In August 2022, Dr.Zou successfully hosted a session"Big Earth Data for Disaster Risk Reduction", and delivered an oral presentation virtually at the 29th International Conference on Geoinformatics (CPGIS). Dr. Zou introduced how to integrate social media data with building footprints for rapid disaster damage assessment. This study aims to leverage two geospatial big data, social media and building footprints data, to estimate disaster damage and inform disaster risk reduction. Using 2017 Hurricane Harvey as an example, this research has two objectives: (1) to develop innovative models integrating social media data with building footprints for rapid disaster damage assessment; (2) to validate the developed model by comparing the estimated flooded households and the official Harvey damage assessment. The developed model can be applied to estimate disaster impacts in previous and future hazard events. The analysis results offer valuable insights into the advantages and limitations of leveraging traditional and novel data sources in disaster damage assessment. The comparative study also reveals the social injustice in disaster recovery and management.
Binbin Lin from GEAR lab also delivered an oral presentation in "GIScience for Public Health" session, and the topic is "How can Geospatial Big Data Help us Fight the Covid-19?". She introduced the applications of geospatial big data on Covid-19 in two stories. Firstly, she examined that global Twitter data could be utilized to uncover the linguistic and geographical disparities of Covid-19 awareness in the outbreak phase, and the changing awareness can predict the pandemic outbreak. Secondly, she revealed the evolving causality among NPIs, i.e., social distancing policies, public awareness of Covid-19, and human mobility, and their impacts on the Covid-19 spread by incorporating diverse geospatial data in the U.S. in 2020. The knowledge gained from this study could provide an insightful understanding of causality relationships between NPIs and Covid-19 health impacts. The developed framework can be used to simulate Covid-19 cases under various scenarios and inform the decision-making and policy-making for pandemic control.
We look forward to seeing more findings and outcomes from these researches!
