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Twitter mobility dynamics during the COVID-19 pandemic: A case study of London
16 references·6 citing·46 second hop
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(2020)The Coronavirus crisis: What …Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter (2020)Public discourse and sentimen…Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic (2020)Multiscale dynamic human mobi…Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns (2017)Coupling mobile phone and soc…Who Tweets with Their Location? Understanding the Relationship between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter (2015)Who Tweets with Their Locatio…Understanding Human Mobility from Twitter (2015)Understanding Human Mobility …Residential built environment and walking activity: Empirical evidence of Jane Jacobs’ urban vitality (2015)Residential built environment…Geo-located Twitter as proxy for global mobility patterns (2014)Geo-located Twitter as proxy …Detecting the dynamics of urban structure through spatial network analysis (2014)Detecting the dynamics of urb…Redrawing the Map of Great Britain from a Network of Human Interactions (2010)Redrawing the Map of Great Br…Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0 (2007)Citizens as Voluntary Sensors…Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I (2001)Developing a bivariate spatia…Local Indicators of Spatial Association—LISA (1995)Local Indicators of Spatial A…Understanding Social Impacts of Major Disruptions from the Perspective of Destination Access (2025)Understanding Social Impacts …Georeferenced X (formerly twitter) data as a proxy of mobility behaviour: case study of Norway (2024)Georeferenced X (formerly twi…Differences in Changes of Mobility Patterns Across the Globe—Evidence From a Natural Experiment (2024)Differences in Changes of Mob…Changes in inter-city car travel behavior over the course of a year during the COVID-19 pandemic: A decision tree approach (2024)Changes in inter-city car tra…Analyzing the Impact of COVID-19 on Travel and Search Distances for Prominent Landmarks: Insights from Google Trends, X, and Tripadvisor (2024)Analyzing the Impact of COVID…Perspectives on Advanced Technologies in Spatial Data Collection and Analysis (2023)Perspectives on Advanced Tech…Power laws, Pareto distributions and Zipf's law (2005)Understanding individual human mobility patterns (2008)Check-in behaviour and 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References

  • 1995Local Indicators of Spatial Association—LISA13,061 cited
  • 2007Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0703 cited
  • 2014Geo-located Twitter as proxy for global mobility patterns675 cited
  • 2015Understanding Human Mobility from Twitter558 cited
  • 2014Detecting the dynamics of urban structure through spatial network analysis523 cited
  • 2010Redrawing the Map of Great Britain from a Network of Human Interactions424 cited
  • 2001Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I391 cited
  • 2020Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter332 cited
  • 2017Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns298 cited
  • 2021A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration296 cited
  • 2015Residential built environment and walking activity: Empirical evidence of Jane Jacobs’ urban vitality275 cited
  • 2015Who Tweets with Their Location? Understanding the Relationship between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter261 cited
  • 2020Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic259 cited

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2020
Twitter reveals human mobility dynamics during the COVID-19 pandemic
242 cited
  • 2020The Coronavirus crisis: What will the post-pandemic city look like?177 cited
  • 2021Population Bias in Geotagged Tweets164 cited
  • Cited by

    • 2024Changes in inter-city car travel behavior over the course of a year during the COVID-19 pandemic: A decision tree approach7 cited
    • 2023Perspectives on Advanced Technologies in Spatial Data Collection and Analysis4 cited
    • 2024Georeferenced X (formerly twitter) data as a proxy of mobility behaviour: case study of Norway4 cited
    • 2025Understanding Social Impacts of Major Disruptions from the Perspective of Destination Access3 cited
    • 2024Differences in Changes of Mobility Patterns Across the Globe—Evidence From a Natural Experiment3 cited
    • 2024Analyzing the Impact of COVID-19 on Travel and Search Distances for Prominent Landmarks: Insights from Google Trends, X, and Tripadvisor2 cited