Abstract: In the area of information system design, the development of context-aware systems which can provide relevant information in a context-driven fashion to support mobile users in crisis management tasks represents a great challenge. Motivated by the fact that police officers are often overwhelmed by too much information, the goal of the MOSAIC project (a multi-officer system of agents for informed crisis control) is to support mobile police officers by a system which delivers contextualized information that is relevant to the task at hand. In order to elicit the information requirements of police officers who are involved in crisis situations, a questionnaire-based requirements elicitation study has been carried out in the context of the work described here. The results mainly shed light on the situation-specific information requirements of police officers in certain scenarios. The design requirements that follow from our study have clearly the potential to guide the design of context-based information delivery systems for users involved in critical situations such as the police officers we target. Our findings thus offer an important contribution to the field of information system design for crisis management.

Abstract: New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases. Accurate and timely information is as crucial as is rapid and coherent coordination among the responding organizations. We are working towards a multi-pronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. The faster emergency personnel are able to analyze, disseminate and act on key information, the more effective and timelier their response will be and the greater the benefit to affected populations. Our tool consists of encoding multiple layers of open source geospatial data including flood risk location, road network strength, inundation maps that proxy inland flooding and computer vision semantic segmentation for estimating flooded areas and damaged infrastructure. These data layers are combined and used as input data for machine learning algorithms such as finding the best evacuation routes before, during and after an emergency or providing a list of available lodging for first responders in an impacted area for first. Even though our system could be used in a number of use cases where people are forced from one location to another, we demonstrate the feasibility of our system for the use case of Hurricane Florence in Lumberton, a town of 21,000 inhabitants that is 79 miles northwest of Wilmington, North Carolina.

Abstract: To assess current situation properly is crucial for effective decision-making in crisis management. However, gathering accurate information from incidence sites and providing appropriate support for assessment practices faces several challenges. The unique information demands of each crisis situation, the information availability or inter-organizational problems and obstacles to information exchange are important factors that need to be considered in designing ICT. In this contribution we present results from an empirical study about decision-making practices in scenarios of medium to large power outages in Germany. We focused on the needs and practices on information exchange at the level of inter-organizational cooperation. We examined the cooperation of fire departments, police, public administration, electricity infrastructure operators and citizens. Our empirical material reflects particularly conditions and challenges in current situation assessment practices, and we were able to derive design requirements for an inter-organizational situation assessment client as a complementary tool for existing crisis management infrastructures. © 2012 ISCRAM.

Abstract: This practitioner report describes a moor- And turf-fire that occurred in 2011 at the Dutch-German border and was engaged by forces both from the Netherlands and Germany. All involved emergency management organizations were facing interoperability issues when it came to cross-border cooperation, although cooperation handbooks were already in use and the involved organizations were used to work together. The size of the incident and it's remote location called for an increased coordination between all involved actors and across all command levels.

Abstract: In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring.

Abstract: Identifying the assets of a community that are part of its Critical Infrastructure (CI) is a crucial task in emergency planning. However, this task can prove very challenging due to the costs involved in defining the methodology and gathering the necessary data. Volunteered Geographic Information from collaborative maps such as OpenStreetMap (OSM) may be able to make a contribution in this context, since it contains valuable local knowledge. However, research is still due to assess the quality of OSM for the particular purpose of identifying critical assets. To fill this gap, this paper proposes a catalogue of critical asset types, based on the analysis of different reference frameworks. We thus analyze how good the emergent OSM data model is for representing these asset types, by verifying whether they can be mapped to tags used by the OSM community. Results show that critical asset types of all selected sectors and branches are well represented in OSM.

Abstract: This work-in-progress paper presents preliminary findings of ongoing research into alternative peer-to-peer (P2P) communication methods for earthquake early warning (EEW) systems. It expands upon previous work (Prasanna et al., 2022) that explores a network architecture for a decentralised EEW system. This paper explores using Quick UDP Internet Connections (QUIC) over a hole-punched UDP tunnel as a potential alternative to a Software-Defined Wide Area Network (SD-WAN) for peer-to-peer networking in an experimental EEW network architecture. The performance of QUIC is tested and compared to TCP over ZeroTier, an SD-WAN chosen as a P2P communication method in the previous work, over a realistic network topology. The results show that QUIC can outperform TCP over ZeroTier. Future work is needed to produce a method suitable for actual use in an EEW system. This paper contributes to the EEW literature by introducing a new method of communication tailored for EEW.

Abstract: This paper presents the research, design, and development of a comprehensive trauma information system inclusive of 911dispatch, Emergency Medical Services, and hospital trauma information. A proof-of-concept GIS based information system was designed and developed for use by trauma and emergency medical practitioners. Methods used include end-user focus group discussions, quantitative and qualitative data analysis, and an iterative system development process. A framework from prior research was utilized; a framework that considers the visualization of emergency medical events across an end-to-end continuum of patient care. Analyses performed provided a multi-layered understanding of the practical and theoretical implications of using an end-to-end information schema for emergency response and trauma health systems.

Abstract: Agile crisis response organizations can be seen as actor-Agent communities, where artificial coordination strategies are applied to manage activities. This paper provides a classification of artificial coordination strategies, specified in terms of the Rasmussen's three-level model for supervisory control: skill, rule and knowledge based. Three distinct strategies to artificial coordination based on Rasmussen's levels are described. These approaches are applied in a small case study related to the problem of medic-casualty allocation in the crisis response domain. In terms of effectiveness and efficiency, the knowledge-level coordination strategies seem to be the most effective, where the skill-level strategies are the most efficient. Concerning flexibility there is a reverse trade-off with efficiency. Opposed to skill-level strategies, knowledge-level strategies easily adjust to changing operational requirements. On all aspects, the performance of rule-level strategies is in-between knowledge-level and skill-level strategies. The results of this work can be used to improve the performance and effectiveness of actor-Agent communities for mission critical applications.

Abstract: One problem affecting crisis management planning teams is overconfidence- An inflated belief that a plan will be successful. In this paper we compared the effect of several different methods for reducing individual team member confidence levels and compared each to a baseline control condition. One hundred and seventy-eight people participated in one of five conditions to evaluate an H1N1 flu epidemic plan in a university context. Over the course of evaluating the plan, participants provided several ratings of confidence in the plan's success and their understanding. We compared several techniques commonly used, such as critique, Pro/Cons generation, Cons only generation and a newer technique, PreMortem, to a baseline condition. The Pro/Cons generation, Cons only generation and the PreMortem technique all reliably reduced confidence levels more than baseline condition. Furthermore, the Premortem method, imagining that a plan has failed and then generating reasons to explain why, reliably reduced confidence more than each of the other conditions, and therefore can be a useful tool for combating overconfidence in crisis management planning. We discuss the results in the context of sensemaking and decision making theory.

Abstract: The analysis and forecasting of eminent air quality play a significant role in municipal regulatory planning and emergency preparedness. In this paper, a FS-GA-BPNN model forecasting the daily average Air Quality Index (AQI) is proposed. Special procedures for feature extraction to find more potential significant variables and feature selection to remove redundant information and avoid overfitting are conducted before modelling. Three different models – BPNN, GA-BPNN and FS-GA-BPNN are established to compare the prediction accuracy, generalization ability and reliability. 17 parameters involving pollutant concentration, meteorological elements and surrounding factors are found essential for the method effectiveness. The result shows that the FS-GA-BPNN model generally performs superior to ordinary BPNN, suggesting the necessity of extensive data mining and feature extraction for successful machine learning. The results of this paper can help to conduct air quality pre-warning system and improve the emergency planning process of extreme weather events.