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From Signals to Society: Data Science for Understanding the Earth and Human Dynamics
March 6 @ 11:00 am – 12:00 pm
Location: OMB 206
Hao Tian
Ph.D. Student in the Department of Geography
Hao Tian is a third-year Ph.D. student in the Department of Geography at Texas A&M University. His research focuses on urban human dynamics, human–environment interactions, and spatial modeling, with a particular interest in using multi-source sensing and GeoAI approaches to understand how human activities respond to environmental and climatic stressors.
Can Seismic Signals Decode Human Behavior? Toward a Data-Driven Understanding of Human Dynamics
Ambient seismic noise, traditionally regarded as merely “noise” in geophysics, can in fact encode rich information about human activity. By integrating seismic data with mobility and environmental datasets through data-driven approaches, this talk introduces how seismic sensing can reveal both large-scale human activity disruptions driven by extreme weather events and fine-grained, street-level traffic states and dynamics, highlighting its potential for advancing our understanding of human–environment interactions and urban resilience.
Yining Liu
Ph.D. Student in the Department of Urban and Regional Science
Yining Liu is a second-year PhD student in Urban and Regional Science. Her research focuses on the impacts of microclimatic conditions and built environment on human well-being.
Outdoor Thermal Comfort and Recreational Walking Thresholds Among Older Adults in Subsidized Housing: Insights from Strava
This study uses crowdsourced Strava walking activity data to examine the non-linear relationship between outdoor thermal comfort and older adults’ recreational walking across Harris County, Texas, focusing on the summer period from July to September 2023. Outdoor thermal exposure is quantified using the novel COMFA-OA heat stress model, which captures human–environment energy exchange and is specifically calibrated for older adults. Using non-linear response curve modeling, the analysis estimates month-specific thermal comfort thresholds and evaluates walking behavior across street segments surrounding subsidized housing and a matched set of apartment neighborhoods.