|
|
Erion Elmasllari. (2019). Design and development methods for improving acceptance of IT among emergency responders. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Various sources report a low adoption of IT-based tools in emergency response, as well as a negative attitude of
responders to such tools. The responders? needs, simply put, are not met by the IT-based tools offered to them.
Observing this situation through a user-centered design lens, we note that such problems typically stem from
insufficient or erroneous context analysis. The deficiencies become even more pronounced when considering that
emergency response represents a complex, adaptive socio-technical system. We also note that the appropriate
methodology for designing ER systems is rarely discussed in literature and in research papers. To fill that void, the
present paper discusses a minimal set of techniques that, both in our experience and according to state of the art
practice, can guide developers towards positively-accepted IT systems for emergency response.
|
|
|
|
Erion Elmasllari. (2018). Why IT systems for emergency response get rejected: examining responders' attitude to IT. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 994–1002). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Emergency responders' attitude to IT is marked by resistance, aversity, and rejection. This does not extend to technology in general and is specific to IT alone. Current research on the topic only presents partial, scattered, and unconnected accounts that do not provide a starting point on how to tackle this attitude. The available models for technology acceptance are also generic and do not take into account the specifics of the emergency response domain. Through extensive user research combined with a grounded theory approach, this paper identifies twelve problem areas from which responders' negative attitude towards IT arises. By extending the technology acceptance models with this new knowledge, we provide system designers with an understanding of what to tackle and tune in their IT system designs so that a positive attitude among emergency responders can be achieved.
|
|
|
|
Erion Elmasllari, & René Reiners. (2017). Learning From Non-Acceptance: Design Dimensions for User Acceptance of E-Triage Systems. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 798–813). Albi, France: Iscram.
Abstract: 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 laboratory; 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 BRIDGE , 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.
|
|
|
|
Esteban Bopp, Johnny Douvinet, & Damien Serre. (2019). Sorting the good from the bad smartphone application to alert residents in case of disasters – Experiments in France. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: The number of smartphone applications to alert and inform the population in a risk situation in France is too large
and these solutions are still unknow by the population. This study proposes an evaluation protocol based on various
indicators, which take into account the capacity of the applications to send a targeted alert, their attractiveness, the
ability of individuals to emit information and number of hazards considered. The results obtained on 50
applications deployed in France show that very few of them meet the objectives of the alert, in the sense defined
by civil security, because of a single-risk approach, a unique sense of communication, and the low acceptance of
these solutions by citizens.
|
|
|
|
Eulalia Gomez Martin, Josune Hernantes, Leire Labaka, & Marcos Borges. (2022). Building upon the Existing Knowledge: Updating and Improving the Smart Mature Resilience Model. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 437–459). Tarbes, France.
Abstract: In recent years the concept of urban resilience has acquired great relevance within urban planning. The complexity of urban systems and the wide scope of the resilience concept require tools to facilitate the integration of the concept in urban development. Numerous studies, tools, and theoretical frameworks have been developed to support the resilient transformation of cities. However, these initiatives are usually not holistically integrated and limit incorporating the changes and advances in the resilience concept. This article highlights the importance of shifting from a continuously-building-new approach to building on an existing knowledge approach. This study has updated and improved the maturity model developed within the Horizon 2020 project Smart Mature Resilience. A bibliometric analysis was carried out to study the developments in resilience over the past four years and to integrate the relevant advances in the area into a new version of the Smart Mature Resilience Maturity Model (SMR MM).
|
|
|
|
Eva Petitdemange, Elyes Lamine, Franck Fontanili, & Matthieu Lauras. (2020). Enhancing Emergency Call Centers' Performance Through a Data-driven Simulation Approach. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 218–227). Blacksburg, VA (USA): Virginia Tech.
Abstract: Emergency Call Centers (ECCs) can be considered as the starting point of the pre-hospital emergency medical system. Although, ECCs exist everywhere, their business processes and their performance levels differ from one place to another, even sometimes in a same country. By definition, users expect a high level of performance, particularly regarding the waiting time and the processing time of the calls. Additionally, ECCs might have difficulties to manage sudden rise of activities following disasters impacting huge number of victims for instance. To support ECCs in their continuous improvement steps, this paper suggests an innovative framework and its associated tools to support both diagnosis of current organizations and enhancement of their performance. Concretely, the proposal is data-driven and simulation oriented. First experiments are shown in order to demonstrate the potential benefits of such an approach. Avenues for further research are also discussed.
|
|
|
|
Fabio Ciravegna, Jerry Gao, Chris Ingram, Neil Ireson, Vita Lanfranchi, & Humasak Simanjuntak. (2018). Mapping Mobility to Support Crisis Management. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 305–316). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: In this paper we describe a method and an infrastructure for rapid mapping of mobility patterns, based on a combination of a mobile mobility tracker, a large-scale data collection infrastructure, and a data and visual analytics tool. The combination of the three enables mapping everyday mobility patterns for decision makers, e.g. city council, motorways authorities, etc. and can support emergency responders in improving their preparedness and the recovery in the aftermath of a crisis. The technology is currently employed over very large scale: (i) in England it is used by a public body to incentivise physical mobility (400,000 app downloads and hundreds of millions of data point since September 2017); (ii) in Sheffield UK, through the MoveMore initiative, tracking active mobility of users (5,000 downloads); and (iii) the European project SETA, to track multimodal mobility patterns in three cities (Birmingham, Santander and Turin).
|
|
|
|
Fatehkia, M., Imran, M., & Weber, I. (2023). Towards Real-time Remote Social Sensing via Targeted Advertising. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 396–406). Omaha, USA: University of Nebraska at Omaha.
Abstract: Social media serves as an important communication channel for people affected by crises, creating a data source for emergency responders wanting to improve situational awareness. In particular, social listening on Twitter has been widely used for real-time analysis of crisis-related messages. This approach, however, is often hindered by the small fraction of (hyper-)localized content and by the inability to explicitly ask affected populations about aspects with the most operational value. Here, we explore a new form of social media data collected through targeted poll ads on Facebook. Using geo-targeted ads during flood events in six countries, we show that it is possible to collect thousands of poll responses within hours of launching the ad campaign, and at a cost of a few (US dollar) cents per response. We believe that this flexible, fast, and affordable data collection can serve as a valuable complement to existing approaches.
|
|
|
|
Fatemeh Hendijani Fard, Cooper Davies, & Frank Mauer. (2017). Agile Emergency Responses Using Collaborative Planning HTN. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 857–867). Albi, France: Iscram.
Abstract: 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. To the best of our knowledge, there is a gap that there is no common knowledge base for provincial level mass emergencies for automatic planners. 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.
|
|
|
|
Federico Angaramo, & Claudio Rossi. (2018). Online clustering and classification for real-time event detection in Twitter. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1098–1107). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Event detection from social media is a challenging task due to the volume, the velocity and the variety of user-generated data requiring real-time processing. Despite recent works on this subject, a generalized and scalable approach that could be applied across languages and topics has not been consolidated, yet. In this paper, we propose a methodology for real-time event detection from Twitter data that allows users to select a topic of interest by defining a simple set of keywords and a matching rule. We implement the proposed methodology and evaluate it with real data to detect different types of events.
|
|
|
|
Fedor Vitiugin, & Carlos Castillo. (2019). Comparison of Social Media in English and Russian During Emergencies and Mass Convergence Events. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Twitter is used for spreading information during crisis events. In this paper, we first retrieve event-related information
posted in English and Russian during six disasters and sports events that received wide media coverage in both
languages, using an adaptive information filtering method for automating the collection of about 100 000 messages.
We then compare the contents of these messages in terms of 17 informational and linguistic features using a
difference in differences approach. Our results suggest that posts in each language are focused on different types
of information. For instance, almost 50% of the popular people mentioned in these messages appear exclusively
in either the English messages or the Russian messages, but not both. Our results also suggest differences in the
adoption of platform mechanics during crises between Russian-speaking and English-speaking users. This has
important implications for data collection during crises, which is almost always focused on a single language.
|
|
|
|
Femke Mulder, & Kees Boersma. (2017). Linking up the last mile: how humanitarian power relations shape community e-resilience. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 715–725). Albi, France: Iscram.
Abstract: 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 case study based power analysis, we show that when civic technology initiatives do strategically engage with these dynamics, they have the potential to alter harmful power relations that limit community e-resilience.
|
|
|
|
Ferda Ofli, Firoj Alam, & Muhammad Imran. (2020). Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 802–811). Blacksburg, VA (USA): Virginia Tech.
Abstract: Multimedia content in social media platforms provides significant information during disaster events. The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or found people, among others. Although many studies have shown the usefulness of both text and image content for disaster response purposes, the research has been mostly focused on analyzing only the text modality in the past. In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques. Specifically, we utilize convolutional neural networks to define a multimodal deep learning architecture with a modality-agnostic shared representation. Extensive experiments on real-world disaster datasets show that the proposed multimodal architecture yields better performance than models trained using a single modality (e.g., either text or image).
|
|
|
|
Fiona Jennet McNeill, Diana Bental, Jeremy Bryan, Paolo Missier, Jannetta S. Steyn, & Tom Kumar. (2019). Communication in Emergency Management through Data Integration and Trust: an introduction to the CEM-DIT system. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: This paper discusses the development of the CEM-DIT (Communication in Emergency Management through Data
Integration and Trust) system, which allows decision makers in crises to send out automated data requests to multiple
heterogeneous and potentially unknown sources and interactively determine how reliable, relevant and trustworthy
the responses are. We describe the underlying technology, which is based partially on data integration and matching,
and partly on utilisation of provenance data. We describe our cooperation with the Urban Observatory (UO), which
allows us to develop the system in collaboration with developers of the kind of crisis-relevant data which the system
is designed for. The system is currently in development, and we describe which parts are fully implemented and
which are currently being developed.
|
|
|
|
Fiona Jennet McNeill, Diana Bental, Jeremy Bryans, Paolo Missier, & Jannetta Steyn. (2018). Informing decision makers: facilitating communication and trust for decision makers during crises. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1133–1135). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: This paper describes our approach to facilitating automated data sharing during a crisis management scenario. There are a number of reasons why this is difficult, of which we are addressing two of the main ones. Firstly, data in different organisations (and organisations) is mismatched in that different terminology, structure, specificity and data formats are used, so automated comprehension of data is problematic. Secondly, is that it is hard to assess the trustworthiness of data from other organisations. We have developed data-matching and provenance-based solutions to these problems individually. In this paper, we discuss how best these approaches can be integrated so that decision makers can quickly and automatically be presented with data to match, or approximately match, their data needs, together with the right information for them to understand the quality and meaning of this data, and introduce the CEM-DIT (Communication for Emergency Management through Data Integration and Trust) system.
|
|
|
|
Firoj Alam, Ferda Ofli, & Muhammad Imran. (2019). CrisisDPS: Crisis Data Processing Services. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Over the last few years, extensive research has been conducted to develop technologies to support humanitarian aid
tasks. However, many technologies are still limited as they require both manual and automatic approaches, and
more importantly, are not ready to be integrated into the disaster response workflows. To tackle this limitation, we
develop automatic data processing services that are freely and publicly available, and made to be simple, efficient,
and accessible to non-experts. Our services take textual messages (e.g., tweets, Facebook posts, SMS) as input to
determine (i) which disaster type the message belongs to, (ii) whether it is informative or not, and (iii) what type of
humanitarian information it conveys. We built our services upon machine learning classifiers that are obtained from
large-scale comparative experiments utilizing both classical and deep learning algorithms. Our services outperform
state-of-the-art publicly available tools in terms of classification accuracy.
|
|
|
|
Firoj Alam, Ferda Ofli, Muhammad Imran, & Michael Aupetit. (2018). A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 553–572). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management.
|
|
|
|
Flavio Dusse, Renato Novais, & Manoel Mendonça. (2020). A Visual Analytics Based Model for Crisis Management Decision-Making. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 157–166). Blacksburg, VA (USA): Virginia Tech.
Abstract: Crisis Management (CM) refers to the ability to deal with crisis tasks in different phases and iterations. People working in a crisis are generally under pressure to make the right decision at the right time. They must process large amounts of data and assimilate the received information in an intuitive way. Visual Analytics (VA) is potentially useful to analyze and understand the huge amount of data in several areas including in a crisis. We propose a model based on VA to support decision-making in CM. The aim of the model is to help visualization designers to create effective VA interfaces, to help crisis managers to make quick and assertive decisions with them. In previous studies, we carried out a survey protocol with a multi-method approach to collect data on crisis related decision-making and analyze all these data qualitatively with formal techniques during the large events held in Brazil in recent years. In this work, we used our previous findings to develop the proposed model. We validated it using the focus group technique. With the new findings, we identified relevant insights on the use of VA for crisis management. We hope that, with these continuous cycles of validation and improvement, the agencies that manage crises might use our model as a reference for building more effective IT decision-making infrastructures based on VA.
|
|
|
|
Flavio Dusse, Renato Novais, & Manoel Mendonça. (2019). Understanding the Main Themes Towards a Visual Analytics Based Model for Crisis Management Decision-Making. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: Crisis Management (CM) refers to the ability to deal with crisis tasks in different phases and iterations. People working in a crisis are generally under stress to make the right decision at the right time. They have to process large amounts of data and to assimilate the received information in an intuitive and visual way. Visual Analytics (VA) is potentially useful to analyze and understand the huge amount of data in several areas including in a crisis. We designed a survey protocol to understand which themes influence visualizations to support CM. In previous work, we carried out systematic mapping studies, analysis of official documents, ethnographic studies, questionnaires during the large events held in Brazil in recent years. In this work, we interviewed eight CM specialists. We analyzed this data qualitatively with the coding technique. Then we evaluated the coding results with the focus group technique. With the results, we identified the relationships between the visual needs and other main themes of influence for CM. This thematic synthesis enabled us to build a draft model based on VA.
We hope that, after future cycles of validations and improvements, the agencies that manage crises might use this model as a reference in their activities of knowledge production and decision-making.
|
|
|
|
Flavio Dusse, Renato Novais, & Manoel Mendonça. (2018). Investigating the Use of Visual Analytics to Support Decision-Making in Crisis Management: A Multi-Method Approach. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 83–98). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Like Crisis Management (CM) itself, Visual Analytics (VA) is a multi-disciplinary research area and is potentially useful to analyze and understand the huge amount of multidimensional data produced in a crisis. Our work investigates how researchers and practitioners are using VA in decision-making in CM. This paper firstly reports on a systematic mapping study to analyze the available information visualization tools and their applications in CM. To complement this information, we report on questionnaires and ethnographic studies applied during the large events held in Brazil in recent years. Then, we analyzed existing tools for visualizing crisis information. Lastly, we analyzed the data gathered from interviews with six professional crisis managers. The compiled results show that the full potential of VA is not being applied in the state-of-the-art and state-of-the-practice. We consider that further researches in the application of VA is required to improve decision-making processes in crisis management.
|
|
|
|
Flavio Horita, Ricardo Vilela, Renata Martins, Danielle Bressiani, Gilca Palma, & João Porto de Albuquerque. (2018). Determining flooded areas using crowd sensing data and weather radar precipitation: a case study in Brazil. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1040–1050). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Crowd sensing data (also known as crowdsourcing) are of great significance to support flood risk management. With the growing volume of available data in the past few years, researchers have used in situ sensor data to filter and prioritize volunteers' information. Nevertheless, stationary, in situ sensors are only capable of monitoring a limited region, and this could hamper proper decision-making. This study investigates the use of weather radar precipitation to support the processing of crowd sensing data with the goal of improving situation awareness in a disaster and early warnings (e.g., floods). Results from a case study carried out in the city of São Paulo, Brazil, demonstrate that weather radar data are able to validate flooded areas identified from clusters of crowd sensing data. In this manner, crowd sensing and weather radar data together can not only help engage citizens, but also generate high-quality data at finer spatial and temporal resolutions to improve the decision-making related to weather-related disaster events.
|
|
|
|
Florent Castagnino. (2019). What can we learn from a crisis management exercise ? Trusting social media in a french firefighters' department. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: This paper sets out the methodology and the temporary results of an ongoing research project on the use of social media in crisis management (in France). It discusses the benefits and limits to use an emergency crisis exercise for research purposes. It describes an observation protocol and a coding method that could be replicate to survey further exercises. Some possible processing of the observation data is exposed, and further visualizations of the data are still in progress. One of the first analytical results tackles the way Var?s firefighters consider social media information. For now, social media seem to be regarded as questionable because they do not easily fit into the organizational routine. At the same time, the awareness of the need to use social media is quite strong. On the analytical level, the paper tries to use sociological concepts to describe and explain some results.
|
|
|
|
Florent Dubois, Paul Renaud-Goud, & Patricia Stolf. (2022). Dynamic Capacitated Vehicle Routing Problem for Flash Flood Victim’s Relief Operations. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 68–86). Tarbes, France.
Abstract: Flooding relief operations are Dynamic Vehicle Routing Problems (DVRPs). The problem of people evacuation is addressed and formalized in this paper. Characteristics of this DVRP problem applied to the crisis management context and to the requirements of the rescue teams are explained. In this paper, several heuristics are developed and assessed in terms of performance. Two heuristics are presented and adapted to the dynamic problem in a re-optimization approach. An insertion heuristic that inserts demands in the existing plan is also proposed. The evaluation is conducted on various dynamic scenarios with characteristics based on a study case. It reveals better performances for the heuristics with a re-optimization approach.
|
|
|
|
Florian Vandecasteele, Krishna Kumar, Kenzo Milleville, & Steven Verstockt. (2019). Video Summarization And Video Highlight Selection Tools To Facilitate Fire Incident Management. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: This paper reports on the added value of combining different types of sensor data and geographic information for fire incident management. A survey was launched within the Belgian fire community to explore the need of added value and the use of new types of sensor data during a fire incident. This evaluation revealed that people are visually-oriented and that video footages and images are of great value to gain insights in a particular problem. However, due to the limited available time (i.e., fast decisions need to be taken) and the large amount of cameras it is not feasible to analyze all video footages sequentially. To solve this problem we propose a video summarization mechanism and a video highlight selection tool based on the automatic generated image and video tags.
|
|
|
|
Francesca Comunello, & Simone Mulargia. (2017). A #cultural_change is needed. Social media use in emergency communication by Italian local level institutions. In eds Aurélie Montarnal Matthieu Lauras Chihab Hanachi F. B. Tina Comes (Ed.), Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management (pp. 512–521). Albi, France: Iscram.
Abstract: 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 (respondents were selected based on their involvement in emergency communication and/or emergency management processes). 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. Two main attitudes emerge both in the narrative style and in social media representations: some respondents seem to adopt an institutional attitude, while others adopt a practical-professional attitude. 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.
|
|
