Symposium on Human Dynamics Research: Mining Human Dynamics with Big Data & Spatio-Temporal Analysis

Type: Virtual Paper
Theme:
Sponsor Groups: Geographic Information Science and Systems Specialty Group, Spatial Analysis and Modeling Specialty Group, Cyberinfrastructure Specialty Group
Poster #:
Day: 4/10/2021
Start / End Time: 9:35 AM / 10:50 AM (PDT)
Room: Virtual 9
Organizers: Xi Gong, Xining Yang
Chairs: Xining Yang

Call for Submissions

With the advancement of information and communication technologies (ICT), location-aware technology, and mobile technology, data about human behaviors and interactions in physical, virtual, and network space has been generated at an unprecedented scale. The so-called big data bring in both opportunities and challenges for understanding, modeling, and predicting human dynamics. On one hand, the big data are collected from ubiquitous data sources (social media data, sensor data, GPS tracks, transaction records, etc.); the data can cover aspects and scales of the human dynamics that are unseen from traditional data. On the other hand, revealing meaningful spatio-temporal patterns are challenging due to the high volume, velocity, and variety nature of the big data. Recent cutting-edge techniques such as data mining, machine learning, and artificial intelligence (AI) open up new opportunities for unveiling the hidden spatio-temporal and network patterns of human dynamics in the big data.

This session welcomes both methodological research on mining human dynamics with big data and human dynamics applications utilizing big data and spatio-temporal analysis. Potential session topics include, but not limited to:
• Mining, detecting, or modeling human dynamics in physical, virtual or network space using big data (such as social media data, sensor data, GPS tracks, and transaction data).
• Algorithm design/optimization for mining human dynamics in big data.
• Spatio-temporal analytics of human dynamics using big data.
• Comparison of big data and traditional data in human dynamics research.
• Innovative data sources and data collection methods for human dynamics research.
• Data quality and privacy issues of big data in human dynamics research.
• Visualization of human dynamic patterns in big data.
• Predicting human dynamics based on historical pattern analysis on big data.

Specialty Group Sponsors: Spatial Analysis and Modeling (SAM), and Geographic Information Science and Systems, Cyberinfrastructure Specialty Group.

If you are interested in this session, please send your abstract and the Personal Identification Number (PIN) for AAG 2021 to Xi Gong (University of New Mexico; xigong@unm.edu) or Xining Yang (Eastern Michigan University; xyang5@emich.edu) before Dec 18th, 2020.


Description

With the advancement of information and communication technologies (ICT), location-aware technology, and mobile technology, data about human behaviors and interactions in physical, virtual, and network space has been generated at an unprecedented scale. The so called big data bring in both opportunities and challenges for understanding, modeling, and predicting human dynamics. On one hand, the big data are collected from ubiquitous data sources (social media data, sensor data, GPS tracks, transaction records, etc.); the data can cover aspects and scales of the human dynamics that are unseen from traditional data. On the other hand, revealing meaningful spatio-temporal patterns are challenging due to the high volume, velocity, and variety nature of the big data. Recent cutting-edge techniques such as data mining, machine learning, and artificial intelligence (AI) open up new opportunities for unveiling the hidden spatio-temporal and network patterns of human dynamics in the big data.


Agenda

Type Details Minutes Start Time
Presenter Nicole D. Payntar*, University of Texas at Austin, Wei-Lin Hsiao, University of Texas at Austin, R. Alan Covey, University of Texas at Austin, Kristen Grauman, University of Texas at Austin, Big Data and Heritage Tourism: Detecting Tourist Movement and Visual Experience at Archaeological Heritage Sites in Cuzco, Peru 15 9:35 AM
Presenter Xi Gong*, University of New Mexico, K. Maria D. Lane, University of New Mexico, How are Geography Departments Tweeting? – A Case Study of Institutional Twitter Usage by U.S. Geography Departments 15 9:50 AM
Presenter Hyowon Ban*, California State University, Long Beach, Geographical Counterpoint to Choreographic Information based on Approaches in GIScience and Visualization 15 10:05 AM
Presenter Xining Yang*, Eastern Michigan University, Yu Feng, University of Michigan, Hua Cai, Purdue University, Predicting subway passenger flow and demand using big data and machine learning methods. 15 10:20 AM
Presenter Toshinori Ariga*, National Institute for Environmental Studies Japan, Shih-Lung Shaw, The University of Tennessee, Dynamic population distribution patterns: A comparison of three datasets for Tokyo, Japan 15 10:35 AM

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