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Øyvind Hanssen. (2021). Improving Trails from GPS Trackers with Unreliable and Limited Communication Channels. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 489–502). Blacksburg, VA (USA): Virginia Tech.
Abstract: In this document we explore position tracking in the context of land based search and rescue operations, where we also may have a limited and unreliable communication channel. This is the case when using APRS (amateur radio tracking) in voluntary SAR services in Norway. We have looked more closely into trails of movements and how to plot these on the map to present informative real-time pictures to the incident commanders. A simple scheme is proposed to improve trails by piggybacking positions at the end of regular transmissions.Experiments show that a significant amount of positions are recovered. In some cases this can recover useful information, though it depends on the actual situation.
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Zou, H. P., Caragea, C., Zhou, Y., & Caragea, D. (2023). Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 385–395). Omaha, USA: University of Nebraska at Omaha.
Abstract: The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in disaster events. To address this challenge, we present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting where only a small number of annotated data is required. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data, mimicking the early stage of a disaster. Through integrating effective semi-supervised learning ideas and incorporating TextMixUp, CrisisMatch achieves performance improvement on two disaster datasets of 11.2% on average. Further analyses are also provided for the influence of the number of labeled data and out-of-domain results.
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Zoha Sheikh, Hira Masood, Sharifullah Khan, & Muhammad Imran. (2017). User-Assisted Information Extraction from Twitter During Emergencies. 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. 684–691). Albi, France: Iscram.
Abstract: 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 information retrieval and text processing techniques, access to relevant information on Twitter is still a challenging task. In this paper, we propose a novel approach to provide timely access to the relevant information on Twitter. Specifically, we employee Word2vec embeddings to expand initial users queries and based on a relevance feedback mechanism we retrieve relevant messages on Twitter in real-time. Initial experiments and user studies performed using a real world disaster dataset show the significance of the proposed approach.
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Zijun Long, & Richard Mccreadie. (2021). Automated Crisis Content Categorization for COVID-19 Tweet Streams. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 667–678). Blacksburg, VA (USA): Virginia Tech.
Abstract: Social media platforms, like Twitter, are increasingly used by billions of people internationally to share information. As such, these platforms contain vast volumes of real-time multimedia content about the world, which could be invaluable for a range of tasks such as incident tracking, damage estimation during disasters, insurance risk estimation, and more. By mining this real-time data, there are substantial economic benefits, as well as opportunities to save lives. Currently, the COVID-19 pandemic is attacking societies at an unprecedented speed and scale, forming an important use-case for social media analysis. However, the amount of information during such crisis events is vast and information normally exists in unstructured and multiple formats, making manual analysis very time consuming. Hence, in this paper, we examine how to extract valuable information from tweets related to COVID-19 automatically. For 12 geographical locations, we experiment with supervised approaches for labelling tweets into 7 crisis categories, as well as investigated automatic priority estimation, using both classical and deep learned approaches. Through evaluation using the TREC-IS 2020 COVID-19 datasets, we demonstrated that effective automatic labelling for this task is possible with an average of 61% F1 performance across crisis categories, while also analysing key factors that affect model performance and model generalizability across locations.
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Zijun Long, & Richard McCreadie. (2022). Is Multi-Modal Data Key for Crisis Content Categorization on Social Media? In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 1068–1080). Tarbes, France.
Abstract: The user-base of social media platforms, like Twitter, has grown dramatically around the world over the last decade. As people post everything they experience on social media, large volumes of valuable multimedia content are being recorded online, which can be analysed to help for a range of tasks. Here we specifically focus on crisis response. The majority of prior works in this space focus on using machine learning to categorize single-modality content (e.g. text of the posts, or images shared), with few works jointly utilizing multiple modalities. Hence, in this paper, we examine to what extent integrating multiple modalities is important for crisis content categorization. In particular, we design a pipeline for multi-modal learning that fuses textual and visual inputs, leverages both, and then classifies that content based on the specified task. Through evaluation using the CrisisMMD dataset, we demonstrate that effective automatic labelling for this task is possible, with an average of 88.31% F1 performance across two significant tasks (relevance and humanitarian category classification). while also analysing cases that unimodal models and multi-modal models success and fail.
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Zewei Zhang, Hongyong Yuan, & Lida Huang. (2018). Study on the Utility of Emergency Map in Emergency Response. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 377–387). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: As modern cities expand rapidly, the loss of emergency has been more serious. To reduce or even avoid losses caused by disasters, using emergency maps to collect, aggregate, analyze, and communicate information is a prerequisite for efficient response. In this paper, we analyzed the impact factors of information transfer efficiency, and constructed the communication model provided by Emergency Map. By comparing the difference with case deduction between the traditional communication mode in emergency response and the new communication mode based on Emergency Map, which is called Group Communication Mode. We proved the Group Communication Mode had the advantages to improve information transfer efficiency in emergency response. Emergency Map can be an effective tool for the timely transfer of information among departments, which put forward a novel communication mode in emergency decision-making process.
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Zeno Franco, Katinka Hooyer, Tanvir Roushan, Casey O'Brien, Nadiyah Johnson, Bill Watson, et al. (2018). Detecting & Visualizing Crisis Events in Human Systems: an mHealth Approach with High Risk Veterans. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 874–885). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Designing mHealth applications for mental health interventions has largely focused on education and patient self-management. Next generation applications must take on more complex tasks, including sensor-based detection of crisis events, search for individualized early warning signs, and support for crisis intervention. This project examines approaches to integrating multiple worn sensors to detect mental health crisis events in US military veterans. Our work has highlighted several practical and theoretical problems with applying technology to evaluation crises in human system, which are often subtle and difficult to detect, as compared to technological or natural crisis events. Humans often do not recognize when they are in crisis and under-report crises to prevent reputational damage. The current project explores preliminary use of the E4 Empatica wristband to characterize acute aggression using a combination of veteran self-report data on anger, professional actors simulating aggressive events, and preliminary efforts to discriminate between crisis data and early warning sign data.
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Zeno Franco, José J. González, & José H. Canós. (2019). Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management (Z. Franco, J. J. González, & J. H. Canós, Eds.). Valencia, Spain: Iscram.
Abstract: The theme of ISCRAM 2019 is Towards individual-centric emergency management
systems. This edition wishes to highlight the particular needs of the individual
stakeholder in Crisis and Emergency Management and to stimulate discussions that
enable the design of individual-centric crisis and emergency management systems.
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Zeno Franco, Chris Davis, Adina Kalet, Michelle Horng, Johnathan Horng, Christian Hernandez, et al. (2021). Augmenting Google Sheets to Improvise Community COVID-19 Mask Distribution. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 359–375). Blacksburg, VA (USA): Virginia Tech.
Abstract: Face mask scarcity in the United States hindered early infection control efforts during the COVID-19 pandemic. Areas with a history of racial segregation and poverty experienced differential COVID-19 death and morbidity rates. Supplying masks equitably and rapidly became an urgent public health priority. A partnership between a local manufacturer with available polypropylene fabric and the Medical College of Wisconsin, which had the capability to assemble and distribute masks, was formed in April, 2020. An improvised logistics framework allowed for rapid distribution more than 250,000 masks, and later facilitated hand-off to other organizations to distribute over 3 million masks. Using an action research framework three phases of the effort are considered, 1) initial deliveries to community clinics, 2) equitable distribution to community agencies while under “safer at home” orders, and 3) depot deliveries and transfer of logistics management as larger agencies recovered. A multi-actor view was used to interrogate the information needs of faculty and staff remotely directing distribution, medical student volunteers delivering masks, and the manufacturer monitorng overall inventory. Logistics information was managed using Google Sheets augmented with a small SQLite component. A phenomenological view, toggling back and forth from the “socio” to the “technical” provides detailed insight into the strengths and limitations of digital solutions for humanitarian logistics, highlighting where paper-based processes remain more efficient. This case study suggests that rather than building bespoke logistics software, supporting relief efforts with non-traditional responders may benefit from extensible components that augment widely used digital tools.
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Zeleskidis, A., Chalarampidou, S., Dokas, I. M., & Torra, F. (2023). COBOT Safety Awareness: A RealTSL Demonstration in a Simulated System. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 874–891). Omaha, USA: University of Nebraska at Omaha.
Abstract: This work aims to propose the RealTSL methodology to empower collaborative robotic systems with self-safety awareness capability and address the methodology's limitation in determining time ranges for the unsafe system state transitions, which are inputs of the methodology. The COBOT system used in this paper to demonstrate RealTSL is an automated scissor lift robot to be used by first responders for “work at height,” simulated in Simulink™. The demonstration begins by 1) applying STPA to the system, 2) applying Early Warning Sign Analysis based on STAMP (EWaSAP), 3) creating an acyclic diagram that depicts system state transitions towards unsafe states, 4) incorporating the appropriate sensory equipment in the simulation, 5) simulating the system's operation for different scenarios using fault injection and finally 6) use information from the simulations to complete the RealTSL analysis and calculate the safety level of the system in real-time during its simulated operation.
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Zelenka, J., Kasanický, T. š, Gatial, E., Balogh, Z., Majlingová, A., Brodrechtova, Y., et al. (2023). Coordination of Drones Swarm for Wildfires Monitoring. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 144–151). Omaha, USA: University of Nebraska at Omaha.
Abstract: As a result of climate change and global weather patterns, large forest fires are becoming more frequent in different parts of the world. The focus of the presented work is on creation of a complex coordination and communication framework for a swarm of drones specially tailored for use in preventing and monitoring of forest fires. The presented algorithm has been testing and evaluating using a computer simulation. The testing and validation in relevant environment is scheduled during a pilot demonstration exercise with real personnel and equipment, which will take place in Slovakia on April 2023. The presented work is a part of the SILVANUS EU H2020 project, whose objective is the creation of a climate resilient forest management platform for forest fire prevention and suppression. SILVANUS draws on environmental, technical, and social science experts to support regional and national authorities responsible for forest fire management in their respective countries.
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Završnik, J., Vošner, H. B. žun, & Kokol, P. (2023). Pandemic crisis management: The EU project STAMINA. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (p. 1070). Omaha, USA: University of Nebraska at Omaha.
Abstract: Pandemics, as COVID-19 showed, can have the potential to result in serious global health threats and crises. Management of such kind of crisis presents a serious challenge due to the number of affected people, differences in legal, administrative, health procedures, political cultures, and the lack of smart interconnected, and compatible digitalized software tolls. The aim of the STAMINA project, sponsored by EU, was to overcome the above challenges and support efficient and effective pandemic management by providing Artificial intelligence-based decision-support technology which could successfully operate at a regional, national, and global level. The project targeted three stages of the emergency management cycle: Prediction, Preparedness, and Response. The STAMINA solution provides national planners, regional crisis management agencies, first responders, and citizens with new tools as well as a clear guide to how they can be used in line with international standards and legislation.
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Zainab Akhtar, Ferda Ofli, & Muhammad Imran. (2021). Towards Using Remote Sensing and Social Media Data for Flood Mapping. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 536–551). Blacksburg, VA (USA): Virginia Tech.
Abstract: Ghana's capital, the Greater Accra Metropolitan Area (GAMA) is most vulnerable to flooding due to its high population density. This paper proposes the fusion of satellite imagery, social media, and geospatial data to derive near real-time (NRT) flood maps to understand human activity during a disaster and the extent of infrastructure damage. To that end, the paper presents an automatic thresholding technique for NRT flood mapping using Sentinel-1 images where four different speckle filters are compared using the VV, VH and VV/VH polarization to determine the best polarization(s) for delineating flood extents. The VV and VH bands together on Perona-Malik filtered images achieved the highest accuracy with an F1-score of 81.6%. Moreover, all tweet text and images were found to be located in flooded regions or in very close proximity to a flooded region, thus allowing crisis responders to better understand vulnerable communities and what humanitarian action is required.
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Zachary Sutherby, & Brian Tomaszewski. (2018). Conceptualizing the Role Geographic Information Capacity has on Quantifying Ecosystem Services under the Framework of Ecological Disaster Risk Reduction (EcoDRR). In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 326–333). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: The use of ecosystems for EcoDRR is a beneficial and a viable option for community stakeholders. For example, ecosystems can mitigate the effects of hazards experienced in anthropogenic communities. Ecosystem services are the underlying reason for this benefit. EcoDRR is the idea of sustainable management, conservation, and restoration of ecosystems to maximize ecosystem services and reduce disaster risks and impacts. The use of geospatial technologies to monitor large-scale ecosystems are often subject to Geographic Information Capacity (GIC), or the ability of ecosystem stakeholders to utilize all existing geographic information, resources, and capacities to monitor ecosystem services. Though these tools are useful, currently there is not a tool that specifically quantifies ecosystem services in the context of DRR. The main contribution of this paper is a conceptual framework intended to quantify ecosystem services in the context of EcoDRR.
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Yuya Shibuya, & Hideyuki Tanaka. (2019). Detecting Disaster Recovery Activities via Social Media Communication Topics. 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: Enhancing situational awareness by mining social media has been widely studied, but little work has been done
focusing on recovery phases. To provide evidence to support the possibility of harnessing social media as a sensor
of recovery activities, we examine the correlations between topic frequencies on Twitter and people?s socioeconomic
recovery activities as reflected in the excess demand for used cars and housing, after the Great East
Japan Earthquake and Tsunami of 2011. Our research suggests that people in the disaster-stricken area
communicated more about recovery and disaster damages when they needed to purchase used cars, while the nonlocal
population communicated more about going to and supporting the disaster-stricken area. On the other hand,
regarding the excess demand for housing, when the local population of the disaster-stricken area started to resettle,
they communicated their opinions more than in other periods about disaster-related situations.
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Yudi Chen, Angel Umana, Chaowei Yang, & Wenying Ji. (2021). Condition Sensing for Electricity Infrastructures in Disasters by Mining Public Topics from Social Media. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 598–608). Blacksburg, VA (USA): Virginia Tech.
Abstract: Timely and reliable sensing of infrastructure conditions is critical in disaster management for planning effective infrastructure restorations. Social media, a near real-time information source, has been widely used in the disaster domain for building timely, general situational awareness, such as urgent public needs and donations. However, the employment of social media for sensing electricity infrastructure conditions has yet been explored. This study aims to address the research gap to sense electricity infrastructure conditions through mining public topics from social media. To achieve this purpose, we proposed a systematic and customized approach wherein (1) electricity-related social media data is extracted by the classifier developed based on Bidirectional Encoder Representations from Transformers (BERT); and (2) public topics are modeled with unigrams, bigrams, and trigrams to incorporate the formulaic expressions of infrastructure conditions in social media. Electricity infrastructures in Florida impacted by Hurricane Irma are studied for illustration and demonstration. Results show that the proposed approach is capable of sensing the temporal evolutions and geographic differences of electricity infrastructure conditions.
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Yu, X., Chen, J., & Liu, J. (2023). Examining the influence of social media on individual’s protective action taking during Covid-19 in China. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 295–308). Omaha, USA: University of Nebraska at Omaha.
Abstract: In the context of COVID-19, this study utilizes the Social Mediated Crisis Communication Model (SMCC) and the Protective Action Decision Model (PADM) to investigate the relationship between social media users' protective actions and crisis information during public health crises in China. By constructing a structural equation model, this study aims to identify the influencing factors that affect social media users' personal’s cognitive, emotional, and behavioral reactions given crisis relevant information. Results findings are that warning information can significantly increase risk perception; emotional responses are not significantly affected by warning information and risk perception; risk perception has a negative impact on information gathering and sharing behavior; risk perception has a significant mediating effect on the relationship between information features and protective action.
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Yossi Nygate, William Johnson, Mark Indelicato, Miguel Bazdresch, & Clark Hochgraf. (2018). Intelligent Wireless Infrastructure Management for Emergency Communications. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1156–1160). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: This poster describes the research of a collaborative faculty-led research that will enable first responders to identify and visualize geo-located quality of service and coverage gaps in wireless and deployable networks during an emergency event and support the deployment additional LTE base stations within FirstNet to augment network coverage and capacity. Our crowd sourced cellular metrics system uses big data analytics to detect changes in coverage and usage patterns and recommends where to deploy additional communication assets. The approach uses machine learning methods to measure and model coverage gaps and automatically implement bandwidth prioritization on whatever communication assets are available.
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Yoshiki Ogawa, Yuki Akiyama, & Ryosuke Shibasaki. (2017). Extraction of significant scenarios for earthquake damage estimation using sparse modeling. 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. 150–163). Albi, France: Iscram.
Abstract: The recent diversification and accumulation of data from GPS equipped mobile phones, building sensors, and other resources in Japan has caused a large increase in the number of earthquake disaster scenarios that can be identified. Disaster prevention planning requires us to contemplate which scenario should be focused on and the required response to various scenarios. As a means to solve this problem, the damage distribution of building collapse and fire from GPS data can be used to estimate future damage based on people flow and various hypocenter models of earthquakes. We propose a method that uses sparse modeling to extract scenarios that are important for disaster estimation and prevention. As a result, this paper makes it possible to quickly grasp the scenario distribution, which was previously impossible to do, and to extract the significant scenarios.
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Yohann Chasseray, Anne-Marie Barthe-Delanoë, Stéphane Négny, & Jean-Marc Le Lann. (2021). Automated unsupervised ontology population system applied to crisis management domain. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 968–981). Blacksburg, VA (USA): Virginia Tech.
Abstract: As crisis are complex systems, providing an accurate response to an ongoing crisis is not possible without ensuring situational awareness. The ongoing works around knowledge management and ontologies provide relevant and machine readable structures towards situational awareness and context understanding. Many metamodels, that can be derived into ontologies, supporting the collect and organization of crucial information for Decision Support Systems have been designed and are now used on specific cases. The next challenge into crisis management is to provide tools that can process an automated population of these metamodels/ontologies. The aim of this paper is to present a strategy to extract concept-instance relations in order to feed crisis management ontologies. The presented system is based on a previously proposed generic metamodel for information extraction and is applied in this paper to three different case studies representing three different crisis namely Ebola sanitarian crisis, Fukushima nuclear crisis and Hurricane Katrina natural disaster.
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Ylenia Casali, Nazli Yonca Aydin, & Tina Comes. (2021). Zooming into Socio-economic Inequalities: Using Urban Analytics to Track Vulnerabilities – A Case Study of Helsinki. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 1028–1041). Blacksburg, VA (USA): Virginia Tech.
Abstract: The Covid19 crisis has highlighted once more that socio-economic inequalities are a main driver of vulnerability. Especially in densely populated urban areas, however, these inequalities can drastically change even within neighbourhoods. To better prepare for urban crises, more granular techniques are needed to assess these vulnerabilities, and identify the main drivers that exacerbate inequality. Machine learning techniques enable us to extract this information from spatially geo-located datasets. In this paper, we present a prototypical study on how Principal Component Analysis (PCA) to analyse the distribution of labour and residential characteristics in the urban area of Helsinki, Finland. The main goals are twofold: 1) identify patterns of socio-economic activities, and 2) study spatial inequalities. Our analyses use a grid of 250x250 meters that covers the whole city of Helsinki, thereby providing a higher granularity than the neighbourhood-scale. The study yields four main findings. First, the descriptive statistical analysis detects inequalities in the labour and residential distributions. Second, relationships between the socio-economic variables exist in the geographic space. Third, the first two Principal Components (PCs) can extract most of the information about the socio-economic dataset. Fourth, the spatial analyses of the PCs identify differences between the Eastern and Western areas of Helsinki, which persist since the 1990s. Future studies will include further datasets related to the distribution of urban services and socio-technical indicators.
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Yitong Li, Duoduo Liao, Jundong Li, & Wenying Ji. (2021). Automated Generation of Disaster Response Networks through Information Extraction. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 431–438). Blacksburg, VA (USA): Virginia Tech.
Abstract: Following a disaster, maintaining and restoring community lifelines require collective efforts from various stakeholders. Aiming at reducing the efforts associated with generating stakeholder collaboration networks (SCNs), this research proposes a systematic approach for reliably extracting stakeholder collaboration information from texts and automatically generating SCNs. In the proposed approach, stakeholders and their interactions are automatically extracted from texts through a natural language processing technique--Named Entity Recognition. Once extracted, the collaboration information is stored into structured datasets to automate the generation of SCNs. A case study on stakeholder collaboration in response to Hurricane Harvey is used to demonstrate the feasibility and applicability of the proposed approach. Overall, the proposed approach achieves the reliable and automated generation of SCNs from texts, which largely reduces practitioners' interpretation loads and eases the data collection process.
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Yingjie Li, Seoyeon Park, Cornelia Caragea, Doina Caragea, & Andrea Tapia. (2019). Sympathy Detection in Disaster Twitter Data. 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: Nowadays, micro-blogging sites such as Twitter have become powerful tools for communicating with others in
various situations. Especially in disaster events, these sites can be the best platforms for seeking or providing social
support, of which informational support and emotional support are the most important types. Sympathy, a sub-type
of emotional support, is an expression of one?s compassion or sorrow for a difficult situation that another person
is facing. Providing sympathy to people affected by a disaster can help change people?s emotional states from
negative to positive emotions, and hence, help them feel better. Moreover, detecting sympathy contents in Twitter
can potentially be used for finding candidate donors since the emotion ?sympathy? is closely related to people who
may be willing to donate. Thus, in this paper, as a starting point, we focus on detecting sympathy-related tweets.
We address this task using Convolutional Neural Networks (CNNs) with refined word embeddings. Specifically, we
propose a refined word embedding technique in terms of various pre-trained word vector models and show great
performance of CNNs that use these refined embeddings in the sympathy tweet classification task. We also report
experimental results showing that the CNNs with the refined word embeddings outperform not only traditional
machine learning techniques, such as Naïve Bayes, Support Vector Machines and AdaBoost with conventional
feature sets as bags of words, but also Long Short-Term Memory Networks.
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Yasir Imtiaz Syed, Raj Prasanna, S Uma, Kristin Stock, & Denise Blake. (2018). A Design Science based Simulation Framework for Critical Infrastructure Interdependency. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 516–524). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: Critical Infrastructures (CI) such as electricity, water, fuel, telecommunication and road networks are a crucial factor for secure and reliable operation of a society. In a normal situation, most of the businesses operate on an individual infrastructure. However, after major natural disasters such as earthquakes, the conflicts and complex interdependencies among the different infrastructures can cause significant disturbances because a failure can propagate from one infrastructure to another. This paper discusses the development of an integrated simulation framework that models interdependencies between electricity and road infrastructure networks of Wellington region. The framework uses a damage map of electricity network components and integrates them with road access time to the damaged components for determining electricity outage time of a region. The results can be used for recovery planning, identification of vulnerabilities, and adding or discarding redundancies in an infrastructure network.
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Yasas Senarath, Jennifer Chan, Hemant Purohit, & Ozlem Uzuner. (2021). Evaluating the Relevance of UMLS Knowledge Base for Public Health Informatics during Disasters. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 97–105). Blacksburg, VA (USA): Virginia Tech.
Abstract: During disasters public health organizations increasingly face challenges in acquiring and transforming real-time data into knowledge about the dynamic public health needs. Resources on the internet can provide valuable information for extracting knowledge that can help improve decisions which will ultimately result in targeted and efficient health services. Digital content such as online articles, blogs, and social media are some of such information sources that could be leveraged to improve the health care systems during disasters. To efficiently and accurately identify relevant disaster health information, extraction tools require a common vocabulary that is aligned to the health domain so that the knowledge from these unstructured digital sources can be accurately structured and organized. In this paper, we study the degree to which the Unified Medical Language System (UMLS) contains relevant disaster, public health, and medical concepts for which public health information in disaster domain could be extracted from digital sources.
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