A digital social network for rapid collection of earthquake disaster information

Abstract

Abstract. Acquiring disaster information quickly after an earthquake is crucial for disaster and emergency rescue management. This study examines a digital social network – an earthquake disaster information reporting network – for rapid collection of earthquake disaster information. Based on the network, the disaster information rapid collection method is expounded in this paper. The structure and components of the reporting network are introduced. Then the work principles of the reporting network are discussed, in which the rapid collection of disaster information is realised by using Global System for Mobile Communications (GSM) messages to report the disaster information and Geographic information system (GIS) to analyse and extract useful disaster information. This study introduces some key technologies for the work principles, including the methods of mass sending and receiving of SMS for disaster management, the reporting network grouping management method, brief disaster information codes, and the GIS modelling of the reporting network. Finally, a city earthquake disaster information quick reporting system is developed and with the support of this system the reporting network obtained good results in a real earthquake and earthquake drills. This method is a semi-real time disaster information collection method which extends current SMS based method and meets the need of small and some moderate earthquakes.

A dasymetric data supported earthquake disaster loss quick assessment method for emergency response in China

“…In the future, we will explore the influence of secondary geological disasters on the estimation of human losses in mountainous areas. The automatic generation of earthquake response countermeasures using earthquake emergency response knowledge (Xu et al, 2014) from estimated earthquake disaster losses is another direction of study that will be pursued in the future.…”

The role of crowdsourcing and social media in crisis mapping: a case study of a wildfire reaching Croatian City of Split

“…Crowdsourcing engages communities around the world in emergency response and disaster management for natural hazards: Fires and wildfires (Becken and Hughey, 2013;Daly and Thom, 2016;De Longueville et al, 2009;Nayebi et al, 2017), earthquakes (Alexander, 2014;Han and Wang, 2019;Hewitt, 2014;Xu and Nyerges, 2017;Xu et al, 2013;Zook et al, 2010), and floods (Begg et al, 2015;Bird et al, 2012;Chan, 2015;Copernicus EMS, 2018;Eilander et al, 2016;Hossain, 2020;Merz et al, 2010;Schanze, 2006;Tingsanchali, 2012). A variety of theories and practical implementations have been developed, which differ in the following areas: technical background and data collection from social networks (Ryabchenko et al, 2016;Xu et al, 2015), classification of social media messages (Mitigation, Prevention, Response and Recovery) (Xiao et al, 2015), analytical models from various sources such as videos (To et al, 2015), geographic approach to social media analysis to indicate the usefulness of messages (de Albuquerque et al, 2015), real-time data mining tools (Zhong et al, 2016;Zhu et al, 2019) or predictions based on Twitter events belonging to geographic analysis of spatiotemporal Big Data (Shi et al, 2016).…”

Earthquake Shake Detecting by Data Mining from Social Network Platforms

“…Haubrock et al [22] tried to establish a concept for a community-based map creation process based on "human sensors" to estimate the earthquake intensity. In China, Xu et al [23] proposed a semi-real time disaster information collection method by using the geographic information system (GIS) to analyze the Global System for Mobile Communications (GSM) messages.…”