Human’s daily movements exhibit high regularity in a space-time context that typically forms circadian rhythms.Understanding the rhythms for human daily movements is of high interest to a variety of parties from urba...Human’s daily movements exhibit high regularity in a space-time context that typically forms circadian rhythms.Understanding the rhythms for human daily movements is of high interest to a variety of parties from urban planners,transportation analysts,to business strategists.In this paper,we present an interactive visual analytics design for understanding and utilizing data collected from tracking human’s movements.The resulting system identifies and visually presents frequent human movement rhythms to support interactive exploration and analysis of the data over space and time.Case studies using real-world human movement data,including massive urban public transportation data in Singapore and the MIT reality mining dataset,and interviews with transportation researches were conducted to demonstrate the effectiveness and usefulness of our system.展开更多
基金The research was conducted at the Future Cities Laboratory at the Singapore-ETH Centre,which was established collaboratively between ETH Zurich and Singapore’s National Research Foundation(FI 370074016)under its Campus for Research Excellence and Technological Enterprise programmeChi-Wing Fu is supported by the CUHK strategic recruitment fund and direct grant(4055061)Kwan-Liu Ma is supported in part by the U.S.National Science Foundation.
文摘Human’s daily movements exhibit high regularity in a space-time context that typically forms circadian rhythms.Understanding the rhythms for human daily movements is of high interest to a variety of parties from urban planners,transportation analysts,to business strategists.In this paper,we present an interactive visual analytics design for understanding and utilizing data collected from tracking human’s movements.The resulting system identifies and visually presents frequent human movement rhythms to support interactive exploration and analysis of the data over space and time.Case studies using real-world human movement data,including massive urban public transportation data in Singapore and the MIT reality mining dataset,and interviews with transportation researches were conducted to demonstrate the effectiveness and usefulness of our system.
