Enhancing situational awareness by mining big social media data in near-real time for disaster manage: a CyberGIS approach

USC ASPIRE-I Track-1 Program (Office of the Vice President for Research)

PI: Zhenlong Li (Co-I: Amir Karami)

07/012017-09/30/2019

Summary: This project aims to design and prototype a CyberGIS system to enhance the situational awareness for better disaster management by streaming, processing, analyzing, and mapping billions of geo-tagged tweets in near-real time. Due to the massive amounts of tweets to be handled and the time-sensitive processing requirement, this project will integrate high performance computing, distributed spatial analysis, and collaborative mapping in a geo-cyberinfrastructure environment.  Three recent hurricanes (Hurricane Joaquin, 2015; Hurricane Hermine, 2016; Hurricane Matthew, 2016) are used as the study cases to evaluate our prototype system. With this system, we aim to provide near-real time assessment of 1) the spatial and temporal evolution of a hurricane by monitoring the volumes of hurricane-related tweets, and where and when these tweets are posted; 2) people’s movement patterns during a hurricane by tracking and analyzing the location of millions of Twitter users; and 3) the people’s needs and opinions by analyzing large volumes of Twitter messages with text mining, sentiment analysis, spatial analysis, and social network analysis.