List of Papers

The present work is part of the project RE(H)STRAIN which investigates security and its impact on the resilience of the Franco-German high-speed train network in case of terrorist attacks. To improve the capacity of this network to recover a normal functioning after a terrorist attack, appropriate security measures must be determined. To do so, the project investigates, in a scenario-driven holistic approach the entire terrorist sphere of possible actions. Terrorism threat is first defined as a set of single attacks called vignette attacks represented by the triplet actor – weapon – target, then complex attack scenarios are built considering combination rules detailed in this article. In this regard, this work aims at providing end-users with an approach to automatically deduce a set of formalized, consistent and plausible complex attack and defense scenarios to allow in further steps to analyze and improve the resilience level of the high-speed train transportation infrastructure.

Human behaviour is influenced by many psychological factors such as emotions, whose role is already widely recognised. Another important factor, and all the more so during disasters where time pressure and stress constrain reasoning, are cognitive biases. In this paper, we present a short overview of the literature on cognitive biases and show how some of these biases are relevant in a particular disaster, the 2009 bushfires in the South-East of Australia. We provide a preliminary formalisation of these cognitive biases in BDI (beliefs, desires, intentions) agents, with the goal of integrating such agents into agent-based models to get more realistic behaviour. We argue that taking such ''irrational'' behaviours into account in simulation is crucial for them to lay valid results that can be used by emergency managers to better understand the population in future bushfires.

Warnings can help to prevent damages and harm if they are issued timely and provide information that help responders and population to adequately prepare for the disaster to come. Today, there are many indicator and sensor systems that are designed to reduce disaster risks. These systems have proved to be effective. Unfortunately, as all systems including human beings, a part of unpredictable remains. Indeed, each person behaves differently when a problem arises.

In this paper, we focus on people behaviors in crisis situations: from the factors that impact human behavior to the integration of these behaviors, with three different modeling propositions, into a warning system in order to have more and more efficient crisis management systems.

According to many existing studies, the data available on social media platforms such as Twitter at the onset of a crisis situation could be useful for disaster response and management. However, making sense of this huge data coming at high-rate is still a challenging task for crisis managers. In this work, we present an interactive social media monitoring tool that uses a supervised classification engine and natural language processing techniques to provide a detailed view of an on-going situation. The tool allows users to apply various filtering options using interactive timelines, critical entities, and other logical operators to get quick access to situational information. The evaluation of the tool conducted with crisis managers shows its significance for situational awareness and other crisis management related tasks.

Australia is frequently hit by bushfires. In 2009, the ''Black Saturday'' fires killed 173 people and burnt hectares of bush. As a result, a research commission was created to investigate, and concluded that several aspects could be improved, in particular better understanding of the population actual behaviour, and better communication with them. We argue that agent-based modelling and simulation is a great tool to test possible communication strategies, in order to deduce valuable insight for emergency managers before new fires happen. In this paper, we extend an existing agent-based model of the population behaviour in bushfires. Concretely, we added a communication model based in social sciences, and user interactivity with the model. We present the results of first experiments with different communication strategies, providing valuable insight for better communication with the population during such events. This model is still preliminary and will eventually be turned into a serious game.

Human behaviour during crisis evacuations is social in nature. In particular, social attachment theory posits that proximity of familiar people, places, objects, etc. promotes calm and feeling of safety, while their absence triggers panic or flight. In closely bonded groups such as families, members seek each other and evacuate as one. This makes attachment bonds necessary in the development of realistic models of mobility during crises. In this paper, we present our review of evacuation behaviour, theories on social attachment, crises mobility, and agent-based modelling of these. In our conclusion, we found that social attachment influences mobility in the different stages of evacuation (pre, during and post). Based on these findings, we intend in future work to develop a multi-agent model of mobility during seismic crises, using the belief, desire and intention (BDI) agent architecture.

Managing crises is considered as one of the most complicated organizational and managerial task. Indeed, dealing with such situations calls for many groups from different institutions and organizations to interact and collaborate their efforts in a timely manner to reduce their effects. However, response organizations are challenged by several problems. The urgent need of a shared and mutual situational awareness, information and knowledge about the situation are distributed across time and space and owned by both organizations and people, decisions and actions have to be made rapidly, under stress and time pressure. The contribution outlined in this paper is suggesting a crowdsourcing-based approach for decision making in collaborative crisis management based on the literature requirements. The objective of the approach is to support situational awareness and enhance the decision making process by involving citizens in providing opinions and evaluations of potential response actions.

Flood evacuation planning models are an important tool used in the preparation for flooding events. Authorities use the plans generated by flood evacuation models to evacuate the population as quickly as possible. Contemporary models consider the whole solution space and use a stochastic search to explore and produce solutions. The one issue with stochastic approaches is that they cannot guarantee the optimality of the solution and it is important that the plans be of a high quality. In this paper, an alternative heuristically driven approach is proposed as an alternative avoiding this disadvantage completely. The determinism of the model means that the optimality of solutions found can be readily verified.

Today, during fire compartments, decision-making rescue teams is mainly based on human decisions.

During these interventions, the transition between a localized and generalized fire can take several forms. One of the most important vectors in the propagation of fire compartments is the smoke due to its high temperature and the large amounts of energy it contains. Despite its extreme danger, smoke remains important to study because it conveys valuable information. To carry out this study, a large-scale experimental cell with a hot-fueled propane-fired burner will be used. The smoke dynamics is analyzed using non-intrusive measurement techniques such as Particle Image Velocity and Laser Induced Incandescence. All the measurements carried out are compared with Large Eddy Simulation simulations of the experiment using FDS.

Automated planning is a domain of Artificial Intelligence which aims to study the deliberation process used to choose and organize actions by anticipating their expected outcomes. In this paper, we discuss the use of automated planning techniques in crisis management contexts. To begin with, the crisis management planning problem is formalized in light of the conceptual model for automated planning. In addition, we describe the conceptual scheme of an information system generating action plans in order to support decision-makers in crisis management. Finally, a proof of concept implementation of the aforementioned system is presented.

This paper is about how artefacts are used in the process of creating a common operational picture (COP) during large police operations. We apply “artefactual multiplicity” as analytical lens. The research is based upon Ethnographical field studies. Based upon the five separate studies, the artefacts studied are found to be very important as collaborative tools. The contribution is 1) extensive knowledge about how the artefacts are used in the establishment of the COP during large police operations, where the establishing of the COP is, to a very large extent, dependent upon the artefacts’ collaborative and multiple functions; 2) the proposal and suggestion to study the whole staff and the artefacts used by the staff as one heterogeneous unit, as a record of activities.

This paper reports on a qualitative study exploring personal crises that emerge during transitions. Personal crises, like crises caused by natural disasters, often lead to new behaviors and opportunities for technology appropriation and design. Through interviews with 14 military veterans re-integrating into civilian society, we find that the veterans’ transitions involve several impediments related to translation work—the process through which people make sense of the conflicting rules and norms between former and present social realities. We developed guidelines for the design of new wearable devices that can aid veterans in the translation process by proposing a six-fold schema of design criteria for wearables—detection, nudging, portability/proximity, inconspicuousness, connectivity, and reflection—to empower veterans in managing personal crises, fostering resilience, and creating normalcy. Finally, we develop the concept of identity creep to explicate these translation-breakdowns.

During emergency crises it is imperative to collect, organise, analyse and share critical information between individuals and humanitarian organisations. Although different models and platforms have been created for helping these particular issues, existing work tend to focus on only one or two of the previous matters. We propose the DoRES ontology for representing information sources, consolidating it into reports and then, representing event situation based on reports. Our approach is guided by the analysis of 1) the structure of a widely used situation awareness platform; 2) stakeholder interviews, and; 3) the structure of existing crisis datasets. Based on this, we extract 102 different competency questions that are then used for specifying and implementing the new three-tiers crisis model. We show that the model can successfully be used for mapping the 102 different competency questions to the classes, properties and relations of the implemented ontology.

In one of the most influential papers in the history of Software Engineering, L. Osterweil analyzed the nature of both software and software development processes to conclude that the latter shared many characteristics with the former and, as a consequence, software development principles and techniques could be applied in the definition and exploitation of processes. Here, we do an imitation exercise to claim that emergency plans are advanced software artifacts and, hence, modern software development principles, methods, techniques and tools can be used in their development and enactment. We advocate for a change of paradigm in which the idea of emergency plans as text-based documents is replaced by that of active, complex digital objects with state and behavior that drive emergency response processes, and also several preparedness activities such as drills and training exercises. These new plans are the result of a systematic process we call Emergency Plan Engineering.

We discuss the results of a research project aimed at exploring the use of social media in emergency communication by officers operating at a local level. We performed 16 semi-structured interviews with national level expert informants, and with officers operating at the municipality and province (prefectures) level in an Italian region. Social media usage appears distributed over a continuum of engagement, ranging from very basic usage to using social media by adopting a broadcasting approach, to deeper engagement, which also includes continuous interaction with citizens. Among the main barriers to a broader adoption of social media, cultural considerations seem to prevail, along with the lack of personnel, a general concern toward social media communication reliability, and the perceived distance between the formal role of institutions and the informal nature of social media communication.

Rio Operations Center (COR) was the leading agency of Rio de Janeiro Prefecture responsible for the monitoring the Rio 2016 Olympic and Paralympic Games operations, due to its role in the prevention, monitoring, mobilization, communication, and, constant learning on the operations risk management of public services. Through data collected by direct observations, interviews, internal documents and access to the systems and software from COR, the planning and preparation of the IT Infrastructure and monitoring teams for the Games are analyzed. The paper explores how the systems and monitoring teams were developed and the use planned for the games, stressing the relevance of different aggregative software and the Planning Team for the integration and the communication among teams. The complete analysis of the integrated operations of the data collected during the Games is expected to be presented in a future paper.

Crowdsourced Geographic Information (CGI) has emerged as a potential source of geographic information in different application domains. Despite the advantages associated with it, this information lacks quality assurance, since it is provided by different people. Therefore, several authors have started investigating different methods to assess the quality of CGI. Some of the existing methods have been summarized in different classification scheme. However, there is not an overview of the methods employed to assess the quality of CGI in the absence of authoritative data. On the basis of a systematic literature review, we found 13 methods that can be employed to this end.

The aftereffects of disaster events are significant in tourist destinations where they do not only lead to destruction and casualties, but also long-lasting economic harms. The public perception causes tourists to refrain from visiting these areas and recovery of the tourist industry, a major economic sector, to become challenging. To improve this situation, current information about the tourist and infrastructure recovery is crucial for a “rebranding”- information that is however time and cost-intensive in acquisition using traditional information sources. An alternative data source that has shown great potential for information gathering in other disaster management phases, which was less considered for disaster recovery purposes, is Volunteered Geographic Information (VGI). Therefore, this paper introduces a VGI-based methodology to address this task. Initial analyses conducted with Flickr data indicate a potential of VGI for recovery monitoring, whereas the analysis of OpenStreetMap data shows, that this form of VGI requires further quality assurance.

As of 26 December 2016, seventeen electronic triage systems for disaster triage have been proposed in the ACM, IEEE, and ISCRAM publication databases. Most of these systems have remained inside the lab; the rest have disappeared entirely. Responders still prefer to do triage with paper tags from the 1960's, while no research has been presented on why the proposed e-triage systems have not found acceptance and use in the field. Based on exhaustive literature research and on the findings from the four-year long EU research project (anonymized), this paper presents e-triage acceptance dimensions, analyzes the main reasons why proposed systems have been rejected, and guides designers towards upcoming, well-accepted e-triage systems.

Natural and man-made hazards have many unexpected consequences that concern as many heterogeneous services. The GéNéPi project offers to support officials in addressing those events: its purpose is to support the collaboration in the field and the decision-making in the crisis cells.

To succeed, the GéNéPi system needs to be aware of the ongoing crisis developments. For now, its best chance is to benefit from the ever growing number of available data sources. One of its goals is, therefore, to learn how to manage numerous, heterogeneous, more or less reliable data, in order to interpret them, in time, for the officials. The result consists on a situation model in the shape of a common operational picture.

This paper describes every stage of modelling from the raw data selection, to the use of the situation model itself.

In emergency domain, experts must make decisions both usual and unusual. These decisions lead to unpredictable impacts, causing the need for these experts to deal with impacts mitigation. Unexpected need of impacts mitigation consists in an overload of material resources and expert cognitive capacity. After decision making, impacts mitigation demands valuable expert efforts. To address this problem, this paper proposes a decision impact projection in early stages of emergency management, during planning stage. In this way, this paper proposes the method called General Conceptual Framework (GCF) and the Framework of Projected Impact on Emergency Domain (PIED). Through the proposed method, PIED Framework was developed, contributing for the characterization of impact projection in emergency environments.

In the years following Chernobyl, many reports and projects reflected on how to improve emergency management processes in dealing with an accidental offsite release of radiation at a nuclear facility. A common observation was the need to address the inevitable uncertainties. Suggestions were made and some were researched in some depth. The Fukushima Daiichi Disaster has led to further reflections. However, many uncertainties inherent in responding to a threatened or actual release remain unaddressed in the analyses that are conducted to support the emergency managers in their decision making. They are often left to factor in allowances for the uncertainty through informal discussion and unsupported judgement. In this paper, we summarise the issues and suggest the R&D programmes needed to provide emergency managers with better guidance on the uncertainty and how it may affect the consequences of taking different countermeasures.

The organizers of crisis management exercises want scenario credible and pedagogical from the beginning until the end. For this reason, they call on an animation team that can use different communication channels. The aim of this article is to understand the different types of animation by analyzing the professional experience of the facilitators and the type of casting that can be done. Finally, a definition of four levels of animation is proposed. These levels are associated with different types of messages and rhythm settings. The main objective is to improve the execution of the scenario during a crisis management training.

Recent events have demonstrated the vulnerability of IT-systems of different companies, organisations or even governments to hacker attacks. Simultaneously, information technologies have become increasingly established and important for institutions of various branches. With respect to modern terrorism developments, cyber-attacks may be used to physically harm critical infrastructures. This leads to a new dimension of cyber-attacks called “terrorist cyber-attacks”.

This research-in-progress paper aims to develop a process model for data acquisition and support of decision making that seeks to enhance the security of public transportation in the context of counterterrorism. Therefore, a generic process model for terrorist cyber-attacks – produced in the research project RE(H)STRAIN – is introduced as a basis for a decision support system (DSS). In the future, such models could improve the deci-sion process by comparing the effectiveness of different security measures.

Effective risk management begins with successful risk identification. Unfortunately, traditional approaches may lead to haphazard and incomplete results. To overcome this, we present a new integrative approach to improve risk identification that sequentially investigates protector-views and narrow scopes using literature review, ethnography, and subject matter expertise. This paper illustrates this approach by identifying man-made and natural threats to mass-gathering events in general, and stadium security as an example. Improving risk identification enhances resilience to known risks by enabling planning and development of targeted response strategies. Working from a more complete portfolio of risk resilience strategies may also improve flexibility and agility to respond to new and emerging risks.

Local citizens can use social media such as Twitter to share and receive critical information before, during, and after emergencies. However, standard methods of identifying local citizens on Twitter discover only a small proportion of local users in a geographic area. To better identify local citizens and their social media sources for local information, we explore the information infrastructure of a local community that is constituted prior to emergencies through the everyday social network curation of local citizens. We hypothesize that investigating social network ties among local organizations and their followers may be key to identifying local citizens and understanding their local information seeking behaviors. We describe social triangulation as a method to identify local citizens vis-à-vis the local organizations they follow on Twitter, and evaluate our hypothesis by analyzing users’ profile location information. Lastly, we discuss how social triangulation might support community preparedness by informing emergency communications planning.

Cooperation and interaction demands digital user skills. In an agile context, there is no time for learning in the doing implicating that multiple skills need to be practiced in forehand. Since exercises is one way of enhancing skills needed in crises situations it is relevant to know what skills are trained during exercises. This review aims to understand what skills has been practiced during exercises executed in Sweden during 2010 until 2014. If ICT has been a part of practicing these skills, experience will be highlighted. Data was obtained from 15 evaluations from exercises including multiple actors. Most exercises practiced collaboration, communication, information and shared situation awareness skills. Results showed that 4 of 15 has a specific goal concerning technology use. Exercises with explicit technology goals are effective for changing opinion about the tool in question. Actors are requesting more exercises specifically concerning routines and how to use technological tools.

This paper presents work in progress on developing a meta-theory of C2 in emergency management. Most research in C2 focuses just on one or two scientific disciplines. Just one paper has been found that gives a systematic overview of the science of C2.

The approach taken employs entity-relationship modelling, yielding a set of scientific disciplines. These disciplines are compared with five military C2 doctrine publications. Doctrine found in at least four publications corresponded to the disciplines of decision theory, leadership theory, organizational theory, psychology, and the degree of delegation. Some topics not covered by the disciplines were found, indicating that analysis should be extended to C2 processes, resilience, and agility, permitting the development of guidance for practitioners. Further work is needed to compare the disciplines with civilian doctrine. Moreover, the disciplines could be compared to ICCRTS and ISCRAM conference proceedings, yielding an assessment of the maturity of C2 research

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This paper analyses the social media communications surrounding the 2015 - 2016 series of winter storms in the UK. Three storms were selected for analysis over a sustained period of time; these were storms Desmond, Eva and Frank which made landfall within quick succession of one another. In this case study we examine communications relating to multiple hazards which include flooding, evacuation and weather warnings using mainstream media content such as news stories, and online content such as Twitter data. Using a mixed method approach of content analysis combined with the application of a conceptual framework, we present (i.) the network of emergency responders managing events, (ii.) an analysis of crisis communications over time, and (iii.) highlight the barriers posed to effective social media communications during multi-hazard disasters. We conclude by assessing how these barriers may be lessened during prolonged periods of crisis.

Cities face a wide range of risks. Potential threats range from natural disasters and the (relatively slow) environmental change, to man-made issues like extremism. To overcome such threats, cities ought to be resilient, capable of resisting problems, of adapting to new situations, and overcoming crises. Effective communication is particularly crucial for a resilient city. Rather than trusting that relevant stakeholders, municipal staff and citizens will intuitively communicate in the ideal way, cities should see communication as a strategic aspect of their resilience development. Thus, how resilient cities communicate should be strategically managed. In this paper, we present immediate results from an ongoing European project called Smart Mature Resilience. In this project, we work with seven cities towards the ultimate goal of developing a Resilience Management Guideline for all European cities. Moreover, we intend to set up a resilience backbone in Europe, which will be driven by effective communication between cities.

This paper details the creation of a massive (over 32,000 messages) artificially constructed ‘Twitter’ microblog stream for a regional emergency preparedness functional exercise. By combining microblog conversion, manual production, and a control set, we created a web-based information stream providing valid, misleading, and irrelevant information to public information officers (PIOs) representing hospitals, fire departments, the local Red Cross, and city and county government officials. Addressing the challenges in constructing this corpus constitutes an important step in providing experimental evidence that complements observational study, necessary for designing effective social media tools for the emergency response setting. Preliminary results in the context of an emergency preparedness exercise suggest how social media can participate in the work practice of a PIO concerning the assessment of the disaster and the dissemination of information within the emergency response organization and to the public.

Simulation and serious games (SSG) are advocated as promising technologies supporting training and increasing the skills necessary to deal with complex and unexpected situations. Based on an investigation of SSG use for fire fighter training in nine countries, this paper is examining key elements and success factors that can counteract potential obstacles and challenges of SSG implementation in organizations responsible for emergency management. Data comes from interviews and observations. By contrasting the different incentives and views regarding the SSG use, this paper contributes to a better understanding of challenges handling these technologies. The results confirm many benefits of SSGs, and provide a broader understanding of integrating these technologies into organizational practices. Only by connecting the local, organizational strategies and user values with technology values the usage can be experienced as successful. This connection requirement is by far not obvious since values are formulated differently at the main stakeholders.

Emergency response planning is a complex task due to multiple organizations involved, different planning considerations, etc. Using artificial intelligence collaborative planning helps in the automatic planning for complex situations. Analyzing all impacting factors along with plans that are executable can facilitate the decision making in Emergency Operations Centers for an agile emergency response. A main component of a planner is a knowledge base. Although many systems are developed to support decision making in emergency response or recovery, they either focus on specific or small organizations, or rely on simulations. The multiplicity of the emergency response documents and their structure makes the knowledge acquisition complex. In this paper, we explain the process of extracting knowledge based on hierarchical task networks and how it speeds up the reactivity to a disaster.

The use of social media is not only part of everyday live but also appearing in crises and emergencies. Many studies focus on the concrete use of social media during a specific emergency, but the prevalence of social media, data access and published research studies allow the examination in a broader and more integrated manner. This work-in-progress paper presents the results of a case study with the Fire Department Frankfurt which is one of the biggest and most modern fire departments in Germany. The findings relate to social media technologies, organizational structure and roles, information validation, staff skills and resources, and the importance of volunteer communities. In the next step, the results will be integrated into the frame of a comparative case study with the overall aim of examining the impact of social media on how emergency services respond and react in an emergency.

Social media platforms have become a source of high volume, real-time information describing significant events in a timely fashion. In this paper we describe a system for the real-time extraction of information from text and image content in Twitter messages and combine the spatio-temporal metadata of the messages to filter the data stream for emergency events and visualize the output on an interactive map. Twitter messages for a geographic region are monitored for flooding events by analysing the text content and images posted. Events detected are compared with a ground truth to see if information in social media correlates with actual events. We propose an Intrusion Index as part of this prototype to facilitate ethical harvesting of data. A map layer is created by the prototype system that visualises the analysis and filtered Twitter messages by geolocation.

We have been studying the standardization of an emergency-management support system mainly for natural disasters at the local-government level. The system provides information from three viewpoints, “Plan: What should we do?”, “Do: What are we doing?”, and “See: What kind of situations are we in?” to support decision making at an emergency operations center. Rapid and accurate judgment prevents the occurrence of new damage and the expansion of damage, and as a result resilience will increase. We investigated its applicability to emergency management for cyber incidents through a cyber-defense exercise.

Volunteered geographic information can be seen as valuable data for various applications such as within disas-ter management. OpenStreetMap data, for example, are contributed by remote mappers based on satellite im-agery and have increasingly been implemented in response actions to various disasters. Yet, the quality often depends on the local and country-specific knowledge of the mappers, which is required for performing the map-ping task. Hence, the question is raised whether there is a possibility to train remote mappers with country-specific mapping instructions in order to improve the quality of OpenStreetMap data. An experiment is con-ducted with Geography students to evaluate the effect of additional material that is provided in wiki format. Furthermore, a questionnaire is applied to collect participants’ socio-demographic information, mapping experi-ence and feedback about the material. This pre-study gives hints for future designs of country-specific mapping instructions as well as the experiment design itself.

This paper focuses on the assessment of ethical, legal and social issues (ELSI) in relation to agile information systems in the domain of crisis management. The authors analyse the differing needs of a move from a traditional approach to the development of information systems to an agile approach, which offers flexibility, adaptability and responds to the needs of users as the system develops. In turn, the authors argue that this development requires greater flexibility and an iterative approach to assessing ELSI. The authors provide an example from the Horizon 2020 EU-funded project iTRACK (Integrated system for real-time TRACKing and collective intelligence in civilian humanitarian missions) to exemplify this move to an iterative approach in practice, drawing on the process of undertaking an ethical and privacy impact assessment for the purpose of this project.

This paper proposes a performance measurement definition to consider sustainable development principles in the humanitarian supply chain operations (source, make, deliver). Previous research has shown the challenge for humanitarian organizations to consider the three sustainability pillars people, planet and profit in their decision-making processes. Based on field research with the IFRC and a literature review on humanitarian performance measurement and sustainability, we define a set of criteria, objectives and Key Performance Indicators that translates sustainability concepts to concrete humanitarian operations. Based on the Triple Bottom Line approach, the environmental and social dimensions are added to the economic dimension, which is standard in HSC literature and practice. The aim of this study is to set the basis for a Decision Support System (DSS) in operations planning.

One of the most used sources of information for fast and flexible crisis information is social media or crowdsourced data, as the information is rapidly disseminated, can reach a large amount of target audience and covers a wide variety of topics. However, the agility that these new methodologies enable comes at a price: ethics and privacy. This paper presents an analysis of the ethical risks and implications of using automated system that learn from social media data to provide intelligence in crisis management. The paper presents a short overview on the use of social media data in crisis management to then highlight ethical implication of machine learning and social media data using an example scenario. In conclusion general mitigation strategies and specific implementation guidelines for the scenario under analysis are presented.

Social Media monitoring has become a major issue in crisis and emergencies management. Indeed, social media may ease the sharing of information between citizens and Public Safety Organizations, but it also enables the rapid spreading of inaccurate information. As information is now provided and shared by anyone to anyone, information credibility is a major issue. We propose an approach to detect rumors in social media.

This paper describes our work on semantic graph based information fusion, enhanced with uncertainty management capabilities. The uncertainty management capability enables managing the different level of credibility of actors of an emergency (different PSO officers and citizens).

Functions for information synthesis, conflicting information detection and information evaluation were developed and test during experimentation campaigns. The synthesis and conflicting information detection functionalities are very welcome by end-users. However, the uncertainty management is a combinatorial approach which remains a limitation for use with large amount of information.

Logistics activities are of high importance for the success of a humanitarian operation and can be responsible for up to 80% of its costs. Vendor Managed Inventory, a concept successfully applied in commercial logistics, might be a possibility to enhance the effectiveness of humanitarian logistics operations. However, there is a lack of an appropriate assessment of the VMI applicability for humanitarian organizations. We propose an adjusted VMI Readiness Score for humanitarian logistics, a tool adapted from a commercial context for the specific requirements of a humanitarian scenario, to gain a general impression of the suitability of VMI. The score is exemplary applied to the IFRC and the results indicate that it is worthwhile to further investigate the applicability of VMI in humanitarian logistics.

Many machine learning techniques have been proposed to reduce the information overload in social media data during an emergency situation. Among such techniques, domain adaptation approaches present greater potential as compared to supervised algorithms because they don’t require labeled data from the current disaster for training. However, the use of domain adaptation approaches in practice is sporadic at best. One reason is that domain adaptation algorithms have parameters that need to be tuned using labeled data from the target disaster, which is presumably not available. To address this limitation, we perform a study on one domain adaptation approach with the goal of understanding how much source data is needed to obtain good performance in a practical situation, and what parameter values of the approach give overall good performance. The results of our study provide useful insights into the practical application of domain adaptation algorithms in real crisis situations.

In order to detect and control the critical potential risk source of railway more scientifically, more reasonably and more accurately in complex accident context, a knowledge modeling method of risk inter-relation is proposed based on ontology modeling of accident context. First, the mechanism of accident causation is summarized based on the accident case analysis. Then, the knowledge model of accident cause is built based on ontology theory, including the ontology model of two context instances. Last but not least, the risk inter-relation rules with different dimensions of inter-relation patterns are inferred based on the instantiation of ontology model. The two context instances are used to illustrate the identification process of risk inter-relation. The results prove the rationality of the method, which can provide a reference for the precise railway risk pre-control.

Disaster managers report that several disasters would have turned out on a dramatic scale without spontaneous unaffiliated on-site volunteers (SUV). Since SUVs are usually not integrated in chains of command and behave in a certain pattern of its own, coordination of SUVs becomes a challenge for disaster management. One key to coordination is communication and adequate support by information systems. However, real disasters or field tests are usually too expensive, elaborate, and partly impossible when coordination of SUVs is to be exercised or novel tools and methods must be evaluated. Simulating the SUV’s behavior by software-agents is considered a constructive solution, however, the specification of simulation settings is an open research field. Therefore, this paper aims at identifying relevant attributes affecting SUVs behavior by a state-of-the-art literature review, classifying and discussing the attributes. Our results provide a sound basis for defining SUV-agents and performing suitable simulations in the future.

The improving of disaster resilience in cross-border areas causes special challenges. The involved countries use different structures in their civil protection systems and have to work together under more difficult conditions than in local incidents. Furthermore, in the past the involved countries worked mainly on their own and focused on the concerned areas in their territories. Project INCA will develop a resilience framework to support decision-makers. The framework will focus on information management, the implementation of volunteers and the needs of Citizens who are receiving medical care. Therefore, a case study region on the German-French border was defined and a scenario-based approach will be used. The scenario is a long-lasting power-outage.

Social media is becoming one of the prominent sources of information in mass emergencies both for the public and the authorities. Public and humanitarian organizations monitor social media in order to extract useful information during emergencies. In this paper we propose a new method for identifying situation awareness (SA) tweets for emergencies. We take a human centered design approach to developing a visual analytics prototype, SMA-RT (“Social Media Analyst Responding Tool”), informed by social media analysts and emergency practitioners. Our design offers insights into the main requirements of social media monitoring tools used for emergency purposes. It also highlights the role that human and technology can play together in such solutions. We embed a machine learning classifier to identify SA tweets in a visual interactive tool. Our classifier aggregates textual, social, location, and tone based features in order to increase precision and recall of SA tweets.

In the recent decade the evolvement and widespread success of new technologies in particular in the field of computing power, network bandwidth, mobile networks and wearable devices have prepared the foundation for completely new approaches in crisis management. Currently, we are at the edge that such new technologies for crisis management are becoming a real and practically applicable option, e.g. in the field of alerting, crowdsourcing, and crowdtasking. In parallel, we witness in the recent years that citizens are more and more willing to help during crisis and disasters, thus providing a large – yet unused – potential for agile support in disaster preparation and response. In this paper we present the results of the research project ENSURE: a general architecture and a system design for the coordination of spontaneous volunteers for agile disaster response.

Crisis and disaster management are increasingly characterized by interactive systems intended to be valuable support for professionals and volunteers in preventing, preparing for, responding to and recovering from crises and disasters. Therefore, usability in terms of safe and efficient usage of computer-based solutions becomes a crucial factor for successful crisis and disaster management. In order to be ensured, it has to be addressed systematically throughout a development project. In this paper, recommendations for applying usability engineering processes to crisis and disaster management systems are described. With respect to the specific challenges in this field of application, they are derived from standards and guidelines (IEC 62366-1:2015, ISO 14971:2007) from the safety-critical domain of medical device design. Particularly, relationships and interactions between usability engineering and risk managements measures (e.g. hazard-related use scenarios) are described.

There is general consensus that subsurface wastewater injections associated with unconventional oil and gas operations are responsible for the rapid increase of earthquake activity in the mid-U.S. Understanding the public response to these earthquakes is crucial for policy decisions that govern developing situational awareness and addressing perceived risks. However, we lack sufficient understanding of the reactive and recovery response behavior of the public tending to occur in the spatiotemporal vicinity of these events. Here, we review the spatiotemporal distribution of public response to the September 3, 2016, M5.8 earthquake in Pawnee, Oklahoma, USA, via a social media network (Twitter). Our findings highlight a statistically significant correlation between the spatial and temporal distribution of public response; and suggest the possible presence of a spatial distance decay, as well as a temporal far-field effect. Understanding the underlying structure of these correlations is fundamental to establishing deliberate policy decisions and targeted response actions.

Social media during the prodromal phase of the crisis lifecycle is critically understudied in the academic literature, as is the understanding of the role of engagement in these mediums by crisis responders and managers in helping the public prepare for a crisis event. This study analyzed 2.8 million tweets captured prior to the landfall of Hurricane Sandy. Risk communicators were identified and their tweets assessed for characteristics in the strategic use of Twitter and their levels of engagement with the general public. This work in progress provides a foundation for a longitudinal study analyzing future crisis events and measuring the growth of expertise and engagement in social media by crisis communicators.

Mobile Instant Messaging platforms like WhatsApp are becoming increasingly popular. They have expanded access to digital text, audio, picture, and video messaging. Integrating them into existing crisis monitoring and response platforms and workflows can help reach a wider population. This paper describes a first attempt to integrate WhatsApp into Aggie, a social media aggregating and monitoring platform. We report on the deployment of this integration during Ghana's 2016 election, along with Twitter, Facebook, and RSS. The WhatsApp messages collected by Aggie during the election improved the effectiveness of the monitoring efforts. Thanks to these messages, more incidents were found and escalated to the Electoral Commission and security forces. From interviews with people involved in monitoring and response, we found that the WhatsApp integration helped their coordination and monitoring activities.

Planning, controlling and coordinating search and rescue operations is complex and time is crucial for survivors who must be found quickly. The search planning phase is especially important when the location of the incident is unknown. We propose, implement, solve, and evaluate mathematical models for the multiple rectangular search area problem. The objective is to define optimal or near-optimal feasible search areas for the available search and rescue units that maximize the probability of success. We compare our new model to an existing model on problem instances of realistic size. Our results show that we are able to generate, in a reasonable time, near optimal operationally feasible plans for searches conducted in vast open spaces. In an operational context, this research can increase the chances of finding survivors. Ultimately, as our models get implemented in the Canadian Coast Guard search planning tool, this can translate into more lives being saved.

In this paper we present a qualitative, social network based, power analysis of relief and recovery efforts in the aftermath of the 2015 earthquakes in Nepal. We examine how the interplay between humanitarian power relations and e-resilience influenced communities’ ability to respond to the destruction brought about by the disaster. We focus in particular on how power dynamics affect online spaces and interactions at the hyper local level (or ‘the last mile’). We explain how civic technology initiatives are affected by these power relationships and show how their efforts may reinforce social inequalities – or be sidelined – if power dynamics are not taken into consideration. However, on the basis of a power analysis and a case study, we show that when civil technology initiatives do strategically engage with these dynamics, they have the potential to alter harmful power relations that limit community e-resilience.

The past decade has seen a significant increase in the use of social media for disaster management. This is due especially to the widespread usage of mobile devices and also to the different data types and data formats that social media supports. In recent years, research in the area of social media visual analytics has also gained interest in the scientific community. Research in this area however, lacks a comprehensive overview on social media visual analytics for disaster management. Hence, in this paper we survey available literature on social media visual analytic toolkits with the goal to outline the major characteristics and features, and to examine the extent to which they cover the full cycle of disaster management. Our main purpose is to provide a foundation based on the current literature that can help shape future research directions to enhance social media visual analytic toolkits for full cycle disaster management.

The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relief operations accordingly. In addition to the textual content, people post overwhelming amounts of imagery data on social networks within minutes of a disaster hit. Studies point to the importance of this online imagery content for emergency response. Despite recent advances in the computer vision field, automatic processing of the crisis-related social media imagery data remains a challenging task. It is because a majority of which consists of redundant and irrelevant content. In this paper, we present an image processing pipeline that comprises de-duplication and relevancy filtering mechanisms to collect and filter social media image content in real-time during a crisis event. Results obtained from extensive experiments on real-world crisis datasets demonstrate the significance of the proposed pipeline for optimal utilization of both human and machine computing resources.

Simulation exercises as a training tool for enhancing preparedness for emergency response are widely adopted in disaster management. This paper addresses current scenario design processes, proposes an alternative approach for simulation exercises and introduces a conceptual design of an adaptive scenario generator. Our work is based on data collected during TRIPLEX-2016 exercise, organized by IHP . The planning process and scenario selection of simulation exercises impact directly the effectiveness of intra- and inter-organizational cooperation. However, collective learning goals are rarely addressed and most simulations are focused on institution-specific learning goals. Current scenario design processes are often inflexible and begin from scratch for each exercise. In our approach, we address both individual and collective learning goals and the demand to develop scenarios on different layers of organizational learning. Further, we propose a scenario generator that partly automates the scenario selection and adaptively responds to the exercise evolvement.

In recent years, big data such as GPS equipped mobile phone data and building data have been generated and accumulated in Japan. As a result, the diversification and accumulation of data causes an explosive increase in the number of earthquake disaster scenarios and bringing some problems. When considering disaster prevention planning, we have to think about which scenario to focus on and how to respond to various scenarios. As a means to solve this problem, by firstly grasping the damage distribution of building collapse, fire, and tsunami from various data, it will be possible to estimate damage based on people flow and various hypocenter models of earthquakes. We propose a method of using Sparse modeling for extracting scenarios that are scientifically important. As a result, this paper is possible to quickly grasp the scenario distribution, which was previously impossible to do, and to extract the scenarios.

It is important to evaluate the effects of improving the disaster vulnerability of towns by using various indices related to human damage. In this paper, we focus on conversion of low quake-resistant old buildings. Firstly, we construct a simulation model, which describes property damage (such as building-collapse and street-blockage), wide-area evacuation behavior, and fire-brigade’s activities immediately after a large earthquake occurs. Next, using the simulation model, we estimate the travel time required for evacuation, the number of evacuees trapped on streets (or in blocks), and the access time of fire-brigades to fires in case that the ratio of quake-resistant buildings in the area increases to a certain value. Based on the results, we discuss the effects by converting old buildings into quake-resistant ones on reducing the difficulty in wide-area evacuation and improving the accessibility of fire-brigades in multiple study areas with different characteristics.

The generation and supply of electricity is currently about to undergo a fundamental transition that includes extensive development of smart grids. Smart grids are huge and complex networks consisting of a vast number of devices and entities which are connected with each other. This fact opens new variations of disruption scenarios which can increase the vulnerability of a power distribution network. However, the network topology of a smart grid has significant effects on urban resilience particularly referring to the adequate provision of vital services of critical infrastructures. An elaborated topology of smart grids can increase urban resilience. In this paper, we discuss the role of smart grids, give research impulses for examining diverse smart grid topologies and for evaluating their impacts on urban resilience by using an agent based simulation approach which considers smart grid topology as a model parameter.

This research presents the conceptual Business Intelligence (BI) model for disaster management. BI can provide agility capacity for decision making in dynamic environment among different agencies. This project designs and develop a data warehouse using multi-dimensional model for severity analysis of flood and landslide in risk area using case study from Department of disaster prevention and mitigation (DDMP), Phuket, Thailand. The concept of BI can be applied for extremely heterogeneous data structures and data platform environment to improve data quality and expose to better decision-making for disaster management. In the next stage of this project, we will integrate more data sources from other agencies for example GIS data from Phuket land-use planning and flooding prediction model database. The result of this study will help organization deploy BI more effectively.

Previous research into the role of social media in crisis communication has tended to focus on how sites such as Twitter are used by emergency managers rather than other key stakeholders, such as critical infrastructure (CI) operators. This paper adds to this emergent field by empirically investigating public expectations of information provided by CI operators during crisis situations. It does so by drawing on key themes that emerged from a literature review on public expectations of disaster related information shared via social media, and presenting the results of an online questionnaire-based study of disaster-vulnerable communities in France, Norway, Portugal and Sweden. Results indicate that members of the public expect CI operators to provide disaster related information via traditional and social media and to respond to their queries on social media. CI operators should avail of the opportunities provided by social media to provide real-time information to disaster affected communities.

Information Systems (IS) increasingly are used in Emergency Management (EM), so it is prudent to include IS study in EM education. This paper presents the results of analyzing the responses to a survey that proposed potential courses for programs at the master’s level. The survey was completed by 373 practitioners, academics and/or researchers with EM experience. All proposed courses were rated above 4 on a 7-point scale for how essential they are to the curriculum. However, there were disagreements. Qualitative analysis of volunteered comments indicate that some low ratings were due to disagreement with the content of the course as described, or with the need for an entire course to cover the topic. An unexpected finding was that a substantial number of respondents spontaneously expressed opposition to the use of IS for EM in general. The findings are discussed and a preliminary curriculum is proposed.

Timely information is essential for better dynamic situational awareness, which leads to efficient resource planning, coordination, and action. However, given the scale and outreach of social media—a key information sharing platform during crises, diverse types of users participate in discussions during crises, which affect the vetting of information for dynamic situational awareness and response coordination activities. In this paper, we present a user analysis on Twitter during crises for three major user types—Organization, Organization-affiliated (a person’s self-identifying affiliation with an organization in his/her profile), and Non-affiliated (person not identifying any affiliation), by first classifying users and then presenting their communication patterns during two recent crises. Our analysis shows distinctive patterns of the three user types for participation and communication on social media during crises. Such a user-centric approach to study information sharing during crisis events can act as a precursor to deeper domain-driven content analysis for response agencies.

Emergency response scenarios are characterised by the participation of multiple agencies, which cooperate to control the situation and restore normality. These agencies can come from diverse areas of expertise which entails that they represent knowledge differently, using their own vocabularies and terminologies. This fact complicates the automation of the information-sharing process, creating problems such as ambiguity or specialisation. In this paper, we present an approach to tackle these problems by domain-aware semantic matching. This method requires the formalisation of domain-specific terminologies which will be added to an existing system oriented to emergency response. Concretely, we have formalised terms from the UK Civil and Protection Terminology lexicon, which gathers some of the most common terms that UK agencies use in these scenarios.

In public services, there is a trend to increasingly utilize collaborations with non-professional volunteers for certain tasks, one example being emergency response. In many of these collaborations, information technology (IT) is an essential tool, and inadequate IT support can have far-reaching consequences—including even the loss of lives. Since a volunteer is a different type of actor, and may have different technical requirements, compared to professionals, there is a need to explore how collaborations between professionals and volunteers can be successfully developed. This paper is based on a case study of the Enhanced Neighbor project, which uses volunteers as first responders in emergency response. The study highlights important factors to consider when involving volunteers, including how IT can foster the collaboration, and the volunteers’ needs for IT support.

The use of social media is gaining more and more in importance in ordinary life, but also in conflicts and emergencies. The social big data, generated by users, is partially also used as a source for situation assessment, e.g. to receive pictures or to assess the general mood. However, the information’s believability is hard to control and can deceive. Rumors, fake news and social bots are phenomenons that challenge the easy consumption of social media. To address this, our paper explores the believability of content in social media. Based on foundations of information quality we conducted a literature study to derive a three-level model for assessing believability. It summarizes existing assessment approaches, assessment criteria and related measures. On this basis, we describe several steps towards the development of an assessment approach that works across different types of social media.

Resilience has become a concept extensively used by several areas of knowledge. However, there is no method, widely recognized capable of measuring resilient behavior. Therefore, this exploratory work aims to present a set of dimensions capable of delimiting metrics in order to measure a resilient attitude. For that, we analyzed some scientific papers considered relevant by the academic community. In this work we follow an analytical flow seeking to conceptualize resilience and situate it in the context of complex systems. This analysis allowed the discovery of some characteristics regarding resilience, fundamental for the proposition of the presented dimensions. Finally, we present a conceptual map that brings together the elicited dimensions.

The French Blood Establishment (EFS) is the only one in France allowed dealing with the blood supply chain. The EFS centers are scattered in France and blood products are transported over hundreds of kilometers, from collection sites to hubs and from the hubs to distribution sites. The strong constraints on lifetime and storage conditions imply a traceability of transportation steps very accurate, which is necessary in case of unexpected and unwanted events. To make “good” decisions, we propose in this research work to use the Physical Internet philosophy. Thanks to sensors in containers and Complex Event Processing modules to aggregate and filter the data collected, we would be able to create a real time “field model”. In case of crisis this model would be helpful and perfectly adapted to help the crisis unit to make “good” decisions and maybe propose solutions based on the past events.

The larger the scale, scope, and duration of a disaster, the higher is the number of response units. However, with more units involved in the response also the heterogeneity of responder units drastically increases in terms of capabilities, experiences, practices, techniques, tactics, and procedures. As a result, the coordination and overall management of the response becomes an increasingly challenging endeavor. In the response to the 2014 Oso/SR530 landslide disaster in Washington State over one hundred agencies were involved, which presented a huge coordination task for the incident command. This empirical study is exploratory and focuses on the activities and interactions of professional responders, particularly, in the early phases of the response. It amends and complements previous studies on the subject by identifying and describing in detail various challenges in the early response. It also discusses recommendations on how to tackle and potentially mitigate the challenges identified in future responses.

Aid workers increasingly face risks when working in crisis regions. In order to improve effectiveness and safety of humanitarians, it is of great importance to provide a well thought out real-time socio-technical support. Thus, new policies and innovative technological solutions need to be developed and integrated into humanitarian workflows. For the requirements elicitation process to realize this aspiration, we employ a board game approach that confronts players with situations aid workers experience in the field. From the first game session, we learned that the game is a valuable tool. It raises awareness to important challenges and trade-offs that humanitarians face. In addition, it is an effective catalyst for initiating a discussion on which system requirements are needed. Future work will include an update of the board game as well as sessions with the target group of practitioners to inform the development of a socio-technical system for humanitarian aid work.

The Smart Mature Resilience Project is a European research Project aimed at developing a Resilience Management Guideline. The guideline is composed of five tools developed in the project. They help to determine the level of resilience of a city and how to improve it.

The Resilience Information Portal, one of these five tools, must support and integrate the different tools. It is also used as an engagement and communication tool.

The tool had to be built before the finalization of the requirements, so a double approach was pursued in this project. First, a fast prototype was built based on a CMS, and second, a solution was built from scratch. The paper explains this process, compare both approaches, and how the long way can be more efficient. The final version has shown to be easy to use, and powerful enough to respond to the requirements of the users of the portal.

Disasters and emergencies bring uncertain situations. People involved in such situations look for quick answers to their rapid queries. Moreover, humanitarian organizations look for situational awareness information to launch relief operations. Existing studies show the usefulness of social media content during crisis situations. However, despite advances in computational techniques (natural language processing etc.) access to relevant information is still a challenging task. In this work-in-progress paper, we propose a novel approach to provide timely access to the relevant information on Twitter. Specifically, based on users queries, we use query expansion (using word2vec embedding) and relevance feedback mechanism to search for relevant messages on Twitter. Initial experiments and user studies performed using a real disaster Twitter data show the significance of the proposed approach.

Collaboration is the foundation to strengthen disaster preparedness and for effective emergency response actions at all levels. Some studies have highlighted that remote volunteers, i.e., volunteers supported by Web 2.0 technologies, possess the potential to strengthen humanitarian relief organizations by offering information regarding disaster-affected people and infrastructure. Although studies have explored various aspects of this topic, none of those provided an overview of the state-of-the-art of researches on the collaboration among humanitarian organizations and communities of remote volunteers. With the aim of overcoming this gap, a systematic literature review was conducted on the existing research works. Therefore, the main contribution of this work lies in examining the state of research in this field and in identifying potential research gaps. The results show that most of the selected studies are concerned with the general domain of disaster management, and the domain of humanitarian logistic remains poorly investigated.

Crowdsourcing is used to enlist workers as a resource for a variety of applications, including disaster response. However, simple tasks such as image labeling often feel monotonous and lead to worker disengagement. This provides a challenge for designing successful crowdsourcing systems. Existing research in the design of work indicates that task variety is a key factor in worker motivation. Therefore, we asked Amazon Mechanical Turk workers to complete a series of disaster response related subtasks, consisting of either image labeling or locating photographed areas on a map. We varied the frequency at which workers encountered the different subtask types, and found that switching subtask type at different frequencies impacted measures of worker engagement. This indicates that a certain amount of variety in subtasks may engage crowdsourcing workers better than uniform subtask types.

Crowdsourcing is an increasingly popular approach for processing data in response to disasters. While volunteer crowdsourcing may suffice for high-profile disasters, paid crowdsourcing may be necessary to recruit workers for less prominent events. Thus, understanding the impact of payment schemes on worker behavior and motivation may improve outcomes. In this work, we paid workers a fixed amount to provide a minimum number of image ratings, allowing them to voluntarily provide more if desired. This allowed us to examine the impact of different amounts of _required work_. We found that requiring _no_ ratings resulted in workers completing _more_ work, and being more likely to indicate motivation related to interest on a post survey, than when small numbers of ratings were required. This is consistent with the motivational _crowding-out_ effect, even in paid crowdsourcing. We additionally found that providing feedback on progress positively impacted the amount of work done.

This paper approaches the problem of monitoring the impact of the disasters by mining web sources for the events, caused by these disasters. We refer to these disaster effects as “micro-events”. Micro-events typically following a large disaster include casualties, damage on infrastructures, vehicles, services and resource supply, as well as relief operations. We present natural language grammar learning algorithms which form the basis for building micro-event detection systems from data, with no or minor human intervention, and we show how they can be applied to mainstream news and social media for monitoring disaster impact. We also experimented with applying statistical classifiers to distill, from social media situational updates on disasters, eyewitness reports from directly affected people. Finally, we describe a Twitter mining robot, which integrates some of these monitoring techniques and is intended to serve as a multilingual content hub for enhancing situational awareness.

Understanding the movements of a population throughout the 24-hour day is critical when directing disaster response in an urban area. An emergency situation can develop rapidly, and understanding the expected locations of groups of people is required for the success of first responders.

Recent advances in modern consumer technologies have facilitated the generation, sharing and mining of an extensive amount of volunteered geographic information. Users leverage inexpensive smart devices, pervasive Internet connections and social media services to provide data about geospatial locations. Using an enterprise system, it is possible to aggregate this freely available, geospatially enabled data and create a population estimation with high spatiotemporal resolution, via a heat map.

This investigation explores the effects of different temporal sampling periods when creating such estimations. Time periods are selected, estimations are generated for several large urban areas in the western United States, and comparisons of the results are shown/discussed.

Every day the Fire department of the Netherlands work hard to save people’s lives. Therefore, they have been investing in Business Intelligence approaches for several years, to get more information for accident prevention and accident fighting. In this paper Linked Open Data has been used as a business intelligence approach for the creation of dwelling fire risk profiles based on demographic data. During the research a Proof of Concept showed the appliance of Linked Open Data for this purpose. However the data have some quality mismatches, such as: outdated, accuracy issues and not 100% completed. From evaluation sessions it turned out that the outcomes show similarities with a fire incident map and the gut feeling of several firefighters.

In response to the threat of hazardous gas releases to public safety and health, we propose an agile framework for detecting and quantifying gas emission sources. Emerging techniques like high-precision gas sensors, source term estimation algorithms and Unmanned Aerial Vehicles are incorporated. The framework takes advantage of both stationary sensor network method and mobile sensing approach for the detection and quantification of hazardous gases from fugitive, accidental or deliberate releases. Preliminary results on street-level detection of urban natural gas leakage is presented. Source term estimation is demonstrated through a synthetic test case, and is verified using Cramér–Rao bound analysis.

Disaster resilience is gaining increasing attention from both industry and academia, but difficulties in operationalizing the concept remain, especially in the urban context. Currently, there is scant literature on measuring both spatial and temporal aspects of resilience empirically. We propose a bio-inspired quantitative framework to track urban resilience to disasters. This framework was built upon a daily human mobility network, which was generated by geolocations from a Twitter Streaming API. System-wide metrics were computed over time (i.e. pre-, during and post-disasters). Fisher information was further adopted to detect the perturbation and dynamics in the system. Specifically, we applied the proposed approach in a flood case in the metropolis of São Paulo. The proposed approach is efficient in uncovering the dynamics in human movements and the underlying spatial structure. It adds to our understanding of the resilience process in urban disasters.

Decision making in the area of humanitarian logistics and supply chain management often suffers because of the interrelations between planning horizons, tasks, and crisis management lifecycle phases. In this paper, we present a method, an exemplary prototypical implementation and its evaluation within a relief organization. Based on a structured literature analysis, a review of existing information systems as well on a consideration of ongoing research projects, basic requirements for an integrated logistics and transport planning approach were derived. Together with end-user involvement, these results were used to design and prototype a concept of an appropriate information system, which was applied and evaluated in a tabletop exercise. The generated results are promising in terms of having a positive impact on the logistics effectiveness. In combination with the identified limitations, our results promise to have an impact on future ISCRAM research.

Crisis response organizations and military units must be agile and able to adapt to dynamic situations. The ability to adapt includes command and control agility, organizational adaptability and individual adaptability. An exploratory study of these adaptability traits were undertaken during a naval exercise. The exercise scenario was designed to progressively become more challenging, suggesting decreased performance and adaptability over time. The study objectives were to develop a data collection approach for adaptability traits and to investigate association between these traits and performance. Data collected from four command teams were evaluated in terms of response rate and item variation. Principal component analysis was used to explore latent structures and relationships. The results indicate acceptable survey response rates and trends showing a decrease in organizational adaptability and C2 agility over time while individual adaptability increased. Data indicate five latent components. A hypothetical model of the structure between these components are suggested.

The ubiquity of social media facilitates widespread participation in crises. As individuals converge online to understand a developing situation, rumors can emerge. Currently little is known about how online rumoring behavior varies by language. Exploring a rumor from the 2015 Paris Attacks, we investigate Twitter rumoring behaviors across two languages: French, the primary language of the affected population; and English, the dominant language of Internet communication. We utilize mixed methods to qualitatively code and quantitatively analyze rumoring behaviors across French and English language tweets. Temporal engagement in the rumor varies across languages, but proportions of tweets affirming and denying a rumor are very similar. Analyzing tweet deletions and retweet counts, we find slight (but not significant) differences between languages. This work offers insight into potential limitations of previous research of online rumoring, which often focused exclusively on English language content, and demonstrates the importance of considering language in future work.

In this paper, we propose sociomaterialism as a theoretical lens for studying virtual team management during organizational crisis. In applying this lens, we propose the use of pattern theory as the method of choice for documenting effective practices for managing virtual teams in organizational crisis settings.

Citizens engaging in crisis management spontaneously and without affiliation to an (honorary) aid organization are a social phenomenon on the rise. Even though public engagement is desirable, it receives mixed reactions by crisis management experts. They claim that “the crowd” has to be managed to ensure a successful crisis response and recovery, leading to high coordination efforts which cannot be achieved by the authorities. To better understand the obstacles in cooperation and to overcome them, this study examines existing patterns of cooperation. The study employed in-depth interviews in two use cases (n=13; flooding, n=4; migrant crisis, n=9) with public authorities, aid organizations and engaged citizens. Results indicate that collaboration works successful when an intermediary organization bridges the coordination gap between the authorities and the public. In addition to the concept of intermediary organizations, two ICT approaches supporting collaboration in crisis events are described: Public Displays and the so-called ‘Security Arena’.

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.

This paper presents an overview of ongoing research into the implementation of an agent-based model aimed at providing decision support for the layout design of lecture theatres and human behavioural management in emergency evacuation. The model enables the spatial layout of lecture theatres to be configured and incorporates agent behaviours at the basic movement and individual level. In terms of individual behaviours, agents can be competitive, cooperative, climb obstacles (e.g. seating and desks) and fall down. Two cases are investigated to evaluate the effects of different exit locations in lecture theatres and competitive behaviour of agents on evacuation efficiency in multiple scenarios.

Earthquakes can cause significant disruption and devastation to populations of communities. Thus, in the event of an earthquake, it is necessary to have the right number of disaster shelters, with the appropriate capacity, in the right location in order to accommodate local communities. Mathematical models, allied with suitable optimization algorithms, have been used to determine the locations at which to construct disaster shelters and allocate the population to them. This paper compares the use of two optimization algorithms, namely a genetic algorithm and a modified particle swarm optimization, both of which have advantages and disadvantages when solving the disaster shelter location-allocation problem.

As part of a movement towards enabling smart cities, a growing number of urban areas in the USA, such as New York City, Boston, and Houston, have established 311 call centers to receive service requests from their citizens through a variety of platforms. In this paper, for the first time, we propose to leverage the large amount of data provided by these non-emergency service centers to help characterize their operational performance in the context of a natural disaster event. We subsequently develop a metric based on the number of open service requests, which can serve as the basis for comparing the relative performance of different departments across different disasters and in different geographic locations within a given urban area. We then test the applicability and usefulness of the approach using service request data collected from New York City’s 311 service center.

In complex crises, coordination between organizations is challenging. Knowledge needed to coordinate, like responsibilities, capabilities and interdependencies between tasks are often not known or not communicated systematically. As a result, coordination develops gradually and causes confusion. In this paper we describe an approach and tool called ‘Profiler’, that focuses on quickly increasing knowledge and understanding about the participating organizations while preparing for, or at the beginning of a crisis. Profiler was evaluated during an exercise of 1 Civil Military Coordination Battalion (1CIMICbat). Teams consisting of functional specialists performed a damage and needs assessments after a flooding. The results show that participants that used Profiler increased their knowledge and integrated understanding, when this was initially lacking. Further, participants with improved knowledge and integrated understanding, coordinated more within and between teams, when they perceived to be interdependent. Results and theory suggest that coordination effectiveness and efficiency can be improved with our approach.

Critical infrastructures become more and more entangled and rely extensively on information technology. A deeper insight into the relationships between critical infrastructures enables the actors involved to more quickly understand the severity of information technology disruptions and to identify robust cross-functional mitigating actions. This study illustrates how and why disruptions in the payment system in Sweden could create cascading effects in other critical infrastructures with potentially severe consequences for many citizens, government institutions and companies. Data from document studies, interviews and workshops with field experts reveal seven challenges for collective cross-functional critical infrastructure resilience that need to be dealt with: 1) Shortage of food, fuel, cash, medicine; 2) Limited capacity of alternative payment solutions; 3) Cities are more vulnerable than the countryside; 4) Economically vulnerable groups in society are more severely affected; 5) Trust maintenance needs; 6) Crisis communication needs; 7) Fragmentation of responsibility for critical infrastructures across many actors.

All too often the collection as well as analysis of data for humanitarian response only starts once a disaster hits. This paper proposes a framework to assess Data Preparedness on five dimensions: Data Sets, Data Services and Tooling, Data Governance, Data Literacy, and Networked Organizations for Data. We demonstrate for one dimension, i.e. Data Sets, how it can be quantified. First step is to determine which Data Sets must be collected before a disaster strikes so that as many as possible decision-makers’ information needs are covered. Subsequently, a Data Sets Preparedness Index can be calculated based on Completeness, Recency and Accuracy & Reliability. We tested the index for Malawi and The Philippines and show how it can be used to direct data collection and determine when data analysis for e.g. predicting severity becomes meaningful. The index can be modified for reporting on global policies such as the Sustainable Development Goals.