Visualizing the time-varying crowd mobility

Feiran Wu; Minfeng Zhu; Xin Zhao; Qi Wang; Wei Chen; Ross Maciejewski

doi:10.1145/2818517.2818540

Visualizing the time-varying crowd mobility

Feiran Wu, Minfeng Zhu, Xin Zhao, Qi Wang, Wei Chen, Ross Maciejewski

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

Modeling human mobility is a critical task in fields such as urban planning, ecology, and epidemiology. Given the current use of mobile phones, there is an abundance of data that can be used to create models of high reliability. Existing techniques can reveal the macro-patterns of crowd movement, or analyze the trajectory of an individual object; however, they focus on geographical characteristics. In this paper, we employ a novel data representation, the mobility transition graph, which is generated from a citywide human mobility dataset by defining the temporal trends of crowd mobility and the interleaved transitions between different mobility patterns. We describe the design, creation and manipulation of the mobility transition graph and demonstrate the efficiency of our approach by case study.

Original language	English (US)
Title of host publication	SIGGRAPH Asia 2015 Visualization in High Performance Computing, SA 2015
Publisher	Association for Computing Machinery, Inc
ISBN (Print)	9781450339292
DOIs	https://doi.org/10.1145/2818517.2818540
State	Published - Nov 2 2015
Event	SIGGRAPH Asia, SA 2015 - Kobe, Japan Duration: Nov 2 2015 → Nov 6 2015

Other

Other	SIGGRAPH Asia, SA 2015
Country	Japan
City	Kobe
Period	11/2/15 → 11/6/15

Keywords

Mobility
Spatio-temporal transition
Storyline
Timeline

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Computer Vision and Pattern Recognition
Human-Computer Interaction

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10.1145/2818517.2818540

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title = "Visualizing the time-varying crowd mobility",

abstract = "Modeling human mobility is a critical task in fields such as urban planning, ecology, and epidemiology. Given the current use of mobile phones, there is an abundance of data that can be used to create models of high reliability. Existing techniques can reveal the macro-patterns of crowd movement, or analyze the trajectory of an individual object; however, they focus on geographical characteristics. In this paper, we employ a novel data representation, the mobility transition graph, which is generated from a citywide human mobility dataset by defining the temporal trends of crowd mobility and the interleaved transitions between different mobility patterns. We describe the design, creation and manipulation of the mobility transition graph and demonstrate the efficiency of our approach by case study.",

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AU - Wu, Feiran

AU - Zhu, Minfeng

AU - Zhao, Xin

AU - Wang, Qi

AU - Chen, Wei

AU - Maciejewski, Ross

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N2 - Modeling human mobility is a critical task in fields such as urban planning, ecology, and epidemiology. Given the current use of mobile phones, there is an abundance of data that can be used to create models of high reliability. Existing techniques can reveal the macro-patterns of crowd movement, or analyze the trajectory of an individual object; however, they focus on geographical characteristics. In this paper, we employ a novel data representation, the mobility transition graph, which is generated from a citywide human mobility dataset by defining the temporal trends of crowd mobility and the interleaved transitions between different mobility patterns. We describe the design, creation and manipulation of the mobility transition graph and demonstrate the efficiency of our approach by case study.

AB - Modeling human mobility is a critical task in fields such as urban planning, ecology, and epidemiology. Given the current use of mobile phones, there is an abundance of data that can be used to create models of high reliability. Existing techniques can reveal the macro-patterns of crowd movement, or analyze the trajectory of an individual object; however, they focus on geographical characteristics. In this paper, we employ a novel data representation, the mobility transition graph, which is generated from a citywide human mobility dataset by defining the temporal trends of crowd mobility and the interleaved transitions between different mobility patterns. We describe the design, creation and manipulation of the mobility transition graph and demonstrate the efficiency of our approach by case study.

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KW - Spatio-temporal transition

KW - Storyline

KW - Timeline

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Visualizing the time-varying crowd mobility

Abstract

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Keywords

ASJC Scopus subject areas

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