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Elina Ramsell, Tobias Andersson Granberg, & Sofie Pilemalm. (2019). Identifying functions for smartphone based applications in volunteer emergency response. 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: Emergency response organisations struggle with resource constraints and thereby faces challenges in providing
high-quality public services. Utilising voluntary first responders is one way to address these challenges. There
are different types of volunteers who can help at an emergency site, e.g. citizen volunteers or voluntary
professionals from other occupations. To successfully engage with and utilise these resources, adequate
information and communication technology (ICT) is necessary. In this meta-study, combining and further
exploring two previous studies, the aim is to identify, analyse and evaluate suitable functions for smartphone
applications that can be used to dispatch and support volunteers. The results show that the functions can be
divided into essential ones that are necessary for the response to work at all, and others that might contribute to a
more effective response. The study also shows that the same functions can be used for different volunteer
groups.
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Eric Daudé, Kevin Chapuis, Clément Caron, Alexis Drogoul, Benoit Gaudou, Sebastien Rey-Coyrehourq, et al. (2019). ESCAPE: Exploring by Simulation Cities Awareness on Population Evacuation. 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: Partial or total horizontal evacuation of populations in urban areas is an important protection measure against a natural or technological risk. However, casualties during massive displacement in a context of stress and in a potentially degraded environment may be high due to non-compliance with instructions, accidents, traffic jams, incivilities, lack of preparation of civil security or increased exposure to hazards. Working in evacuation plans is therefore fundamental in avoiding casualties caused by improvisation and in promoting self-evacuation whenever possible. Since it is impossible to re-create the conditions of a crisis on the ground to assess such evacuation plans, there is a need for realistic models in order to evaluate them using simulations. In this paper, we present the ESCAPE software framework that helps in the development of such plans and testing them. In particular, ESCAPE, which uses the GAMA open-source platform as a core component, provides an agent-based simulation tool that supports simulation of the evacuation of a city's population at fine temporal and Geographical scales. The framework was developed such that it works for a wide range of scenarios, both in terms of hazards, geographical configurations, individual behaviors and crisis management. In order to show its adaptability, two applications are presented, one concerning the evacuation of the city of Rouen (France) in the context of a technological hazard and the other pertaining to the evacuation of the district of Hanoi (Vietnam) in the event of floods.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Frâncila Weidt Neiva, & Marcos R. S. Borges. (2019). Sharing Gut Feelings to Support Collaborative 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: Expertise-based intuition plays a key role in decision making, especially in complex environments as those
involved with crisis and emergency domains where decisions often need to be made on the basis of dynamic,
incomplete, and/ or contradictory information. In such environments, a deliberative analysis is often impossible
or inefficient. Using teams to make collaborative decisions in complex environments can bring benefits to
organizations, but the complexity of supporting this scenario also increases. The present work proposes a
solution based on graphs to support the sharing of the intuition rationale in teams aiming at an accelerated
expertise. The development of the proposal is part of a methodological context of design science research. In
this paper we report the execution of one of the expected cycles that explores the use of generated artifacts in
practice that then produced insights for the proposed computational support.
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Gabriela C Barrera, & Maria C Yang. (2019). Evaluation of Digital Volunteers using a Design Approach: Motivations and Contributions in Disaster Response. 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: With the growth of social media and crowdsourcing in disaster response, further research is needed on the motivations
and contributions of digital volunteers. This study applies a user-centered design approach to understanding how we
might make better tools to support digital volunteers. This user-centered design approach involves stated preference
elicitation methods through an online survey to understand what digital volunteers want in such tools. Through
choice-based conjoint analysis, we contribute to mixed-methods research to gain additional insight into motivations
and user preferences for a set of design features that might be incorporated into an online tool specifically for digital
volunteers. Initial results show preferences for measures of success that were not monetary, which aligned with
directly stated motivations for volunteering. Our findings corroborate with previous research in that feedback to
volunteers is very important, as well as being able to measure the impact of their work.
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Guillaume Lambert, Bruno Fontaine, Michel Monneret, & Mourad Madani. (2019). How to build an innovative C2 system supporting individual-centric emergency needs ? 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 paper describes the need for, and work in progress to provide the French population with
a modern emergency communication infrastructure that uses open source components to
deliver real time communications from smart phones as well as traditional routes.
The article puts forward the vision of the NexSIS 18-112 project aimed at designing and
implementing the next generation AI enhanced emergency services response platform for
France. The vision and ambition of the NexSIS 18-112 system is to rewrite the command and
control system from scratch at a national level, providing it with state of the art functionalities.
Anticipating the future deployment of 5G networks, the work described in the article explains
how to ensure the transition of the legacy emergency operation systems to an operational IPbased
model, capable of offering voice, video, Instant Messaging, and Real Time Text (RTT)
services to emergency services? operators.
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Guoqin Ma, & Chittayong Surakitbanharn. (2019). Predicting Hurricane Damage Using Social Media Posts Coupled with Physical and Socio-Economic Variables. 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: During a natural disaster or emergency event, individual social media posts or hot spots may not necessarily correlate
to the most devastated areas. To better understand the correlation between social media and physical damage, we
compare Tweets, data about the physical environment, and socio-economic factors with insurance claim information
(as a proxy for physical damage) from 2017 Hurricane Irma in the state of Florida. We use machine learning
to identify relevant Tweets, sensitivity analyses to identify socio-economic factors, and statistical regression to
determine the predictive capability of insurance claims as a proxy for damage. We find that Tweets alone result in a
poorly fitted regression model of insurance claims, but the inclusion of physical features (e.g., power outages, wind
level) and socio-economic factors (e.g., population density, education, Internet access) improves the model?s fit.
Such models contribute to the knowledge base that may allow social media to predict damage in real-time.
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Hans C.A. Wienen, Faiza A. Bukhsh, Eelco Vriezekolk, & Roel J. Wieringa. (2019). Applying Generic AcciMap to a DDOS Attack on a Western-European Telecom Operator. 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: After a large incident on a telecommunications network, the operator typically executes an incident analysis to
prevent future incidents. Research suggests that these analyses are done ad hoc, without a structured approach. In
this paper, we conduct an investigation of a large incident according to the AcciMap method. We find that this
method can be applied to telecommunications networks with a few small changes; we find that such a structured
approach yields many more actionable recommendations than a more focused approach and we find that both the
onset of an incident and the resolution phase merit their own analysis. We also find that such an analysis costs a
lot of effort and we propose a more efficient approach to using this method. An unexpected outcome was that
AcciMap may also be very useful for analyzing crisis organizations.
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Haya Aldossary, & Graham Coates. (2019). A Preliminary Optimisation-based Approach to Coordinate the Response of Ambulances in Mass Casualty Incidents. 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: Mass Casualty Incidents (MCIs) may occur with no notice and require a rapid response to manage the casualties and arrange their transportation to hospitals. MCIs may result in different numbers of casualties and fatalities. Further, response time can play a crucial role in reducing fatalities and protecting lives. This paper reports on a preliminary optimisation-based approach, termed MCIER, which has been developed to co-ordinate the response of ambulances to multiple MCIs. In this approach, a realistic representation of the road network is modelled for the geographical area of interest. Also, a Neighbourhood Search Algorithm (NSA) has been developed in order to find the optimum solution to the problem under consideration. A hypothetical case study of a MCI in Newcastle-upon-Tyne has been considered to investigate the effect on response time of the time of day, and day of week, on which the incident occurs.
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Hoang Nam Ho, Mourad Rabah, Ronan Champagnat, & Frédéric Bretrand. (2019). Towards an Automatic Assistance in Crisis Resolution with Process Mining. 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: To deal with a crisis situation, experts must undertake a chain of activities, called process, to minimize crisis
consequences. To assist the expert in making decision in crisis resolutions, authors propose a method aiming at
discovering crisis response processes. This method is based on a two-step strategy: the first step classifies the
system?s traces, representing stakeholders? past actions, into different sets, where each one represents a set of
response processes according to a specific context; the second step uses process mining algorithm to discover
the corresponding response plan process model based on the obtained chain of activities for each previously
classified context. These response plans will be a referenced aid for experts while making crisis resolution,
according to each context. The proposed approach is illustrated on the traces issued from the crisis caused by the
2010 Xynthia storm in France.
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Humaira Waqas, & Muhammad Imran. (2019). #CampFireMissing: An Analysis of Tweets About Missing and Found People From California Wildfires. 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: Several research studies have shown the importance of social media data for humanitarian aid. Among others,
the issue of missing and lost people during disasters and emergencies is crucial for disaster managers. This work
analyzes Twitter data from a recent wildfire event to determine its usefulness for the mitigation of the missing and
found people issue. Data analysis performed using various filtering techniques, and trend analysis revealed that
Twitter contains important information potentially useful for emergency managers and volunteers to tackle this
issue. Many tweets were found containing full names, partial names, location information, and other vital clues
which could be useful for finding missing people.
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Humasak Simanjuntak, & Fabio Ciravegna. (2019). Semantic Understanding of Human Mobility Lifestyle to support Crisis 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: In this paper, we propose a method for understanding the semantics of mobility (mainly related to lifestyle)
patterns based on stay point detection from tracking data. The method identifies the context (trip purpose and
visited point of interest) of tracking data by using large-scale data collection infrastructure. We evaluate our
method with a tracking dataset in Birmingham (European project SETA) generated by 534 users from
September 2017 to September 2018. To this end, we compare insights from the tracking data with check-in
mobility in social media. The results show that both data capture rich human lifestyle features related to the
visited point of interest. Our study provides solid evidence that lifestyle patterns from tracking and social media
data can indeed be useful for understanding and gauging the level of disruption after a crisis, as it is possible to
check the deviation of habits from normal conditions and post-crisis.
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Ingo J. Timm, Bernhard Hess, & Fabian Lorig. (2019). Data Acquisition for ad-hoc Evacuation Simulations of Public Buildings. 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: Crowd simulation is suitable to evaluate evacuation strategies but its validity strongly depends on the quality of input
data. The acquisition of adequate input data is particularly challenging when simulating the evacuation of public
buildings such as universities. As they are publicly accessible, the exact number of persons on site is unknown.
Yet, to investigate specific emergency situations by means of simulation, e.g. amok or fire, information is required
about distribution and amount of people within the building at a specific point of time. Due to data privacy, public
buildings do not implement access control. However, data artifacts are available in various information systems,
e.g., wifi data, room administration. Our hypothesis is, that the acquisition and fusion of such data artifacts is
sufficient to enable data-based ad-hoc simulation of evacuation scenarios as decision support for the operations
management. To this end, we introduce a procedure for the situation-dependent collection fusion of simulation
input data. Furthermore, a case study is provided to demonstrate the feasibility of the approach.
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Jennifer Lisa Chan, Gabriel Nam, Allison G. Marshall, & Hemant Purohit. (2019). Trends in Humanitarian Health Information during 2010 Haiti Earthquake: Motivation for Curating Domain Knowledge Base. 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: Health response plays a major role during disasters and information management plays a crucial role in situational awareness to adapt to evolving needs. Health organizations exchange information often through narrative-based documents called situation reports. Although situation reports are widely shared, they are an increasingly challenging information source from which to infer knowledge for situational awareness. This paper analyzed health information from traditional health reports using mixed methods during the aftermath of the 2010 Haiti Earthquake and provides insights into the patterns of what?s being said, how it?s being said, and trends over time. Opportunities lie ahead to analyze narrative documents at scale by combining human knowledge from qualitative coding with machine intelligence. In addition, developing unifying health domain ontologies representing diverse humanitarian health concepts will advance computational techniques to improve
the efficiency and accuracy of retrieving knowledge for improved situational awareness and potential decision
making during humanitarian health response.
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Jens Kersten, Anna Kruspe, Matti Wiegmann, & Friederike Klan. (2019). Robust filtering of crisis-related tweets. 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: Social media enables fast information exchange and status reporting during crises. Filtering is usually required to
identify the small fraction of social media stream data related to events. Since deep learning has recently shown to
be a reliable approach for filtering and analyzing Twitter messages, a Convolutional Neural Network is examined for
filtering crisis-related tweets in this work. The goal is to understand how to obtain accurate and robust filtering
models and how model accuracies tend to behave in case of new events. In contrast to other works, the application
to real data streams is also investigated. Motivated by the observation that machine learning model accuracies
highly depend on the used data, a new comprehensive and balanced compilation of existing data sets is proposed.
Experimental results with this data set provide valuable insights. Preliminary results from filtering a data stream
recorded during hurricane Florence in September 2018 confirm our results.
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Jess Kropczynski, Rob Grace, Shane Halse, Doina Caragea, Cornelia Caragea, & Andrea Tapia. (2019). Refining a Coding Scheme to Identify Actionable Information on Social Media. 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 describes the use of a previously established qualitative coding scheme developed through a design workshop with public safety professionals, and applied the schema to social media data collecting during crises. The intention of applying this scheme to existing crisis datasets was to acquire training data for machine learning. Applying the coding scheme to social media data revealed that additional subcategories of the coding scheme are necessary to satisfy information requirements necessary to dispatch first responders to an incident. The coding scheme was refined and adapted into a set of instructions for qualitative coders on Amazon Mechanical Turk. The contribution of this work is a coding scheme that is more directly related to the information needs of public safety professionals. Implications of early results using the refined coding scheme are discussed in terms of proposed automated methods to identify actionable information for dispatch of first responders during emergency incidents.
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Jo Erskine Hannay, & Yelte Kikke. (2019). Structured crisis training with mixed reality simulations. 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: We argue that current technology for crisis training does not explicitly cater well enough for managing training
objectives and skill building metrics throughout the lifespan of training. We suggest how successful crisis training
may be enabled by interoperating next-generation exercise management tools with mixed-reality simulations. We
propose an architecture consisting of (1) a front-end in which training objectives, essential skills, corresponding
events and metrics can be declared, (2) a back-end consisting of simulations that implement the events and metrics
and (3) a middleware which transfers information between the front-end and back-end to enable semi-automatic
composition of the simulations and performance analysis. The purpose of this architecture is to facilitate learning
through the principles of deliberate practice. We indicate where emerging technologies are necessary to achieve this.
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Joao Moreira, Luis Ferreira Pires, & Marten Sinderen. (2019). SEMIoTICS: Semantic Model-Driven Development for IoT Interoperability of Emergency 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: Modern early warning systems (EWSs) use Internet-of-Things (IoT) technologies to realize real-time data acquisition, risk detection and message brokering between data sources and warnings? destinations. Interoperability is crucial for effective EWSs, enabling the integration of components and the interworking with other EWSs. IoT technologies potentially improve the EWS efficiency and effectiveness, but this potential can only be exploited if interoperability challenges are properly addressed. The three main challenges for interoperability are: (1) achieving semantic integration of a variety of data sources and different representations; (2) supporting time- and safety-critical applications with performance and scalability; and (3) providing data analysis for effective responses with personalized information requirements. In this paper, we describe the ?SEmantic Model-driven development for IoT Interoperability of emergenCy serviceS? (SEMIoTICS) framework, which supports the development of semantic interoperable IoT EWSs. The framework has been validated with a pilot performed with accident use cases at the port of Valencia. The validation results show that it fulfils the requirements that we derived from the challenges above.
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