A dynamic decision support system based on geographical information and mobile social networks: A model for tsunami risk mitigation in Padang, Indonesia. (December 2016) - Ai, Fuli; Comfort, Louise K.; Dong, Yongqiang; Znati, Taieb
Abstract: In coastal cities, population and property are concentrated in small areas, with abundant resources and convenient transportation, but also with potential tsunami risk, as shown by the tsunami disasters of 2004, 2010 in Indonesia. Coastal area citizens need to evacuate to a safe place as soon as tsunamis occur. The prime evacuation time is very critical for them, but it is delayed in practice by complex information transfer processes. In recent years, spatial information has become an important resource used in dynamic decision support for emergencies, and smart phones have become a primary social communication device during interactions in emergencies. This paper outlines the design and development of a prototype geographical information system centric, social media based dynamic decision support system (GIS-SM-DDSS) that integrates geographical information with Twitter technology to enable self-organized information networks to support decision making and collective actions in emergency situations. The actors include government policy makers, policy managers, highly influential social leaders in local communities, and policy executors and urban citizens impacted by disasters. The main system functions include dynamic disaster risk analysis, timely dissemination of evacuation strategies to community residents, and real-time detection of environmental risk and evacuation support. This system is designed as a field experiment in Padang, Indonesia, to help public officials design tsunami risk maps with timely evacuation routes and transmit these maps to influential leaders in local neighborhoods that are exposed to tsunami risk. Each neighborhood leader would then tweet the detailed route to citizens that follow the tweet. The proposed has potential to support evacuation strategies and real-time guidance of communities at risk during disaster.