Implementation of Mobile Data Collection System for Disaster Rapid Emergency Response System Using Open Data Kit

Abstract— Indonesia is a disaster-prone country. One of the main problems related to disaster in Indonesia is that the country does not yet have a good disaster management that can function optimally in responding to disaster. Therefore, this research is aimed to produce a system that can provide emergency response for disaster management using mobile data collection system. This system would provide crucial and immediate information needed by the decision makers to decide emergency response and recovery procedure. This is a research and development research. The data assessment process is carried out by the assessor at the disaster location using ODK collect. The data then is saved in a mobile communication device. Data can be uploaded by the disaster server network when the connectivity is available. Assessment data can be viewed on the application server through a web admin. The system was generated by considering the law of the state of Indonesia no. 24 Year 2007 concerning disaster management agency planning and regulation by Head of BNPB (Indonesia National Board for Disaster Management) No. 9 Year 2008 on the standard procedure for emergency response team of national board for disaster. Data over disaster occurrence, its massive impact, emergency facilities or supplies needed, post-disaster condition, and a missing victim is provided in these queries. After being tested, it appears that the design and implementation of an emergency disaster response system have been able to run satisfactorily according to system specifications. Keywords—rapid emergency disaster response system, mobile data collection, open data kit (ODK

Disasters Preparedness and Emergency Response: Prevention, Surveillance and Mitigation Planning

This Special Issue welcomes research papers on new approaches that have been applied or are under development to improve preparedness and emergency response. We especially encourage the submission of inter-disciplinary and crosscutting research. We also encourage the submission of manuscripts that focus on various types of disasters, disaster and emergency research, and on policy or management solutions at multiple scales

Understanding Network Dynamics in Flooding Emergencies for Urban Resilience

Many cities around the world are exposed to extreme flooding events. As a result of rapid population growth and urbanization, cities are also likely to become more vulnerable in the future and subsequently, more disruptions would occur in the face of flooding. Resilience, an ability of strong resistance to and quick recovery from emergencies, has been an emerging and important goal of cities. Uncovering mechanisms of flooding emergencies and developing effective tools to sense, communicate, predict and respond to emergencies is critical to enhancing the resilience of cities. To overcome this challenge, existing studies have attempted to conduct post-disaster surveys, adopt remote sensing technologies, and process news articles in the aftermath of disasters. Despite valuable insights obtained in previous literature, technologies for real-time and predictive situational awareness are still missing. This limitation is mainly due to two barriers. First, existing studies only use conventional data sources, which often suppress the temporal resolution of situational information. Second, models and theories that can capture the real-time situation is limited. To bridge these gaps, I employ human digital trace data from multiple data sources such as Twitter, Nextdoor, and INTRIX. My study focuses on developing models and theories to expand the capacity of cities in real-time and predictive situational awareness using digital trace data. In the first study, I developed a graph-based method to create networks of information, extract critical messages, and map the evolution of infrastructure disruptions in flooding events from Twitter. My second study proposed and tested an online network reticulation theory to understand how humans communicate and spread situational information on social media in response to service disruptions. The third study proposed and tested a network percolation-based contagion model to understand how floodwaters spread over urban road networks and the extent to which we can predict the flooding in the next few hours. In the last study, I developed an adaptable reinforcement learning model to leverage human trace data from normal situations and simulate traffic conditions during the flooding. All proposed methods and theories have significant implications and applications in improving the real-time and predictive situational awareness in flooding emergencies