Rapid estimation of an earthquake impact area using a spatial logistic growth model based on social media data

Abstract

ABSTRACTRapid estimates of impact areas following large earthquakes constitute the cornerstone of emergency response scenarios. However, collecting information through traditional practices usually requires a large amount of manpower and material resources, slowing the response time. Social media has emerged as a source of real-time ‘citizen-sensor data’ for disasters and can thus contribute to the rapid acquisition of disaster information. This paper proposes an approach to quickly estimate the impact area following a large earthquake via social media. Specifically, a spatial logistic growth model (SLGM) is proposed to describe the spatial growth of citizen-sensor data influenced by the earthquake impact strength after an earthquake; a framework is then developed to estimate the earthquake impact area by combining social media data and other auxiliary data based on the SLGM. The reliability of our approach is demonstrated in two earthquake cases by comparing the detected areas with official intensity maps, and the time sensitivity of the social media data in the SLGM is discussed. The results illustrate that our approach can effectively estimate the earthquake impact area. We verify the external validity of our model across other earthquake events and provide further insights into extracting more valuable earthquake information using social media.