my team and i are very excited to share that we have been afforded the opportunity to share our early and ongoing work at the intersection of physiological stress, local microclimate, wearables and Data Science, during the conference of the American Association of Geographers (AAG) which runs from the 25th of February till the 1st of March 2022.
our paper on Investigating the relationship between physiological stress and environmental factors through Data Science, the Internet of Things and DIY wearables has been invited to be part of a symposium on the 25th of February organised by Ast/P Guiming Zhang from the University of Denver and Ast/P Xi Gong from the University of New Mexico. My co-authors are Nguyen Duc Minh Anh and Nguyen Thien Minh Tuan.
the theme of the symposium is Human Dynamics Research: Mining Human Dynamics with Big Data, and it is sponsored by the Spatial Analysis and Modeling (SAM) specialty group, the Geographic Information Science and Systems specialty group, and the Cyberinfrastructure specialty group of the AAG, as part of the 8th Symposium on Human Dynamics Research.
Dr Gong is a faculty member of the Department of Geography & Environmental Studies, at the UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE). Dr Zhang is a faculty member of the Department of Geography and the Environment at his university. Together, they describe the symposium as follows:
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, volunteered geographic information - VGI, 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. 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, VGI, 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.