaster-avoidance response by further corroborating that those abnormal resilience patterns are not associated with people’s gender or age. In view of the common association between travel behaviors and travelers’ socio-demographic characteristics, our findings provide a caveat for scholars when disclosing disparities in human travel behaviors during flood-induced emergencies.

关键词： disaster resilience urban flood human mobility Zhengzhou flood mobile phone data

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Incorporation of intra-city human mobility into urban growth simulation:A case study in Beijing

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Journal of Geographical Sciences 2022年第5期32卷 892-912页

作者： WANG Siying FEI Teng LI Weifeng ZHANG Anqi GUO Huagui DU Yunyan School of Resource and Environmental Science Wuhan UniversityWuhan430079China Department of Urban Planning and Design The University of Hong KongHong KongChina Guangdong-Hong Kong-Macao Joint Laboratory for Smart Cities Hong KongChina Institute of Geographic Sciences and Natural Resources Research CASBeijing100101China School of Architecture and Urban-rural Planning Fuzhou UniversityFuzhou350108China

The effective modeling of urban growth is crucial for urban planning and analyzing the causes of land-use dynamics.As urbanization has slowed down in most megacities,improved urban growth modeling with minor changes has become a crucial open issue for these cities.Most existing models are based on stationary factors and spatial proximity,which are unlikely to depict spatial connectivity between regions.This research attempts to leverage the power of real-world human mobility and consider intra-city spatial interaction as an imperative driver in the context of urban growth simulation.Specifically,the gravity model,which considers both the scale and distance effects of geographical locations within cities,is employed to characterize the connection between land areas using individual trajectory data from a macro perspective.It then becomes possible to integrate human mobility factors into a neural-network-based cellular automata(ANN-CA)for urban growth modeling in Beijing from 2013 to 2016.The results indicate that the proposed model outperforms traditional models in terms of the overall accuracy with a 0.60%improvement in Cohen’s Kappa coefficient and a 0.41%improvement in the figure of merit.In addition,the improvements are even more significant in districts with strong relationships with the central area of Beijing.For example,we find that the Kappa coefficients in three districts(Chaoyang,Daxing,and Shunyi)are considerably higher by more than 2.00%,suggesting the possible existence of a positive link between intense human interaction and urban growth.This paper provides valuable insights into how fine-grained human mobility data can be integrated into urban growth simulation,helping us to better understand the human-land relationship.

关键词： cellular automata urban growth simulation human mobility massive trajectories

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Untangling the contributions of meteorological conditions and human mobility to tropospheric NO2 in Chinese mainland during the COVID-19 pandemic in early 2020

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National Science Review 2021年第11期8卷 121-132页

作者： Yuxiang Zhang Haixu Bo Zhe Jiang Yu Wang Yunfei Fu Bingwei Cao Xuewen Wang Jiaqi Chen Rui Li School of Earth and Space Sciences University of Science and Technology of China Comparative Planetary Excellence Innovation Center Frontiers Science Center for Planetary Exploration and Emerging Technologies Chinese Academy of Sciences State Key Laboratory of Fire Science University of Science and Technology of China Jiangxi Ecological Environment Monitoring Center Green Earth Science and Education Service

In early 2020,unprecedented lockdowns and travel bans were implemented in Chinese mainland to fight COVID-19,which led to a large reduction in anthropogenic emissions.This provided a unique opportunity to isolate the effects from emission and meteorology on tropospheric nitrogen dioxide(NO2).Comparing the atmospheric NO2in 2020 with that in 2017,we found the changes of emission have led to a49.3±23.5% reduction,which was～12% more than satellite-observed reduction of 37.8±16.3%.The discrepancy was mainly a result of changes of meteorology,which have contributed to an 8.1±14.2%increase of NO2.We also revealed that the emission-induced reduction of NO2has significantly negative correlations to human mobility,particularly that inside the city.The intra-city migration index derived from Baidu Location-Based-Service can explain 40.4%±17.7% variance of the emission-induced reduction of NO2in 29 megacities,each of which has a population of over 8 million in Chinese mainland.

关键词： atmospheric nitrogen dioxide anthropogenic emissions meteorology conditions human mobility COVID-19 quarantine

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Clustering Indoor Location Data for Social Distancing and human mobility to Combat COVID-19

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Computers, Materials & Continua 2022年第4期71卷 907-924页

作者： Yuan Ai Ho Chee Keong Tan Yin Hoe Ng School of Information Technology Monash University MalaysiaJalan Lagoon SelatanBandar Sunway47500Selangor Darul EhsanMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100Selangor Darul EhsanMalaysia

The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease(i.e.,COVID-19).As a countermeasure,contact tracing and social distancing are essential to prevent the transmission of the virus,which can be achieved using indoor location analytics.Based on the indoor location analytics,the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of COVID-19.Given the indoor location data,the clustering can be applied to cluster spatial data,spatio-temporal data and movement behavior features for proximity detection or contact tracing applications.More specifically,we propose the Coherent Moving Cluster(CMC)algorithm for contact tracing,the density-based clustering(DBScan)algorithm for identification of hotspots and the trajectory clustering(TRACLUS)algorithm for clustering indoor trajectories.The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users.The network of users is used to model an optimization problem to manage the human mobility on a site.The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools.The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30%in terms of accuracy of clustering trajectories.By adopting this system for human mobility management,the count of close contacts among the users within a confined area can be reduced by 80%in the scenario where all users are allowed to access the site.

关键词： Indoor location analytics COVID-19 contact tracing social distancing spatial-temporal dimensions human mobility

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Measuring spatio-temporal autocorrelation in time series data of collective human mobility

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Geo-Spatial Information Science 2019年第3期22卷 166-173页

作者： Yong Gao Jing Cheng Haohan Meng Yu Liu Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijingChina

Massive spatio-temporal big data about human mobility have become increasingly available.Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment.Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time.The most widely used spatial autocorrelation measurements,such as Moran’s I and local indicators of spatial association(LISA),only apply to static data,so are powerless to spatio-temporal big data about human mobility.Thus,we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data.Then the method was applied to taxi ride data in Beijing,China to reveal the spatial pattern of collective human mobility.The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road,weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road,and almost no spatiotemporal autocorrelation between the roads.Local spatial patterns of taxi travel were also recognized.This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility.

关键词： Taxi rides human mobility spatio-temporal autocorrelation Moran index

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Predicting human mobility via Long Short-Term Patterns

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Computer Modeling in Engineering & Sciences 2020年第9期124卷 847-864页

作者： Jianwei Chen Jianbo Li Ying Li College of Computer Science and Technology Qingdao UniversityQingdao266071China

Predicting human mobility has great significance in Location based Social Network applications,while it is challenging due to the impact of historical mobility patterns and current trajectories.Among these challenges,historical patterns tend to be crucial in the prediction task.However,it is difficult to capture complex patterns from long historical trajectories.Motivated by recent success of Convolutional Neural Network(CNN)-based methods,we propose a Union ConvGRU(UCG)Net,which can capture long short-term patterns of historical trajectories and sequential impact of current trajectories.Specifically,we first incorporate historical trajectories into hidden states by a shared-weight layer,and then utilize a 1D CNN to capture short-term pattern of hidden states.Next,an average pooling method is involved to generate separated hidden states of historical trajectories,on which we use a Fully Connected(FC)layer to capture longterm pattern subsequently.Finally,we use a Recurrent Neural Net-work(RNN)to predict future trajectories by integrating current trajectories and long short-term patterns.Experiments demonstrate that UCG Net performs best in comparison with neural network-based methods.

关键词： human mobility prediction CNN average pooling

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A general law of human mobility

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Science China(Information Sciences) 2015年第10期58卷 24-37页

作者： LIANG Xiao ZHAO JiChang XU Ke Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences School of Economics and Management Beihang University State Key Laboratory of Software Development Environment Beihang University

The intrinsic factors that drive human mobility have remained unclear for decades. Our observations from both intra-urban and inter-urban trips demonstrate a general law of human mobility. Specifically, the probability that a trip will occur is inversely proportional to the size of population located inside a circle with radius equal to the travel distance centered at the trip origin. A simple parameterless rank-based model is presented; this model can predict human flows with a convincing fidelity. Moreover, existing models can be implemented as special cases of our model, suggesting that our model is stable at more spatial scales. Our model also creates a fundamental bridge between individual mobility and social relationships.

关键词： human mobility social mobility general law rank-based model flux prediction

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Impact of human mobility on the epidemic spread during holidays

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Infectious Disease Modelling 2023年第4期8卷 1108-1116页

作者： Han Li Jianping Huang Xinbo Lian Yingjie Zhao Wei Yan Li Zhang Licheng Li Collaborative Innovation Center for Western Ecological Safety College of Atmospheric SciencesLanzhou UniversityLanzhou730000China College of Atmospheric Sciences Lanzhou UniversityLanzhou730000China School of Mathematics and Statistics Lanzhou UniversityLanzhou730000China

COVID-19 has posed formidable challenges as a significant global health crisis.Its complexity stems from factors like viral contagiousness,population density,social behaviors,governmental regulations,and environmental conditions,with interpersonal interactions and large-scale activities being particularly pivotal.To unravel these complexities,we used a modified SEIR epidemiological model to simulate various outbreak scenarios during the holiday season,incorporating both inter-regional and intra-regional human mobility effects into the parameterization scheme.In addition,evaluation metrics were used to evaluate the accuracy of the model simulation by comparing the congruence between simulated results and recorded confirmed cases.The findings suggested that intra-city mobility led to an average surge of 57.35%in confirmed cases of China,while inter-city mobility contributed to an average increase of 15.18%.In the simulation for Tianjin,China,a one-week delay in human mobility attenuated the peak number of cases by 34.47%and postponed the peak time by 6 days.The simulation for the United States revealed that human mobility played a more pronounced part in the outbreak,with a notable disparity in peak cases when mobility was considered.This study highlights that while inter-regional mobility acted as a trigger for the epidemic spread,the diffusion effect of intra-regional mobility was primarily responsible for the outbreak.We have a better understanding on how human mobility and infectious disease epidemics interact,and provide empirical evidence that could contribute to disease prevention and control measures.

关键词： COVID-19 Modified SEIR model human mobility Parameterization scheme

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Affinity-based human mobility pattern for improved region function discovering

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The Journal of China Universities of Posts and Telecommunications 2016年第1期23卷 60-67页

作者： Xu Yajing Li Gongfu Xue Chao Luo Angen Song Yizhe School of Telecommunication Engineering Beijing University of Posts and Telecommunications School of Electronics Engineering and Computer Science Queen Mary University of London

The process of urbanization is formed by regular movements of human beings. It yields different functional zones in a city, such as residential zone and commercial zone. Consequently, there exists a close connection between the human mobility pattern and the city＇s zones. However, it is not easy to collect large-scale society-wide data that can precisely capture the underlying relations between the individual＇s movement and the regional functions. Hence, our knowledge for understanding the basic patterns of human mobility is still limited. In order to discover the functions of different regions in a city, we propose an affinity based method in this paper. The affinity is a recently introduced metric for measuring the correlation of two connecting node in a complex network. The proposed model groups different functional zones by measuring user＇s arrival/departure distribution via relative entropy. In addition to this, we also identify the intensity of each functional zone by taking kernel density estimation （KDE） method. In the end, some experiments are conducted to evaluate our method with a large-scale real-life dataset, which consists of 3 million cellphone users＇ records from a period of one month. Our findings on the interaction between the mobility pattern and the regional functions can capture the city dynamics efficiently and provide a valuable reference for urban planners.

关键词： human mobility functional regions affinity measure

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