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A data visualization interactive exploration of human mobility data during the COVID-19 outbreak: a case study
12 references·2 citing·39 second hop
A data visualization interactive exploration of human mobility data during the COVID-19 outbreak: a case study (2020)A data visualization interactive explorat…COVID-19: Challenges to GIS with Big Data (2020)COVID-19: Challenges to GIS w…Human mobility: Models and applications (2018)Human mobility: Models and ap…Beanstalk (2017)BeanstalkActivity patterns mining in Wi-Fi access point logs (2017)Activity patterns mining in W…Urban human mobility data mining: An overview (2016)Urban human mobility data min…Crowdsourced mobility prediction based on spatio-temporal contexts (2016)Crowdsourced mobility predict…Understanding Human Mobility from Twitter (2015)Understanding Human Mobility …Tracking Human Mobility Using WiFi Signals (2015)Tracking Human Mobility Using…Large scale movement analysis from WiFi based location data (2012)Large scale movement analysis…Discovering regions of different functions in a city using human mobility and POIs (2012)Discovering regions of differ…Understanding individual human mobility patterns (2008)Understanding individual huma…The computer for the 21 st century (1999)The computer for the 21 st ce…Using passive Wi-Fi for community crowd sensing during the COVID-19 pandemic (2023)Using passive Wi-Fi for commu…Analysis of Mobility Changes Caused by COVID-19 in a Context of Moderate Restrictions Using Data Collected by Mobile Devices (2022)Analysis of Mobility Changes …Computational Social Science (2009)Limits of Predictability in Human Mobility (2010)Inferring Person-to-person Proximity Using WiFi Signals (2017)Digital footprints: Using WiFi probe and locational data to analyze human mobility trajectories in cities (2018)Individual mobility prediction using transit smart card data (2018)Power laws, Pareto distributions and Zipf's law (2005)Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China (2018)Power-law distributions in empirical data (2018)Urban mobility and neighborhood isolation in America’s 50 largest cities (2018)Social physics (2022)The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Friendship and mobility (2011)A universal model for mobility and migration patterns (2012)Cellular Census: Explorations in Urban Data Collection (2007)Enhancing shopping experiences in smart retailing (2021)The Laws of Migration (1885)Systematic Review of the Literature on Big Data in the Transportation Domain: Concepts and Applications (2019)scikit-mobility : A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data (2022)COVID-19 is linked to changes in the time–space dimension of human mobility (2023)Power-Law Distributions in Empirical Data (2009)The scales of human mobility (2020)The universal visitation law of human mobility (2021)A survey on deep learning for human mobility (2023)Mobility Detection Using Everyday GSM Traces (2006)Clustering daily patterns of human activities in the city (2012)Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data (2020)CountMeIn: Adaptive Crowd Estimation with Wi-Fi in Smart Cities (2022)Space–time tourist flow patterns in community-based tourism: an application of the empirical orthogonal function to Wi-Fi data (2022)Crowdsourcing geographic information for disaster response: a research frontier (2010)COVID-19 and urban public transport services: emerging challenges and research agenda (2020)Mapping county-level mobility pattern changes in the United States in response to COVID-19 (2020)Roles of Innovation Leadership on Using Big Data Analytics to Establish Resilient Healthcare Supply Chains to Combat the COVID-19 Pandemic: A Multimethodological Study (2021)TWITTERING THE NEWS (2010)Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning (2019)The Impossibility of Automating Ambiguity (2021)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)Methodologies for Cross-Domain Data Fusion: An Overview (2015)Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017)
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References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 1999The computer for the 21 st century1,234 cited
  • 2012Discovering regions of different functions in a city using human mobility and POIs1,136 cited
  • 2018Human mobility: Models and applications1,032 cited
  • 2015Understanding Human Mobility from Twitter558 cited
  • 2020COVID-19: Challenges to GIS with Big Data516 cited
  • 2015Tracking Human Mobility Using WiFi Signals141 cited
  • 2016Urban human mobility data mining: An overview132 cited
  • 2012Large scale movement analysis from WiFi based location data46 cited
  • 2017Beanstalk34 cited
  • 2017Activity patterns mining in Wi-Fi access point logs33 cited
  • 2016Crowdsourced mobility prediction based on spatio-temporal contexts12 cited

Cited by

  • 2022Analysis of Mobility Changes Caused by COVID-19 in a Context of Moderate Restrictions Using Data Collected by Mobile Devices8 cited
  • 2023Using passive Wi-Fi for community crowd sensing during the COVID-19 pandemic8 cited

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