We’re still accepting papers for the Uncertainties in Big Data Analytics in Disaster Research session. Please consider being part of our awesome session that we have been organizing for 4 years now.
In this session, we will bring together researchers, practitioners, and policy makers from different specialties, institutions, sectors, and continents to share ideas, findings, methodologies, and technologies that are needed to leverage geospatial big data in disaster research as well as address the uncertainties to increase usability and acceptability of these datasets. The session will also provide a platform to establish and strengthen personal connections, communication channels, and research collaborations.
Significant advancements have been made to collect and analyze big datasets for emergency response, risk communication, mobility studies among others. These datasets tend to suffer from a myriad of uncertainties in terms of positional accuracy, context ambiguity, credibility, reliability, representativeness and completeness. Moreover, there are also serious concerns about data provenance and privacy. While there is no shortage in big data applications, the quality issue of these data remains an intellectual and practical challenge. Although aggregation, permutation or masking techniques can be used to protect data privacy without compromising the overall quality of data, its effectiveness depends on the degree of distribution heterogeneity of the geographic phenomenon.
Possible topics may include but are not limited to:
- Quality issues in social media big data
- Challenges in collecting, processing and analyzing big data for real-time applications
- Big data quality and its impact in decision making
- Calibration and validation techniques/approaches in big data
- Data fusion of multi-source and/or heterogeneous datasets
- Big data analytics in hazards and built-environment
- Big data analytics in human movements and behaviors during disasters
- Geo-visualization techniques to analyze and visualize social media data
- Privacy and big data management
- Provenance and metadata generation
- Applications of machine-learning and computer vision in disaster research
- New methods to measure social media credibility of social media content and users
- Influential social media user detection
- Bandana Kar, AAAS Science, Technology and Policy Fellow at U.S. Dept. of Energy (email@example.com)
- Edwin Chow, Texas State University, firstname.lastname@example.org
- Zhenlong Li, University of South Carolina, email@example.com
- Qunying Huang, University of Wisconsin, firstname.lastname@example.org
If interested in participating in this session, please send the confirmation of a successful abstract submission to us by December 1st, 2022, and state whether your talk will be virtual or in-person.
Abstract submission portal: aag.secure-platform.com/aag2023