Abstract: Population sheltering is a recurrent problem in crisis management that requires addressing two aspects: evacuating vulnerable people using emergency vehicles and regulating movements of pedestrians and individual vehicles towards shelters. While these aspects have received considerable attention in modeling and simulation literature, very few approaches consider them simultaneously. In this paper, we argue that Agent-Based Modeling and Simulation (ABMS) and Optimization are two complementary approaches that can address the problem of sheltering globally and efficiently and be the basis of coherent frameworks for decision- and policy-making. Optimization can build efficient sheltering plans, and ABMS can explore what-if scenarios and use geospatial data to display results within a realistic environment. To illustrate the benefits of a framework based on this coupling approach, we simulate actual flash flood scenarios using real-world data from the city of Trèbes in South France. Local authorities may use the developed tools to plan and decide on sheltering strategies, notably, when and how to evacuate depending on available time and resources.

Abstract: Offshore wind farms in the German North and Baltic Seas have a key role to play in the context of the shift towards renewable energy and in securing electricity supplies in the future. At the same time, however, shipping routes in the North and Baltic Seas are among the busiest in the world, wind farms are increasingly being operated unmanned and conflicts increasingly involve a hybrid element. From these constellations and competing interests, various risk and threat scenarios emerge. By means of a survey among experts from offshore wind industry, this paper first captures the subjective assessment of the risk situation in order to subsequently develop an approach for their evaluation. The paper uses Bayesian networks in order to enable a risk assessment also under inclusion of uncertain parameters.

Abstract: Practice and research collaborations in the disaster domain have the potential to improve emergency management practices while also advancing disaster science theory. However, they also pose challenges as practitioners and researchers each have their own culture, history, values, incentives, and processes that do not always facilitate collaboration. In this paper, we reflect on a 6-month practice and research collaboration, where researchers and practitioners worked together to craft a social media monitoring system for emergency managers in response to the COVID-19 pandemic. The challenges we encountered in this project fall into two broad categories, job-related and timescale challenges. Using prior research on team science as a guide, we discuss several challenges we encountered in these two categories and show how our team sought to overcome them. We conclude with a set of best practices for improving practice and research collaborations.

Abstract: This paper provides a descriptive analysis of the eMARS database, which contains compulsory information on major chemical incidents under the SEVESO Directive. This analysis serves to assess the installations with the highest number of direct fatalities and injuries. At present, the data collected to assess the status of chemical accident risk globally are rather limited. There are some sources of data on chemical accidents in government and industry that might be used to estimate the frequency and severity of some types of events, but they are far from providing a complete perspective that covers all chemical accidents, thus limiting the possibilities of obtaining a more homogeneous picture of the risk of chemical accidents worldwide. Waste storage, treatment and disposal is one of the industrial areas with the highest number of fatalities and injuries, so we must emphasize the importance of this type of industry within the risk maps.

Abstract: Data represents an essential resource to the management of emergencies: organizations have been growingly investing in technologies and resources to lever data as an asset before, during, and after disasters and emergencies. However, research on data usage in emergency management remains fragmented, preventing practitioners and scholars from approaching data comprehensively. To address this gap, this research in progress consists of a systematic review of the literature in a two-steps approach: we first propose a socio-technical framework and use it in an exploratory mapping of the main topics covered by the literature. Our preliminary findings suggest that research on data usage primarily focuses on technological opportunities and affordances and, hence, lacks practical implementation aspects in organizations. The expected contribution is double. First, we contribute to a more comprehensive understanding of data usage in emergency management. Second, we propose future avenues for research on data and resilience.

Abstract: Heat waves are becoming more common and intense with global climate change, which requires deploying resilience strategies of governments to prepare for long-term trends of higher temperatures and carefully plan emergency responses for such extreme heat events. The British Columbia province of Canada is one of the regions severely affected by extreme climatic events in 2021, which resulted in several deaths and put hundreds of thousands of people scrambling for relief. This study examines the public reactions to one of these extreme climatic events, the 2021 Pacific Northwest heatwave, in a non-emergency service request platform to uncover the types of municipal service needs during severe climatic disasters. City of Vancouver 311 system data is used to identify the impact of the heatwave on the frequency and types of service needs and examine the significance of the relationship between climatic conditions and the non-emergency service volumes.

Abstract: This article focuses on the question of the impact of the COVID-19 crisis on air pollution. The chosen approach is based on the principle of “Physics of Decision” (POD), which considers: (i) the performance of a system as a physical trajectory within the framework of its performance indicators, (ii) risks or opportunities (potentialities) as forces that may deviate that trajectory, and (iii) benefits or damages (actualities) as concrete deviations of the performance trajectory. The daily data about the air pollution in Paris area (France) has been gathered for eight years (2014-2021) and three main performance indicators have been chosen. Then, the performance trajectory of each year has been plotted and the expected trajectories of 2020 and 2021 have been guessed from the previous ones. The deviation between the expected and actual trajectories of 2020 and 2021 have been assessed, and using physics and motion laws, evaluated as a deviation force.

Abstract: Crises always impose a difficult compromise between safety and liberty, and the COVID-19 pandemic is no different. Governments have enforced various sanitary restrictions to reduce virus spread. With the help of Artificial Intelligence (AI), the scale of surveillance has risen to unprecedented levels. However, these technologies entail many risks, from potential errors or biases, to their extended enforcement beyond the duration of the initial crisis. Citizens should be aware that these technologies are not infallible, and measure the consequences of errors, so as to make informed decisions about what they want to accept, and for how long. To this aim, we have designed a serious game in the form of a municipal debate between citizens of a virtual town. Some first test sessions helped us improve the game design, and provided proof of the interest of this game to trigger debates and raise awareness.

Abstract: The objective of crisis management is to limit the impact of a feared event that has occurred and to restore the conditions corresponding to a nominal situation. In this context, we will focus on emergency response plans for mass casualty crises. In this paper, we propose a functional modelling of the French generic emergency plan, ORSEC plan, using the Business Process Model and Notation (BPMN). On the basis of this representation, a dysfunctional analysis is performed from a new approach identifying Failure mode, effects and criticality analysis (FMECA), in order to better anticipate, the events likely to interrupt the intervention plan. This work will then be used in a multi-agent dynamic planning and scheduling model to allow an actor to choose among the dynamic planning approaches the one that allows him/her to reach his/her goal.

Abstract: Post-disaster information processing is relevant for the continuous improvement of operations and the reductionof risks. The current methodologies for post-disaster review suffer from several limitations, which reduce their use as a way of translating narrative in data for qualitative and quantitative analysis. Learning or effective knowledge sharing need a common formalism and method. Ontologies are the reference tool for structuring information in a “coded” data structure. Using the investigation of disaster management during the 2017 hurricane season in the French West Indies within the scope of the ANR “APRIL” project, this contribution introduces a methodology and a tool for providing a graphical representation of experiences for post-disaster review and lessons learning, based on a novel approach to case-based ontology development.

Abstract: It is generally agreed in crisis management that a comprehensive visualization of the situation is crucial for an appropriate situational awareness of the staff personnel in control rooms. Therefore an expert group of fire officers in the German State North Rhine Westphalia developed a system for this purpose known as the “tactical wall”. The core of the system is a situation map of the relevant area with so-called tactical signs, i. e. defined graphic symbols for hazards, response units and tactical measures. Moreover, the assignment of response units to tactical sectors or staging areas as well as other relevant information such as the management organization is displayed at defined places within the wall. While the system was purely manual in its original version, a new digital version was recently developed. The user interfaces of this system are web-based and can by intuitively operated after a minor training effort.

Abstract: This paper summarizes the final year of the four-year Text REtrieval Conference Incident Streams track (TREC-IS), which has produced a large dataset comprising 136,263 annotated tweets, spanning 98 crisis events. Goals of this final year were twofold: 1) to add new categories for assessing messages, with a focus on characterizing the audience, author, and images associated with these messages, and 2) to enlarge the TREC-IS dataset with new events, with an emphasis of deeper pools for sampling. Beyond these two goals, TREC-IS has nearly doubled the number of annotated messages per event for the 26 crises introduced in 2021 and has released a new parallel dataset of 312,546 images associated with crisis content – with 7,297 tweets having annotations about their embedded images. Our analyses of this new crisis data yields new insights about the context of a tweet; e.g., messages intended for a local audience and those that contain images of weather forecasts and infographics have higher than average assessments of priority but are relatively rare. Tweets containing images, however, have higher perceived priorities than tweets without images. Moving to deeper pools, while tending to lower classification performance, also does not generally impact performance rankings or alter distributions of information-types. We end this paper with a discussion of these datasets, analyses, their implications, and how they contribute both new data and insights to the broader crisis informatics community.