Recording Plan: 

Curated Track: 

Zhenlong Li, Pennsylvania State University

Call For Participation
This session aims to capture recent advancement in developing new modeling techniques for handling geospatial big data related to human mobility, the role of human mobility research in public health and environmental studies, challenges and existing biases in human mobility data sources, use of high-performance computing technologies in human mobility research, and how different sector utilize human mobility data to understand and adapt to change in human behavior.

Description:

Aims and Scope:

Over the past few decades, human movement and migration have surged, presenting significant challenges for societies worldwide. Analyzing patterns of human mobility is crucial for urban planning, controlling infectious diseases, traffic forecasting, mitigating climate change impacts, improving public health, managing disasters, and studying human behavior. The Covid-19 pandemic has highlighted the urgency of understanding human movement to predict and mitigate the spread of infectious diseases and to develop strategies for preventing further transmission. With the vast availability of human mobility data, researchers and communities face both opportunities and challenges in analyzing this information, constructing comprehensive models, extracting meaningful insights, and visualizing this data effectively. Social media platforms, like Twitter, Facebook, and Instagram, as well as other smart devices, offer a wealth of geolocation data about people's daily movements. Properly harnessed, these data can illuminate movement patterns within networks, their societal impact, and assist stakeholders from various sectors in making informed decisions to cope with a fast-evolving world. Nonetheless, interpreting human mobility data to yield useful information is a complex task. The analysis, burdened by the spatial and temporal dimensions of geolocation data, can be computationally demanding. Moreover, safeguarding individual privacy is critical when analyzing human movement at the individual level.

This session aims to capture recent advancement in developing new modeling techniques for handling geospatial big data related to human mobility, the role of human mobility research in public health and environmental studies, challenges and existing biases in human mobility data sources, use of high-performance computing technologies in human mobility research, and how different sector utilize human mobility data to understand and adapt to change in human behavior.

Potential topics for this session include (but not limited to) the following:
1- Innovative methods for analyzing large-scale human mobility datasets
2- Integrating urban planning with dynamic human movement patterns
3- Advanced computational models for predicting the spread of infectious diseases using mobility data
4- Strategies for climate change adaption informed by mobility data
5- Human mobility consideration in effective disaster management
6- The impact of human mobility on traffic systems and forecasting methods
7- Behavioral analysis through the lens of geolocation data from social media
8- Public health strategies derived from the study of human movement patterns
9- Addressing computational challenges in the visualization of mobility patterns
10- Cross-sectoral applications of human mobility research for societal benefit
11- Tools and techniques for ensuring privacy in the study of human mobility

To present a paper in this session, please submit your abstract via the online submission portal by October 31, at (https://www.aag.org/events/aag2025/). Following your submission, kindly email the abstract code/PIN, the title of your paper, and the abstract itself to one of the session organizers no later than December 2, 2024.

Organizers:
1- M. Naser Lessani, Penn State University, US. mzl6134@psu.edu
2- Zhenlong Li, Penn State University, US. zhenlong@psu.edu
3- Huan Ning, Pennsylvania State University, US. hmn5304@psu.edu
4- Temitope Ezekiel Akinboyewa, Pennsylvania State University, US. tea5209@psu.edu
5- Manzhu Yu, Pennsylvania State University, US. mqy5198@psu.edu

Non-Presenting Participants