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 sim...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.展开更多
Performing mobile k nearest neighbor （MkNN） queries whilst also being mobile is a challenging problem. All the mobile objects issuing queries and/or being queried are mobile. The performance of this kind of query re...Performing mobile k nearest neighbor （MkNN） queries whilst also being mobile is a challenging problem. All the mobile objects issuing queries and/or being queried are mobile. The performance of this kind of query relies heav- ily on the maintenance of the current locations of the objects. The index used for mobile objects must support efficient up- date operations and efficient query handling. This study aims to improve the performance of the MkNN queries while re- ducing update costs. Our approach is based on an observa- tion that the frequency of one region changing between being occupied or not by mobile objects is much lower than the frequency of the position changes reported by the mobile ob- jects. We first propose an virtual grid quadtree with Voronoi diagram （VGQ-Vor）, which is a two-layer index structure that indexes regions occupied by mobile objects in a quadtree and builds a Voronoi diagram of the regions. Then we propose a moving k nearest neighbor （kNN） query algorithm on the VGQ-Vor and prove the correctness of the algorithm. The ex- perimental results show that the VGQ-Vor outperforms the existing techniques （Bx-tree, Bdual-tree） by one to three or- ders of magnitude in most cases.展开更多
基金supported by the NSF[National Science Foundation]under grant 1841403,2027540,and 2028791.
文摘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.
文摘Performing mobile k nearest neighbor （MkNN） queries whilst also being mobile is a challenging problem. All the mobile objects issuing queries and/or being queried are mobile. The performance of this kind of query relies heav- ily on the maintenance of the current locations of the objects. The index used for mobile objects must support efficient up- date operations and efficient query handling. This study aims to improve the performance of the MkNN queries while re- ducing update costs. Our approach is based on an observa- tion that the frequency of one region changing between being occupied or not by mobile objects is much lower than the frequency of the position changes reported by the mobile ob- jects. We first propose an virtual grid quadtree with Voronoi diagram （VGQ-Vor）, which is a two-layer index structure that indexes regions occupied by mobile objects in a quadtree and builds a Voronoi diagram of the regions. Then we propose a moving k nearest neighbor （kNN） query algorithm on the VGQ-Vor and prove the correctness of the algorithm. The ex- perimental results show that the VGQ-Vor outperforms the existing techniques （Bx-tree, Bdual-tree） by one to three or- ders of magnitude in most cases.
