Crowdsourced social media and mobile phone signaling data for disaster impact assessment: A case study of the 8.8 Jiuzhaigou earthquake

Abstract

Rapid and accurate disaster impact assessment is crucial for disaster emergency decision-making. The perception and behavioral characteristics of people derived from crowdsourced data, such as social media and mobile phone signaling data, can provide information sources for disaster assessment and assist disaster emergency management. This topic has attracted extensive attention from scholars. However, these data types are incomplete and have wide-ranging quality, which makes it difficult to evaluate the disaster. Social media data contain many public descriptions of disasters, but the spatial distribution is uneven. Mobile phone signaling data cover a wide range and are spatiotemporal continuous, but semantic information is lacking. Thus, this paper develops a disaster impact assessment method that integrates social media data and mobile signaling data based on a classification of social media data with a character-level convolutional neural network (Char-CNN), anomaly detection in mobile signaling data, and feature mining of human activity. The result shows that social media and mobile signaling data complement each other well. The proposed method improves the assessment accuracy and provides detailed information on the spatial distribution of disasters. The results of this study provide reference for the combination of different sources of information for disaster response decisions.