Soudip Roy Chowdhury, Muhammad Imran, Muhammad Rizwan Asghar, Amer-Yahia, S., & Carlos Castillo. (2013). Tweet4act: Using incident-specific profiles for classifying crisis-related messages. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 834–839). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: We present Tweet4act, a system to detect and classify crisis-related messages communicated over a microblogging platform. Our system relies on extracting content features from each message. These features and the use of an incident-specific dictionary allow us to determine the period type of an incident that each message belongs to. The period types are: Pre-incident (messages talking about prevention, mitigation, and preparedness), during-incident (messages sent while the incident is taking place), and post-incident (messages related to the response, recovery, and reconstruction). We show that our detection method can effectively identify incident-related messages with high precision and recall, and that our incident-period classification method outperforms standard machine learning classification methods.
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Derya Ipek Eroglu, Duygu Pamukcu, Laura Szczyrba, & Yang Zhang. (2020). Analyzing and Contextualizing Social Vulnerability to Natural Disasters in Puerto Rico. 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. 389–395). Blacksburg, VA (USA): Virginia Tech.
Abstract: As the third hurricane the U.S. experienced in 2017, Hurricane María generated impacts that resulted in both short term and long term suffering in Puerto Rico. In this study, we aim to quantify the vulnerability of Puerto Ricans by taking region and society specific characteristics of the island into account. To do this, we follow Cutter et al.'s social vulnerability calculation, which is an inductive approach that aims to represent a society based on its characteristics. We adapted the Social Vulnerability Index (SoVI) for Puerto Rico by using data obtained from the U.S. Census Bureau. We analyzed the newly calculated SoVI for Puerto Rico and compared it with the existing deductive approach developed by the Center for Disease Control (CDC). Our findings show that the new index is able to capture some characteristics that the existing vulnerability index is unable to do.
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Eva Blomqvist, Vitaveska Lanfranchi, Suvodeep Mazumdar, Tomi Kauppinen, & Carsten Kessler. (2015). Workshop summary: Workshop on Semantics and Analytics for Emergency Response (SAFE2015). In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: The Emergency Response domain is a highly challenging domain, requiring the active collaboration of several experts and authorities on the one hand and large-scale data analysis on the other. This poses significant challenges in sharing and analysing highly dynamic data describing highly evolving situations. This paper provides a brief summary for the first workshop in the SAFE workshop series. The workshop is aimed at bringing together analysts, practitioners, researchers and enthusiasts and provides a discussion ground for practical problems, solutions and projects that exploit Semantic Web, Linked Data analytics for Emergency Response. Following a round of thorough reviews, four papers are accepted and a keynote will complement the paper presentations along with a few discussion sessions.
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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).
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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.
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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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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.
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Huse, L., Schwedhelm, M., & Steinecker, H. (2023). Improving Visibility for Proactive Tactics in Emerging Situations. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1078–1079). Omaha, USA: University of Nebraska at Omaha.
Abstract: Whether it’s an infectious disease, a natural disaster, a human-made disaster, or a loss in utilities and resources, state and local leaders need visibility into the real-time resources of the entire healthcare continuum from labs, hospitals, long-term care settings, and shelters. By connecting public health and healthcare systems, information, and resources, leaders can be more agile and predictive in where to deploy limited resources before and during an emerging situation. The panelists will discuss how technology and data analytics can be utilized in real-time to resource decisions, bi-directional communication, transparency to stakeholders, and policy development. They will also explore the public health and healthcare continuum for mutual strategy, predictive modeling and reduction of excess loss of life. The panel will consist of a short introduction by each panelist followed by a facilitated discussion, and questions from the audience.
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Josep Cobarsí, & Laura Calvet. (2021). Quantitative data about deaths due to COVID-19 and comparability between countries: An approach through the case of Spain. 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. 294–304). Blacksburg, VA (USA): Virginia Tech.
Abstract: Mortality statistics tend to be inaccurate because of the imperfections related to individual deaths' recording. Recently, the COVID-19 pandemic has brought controversies regarding the quantification of deaths in many countries. Mainly, controversies were fueled by the sudden change of the criteria being applied, the limited testing and tracing capacities, and the collapse of the healthcare system. This work analyses the case of Spain, which constitutes one of the European countries with the highest number of cases and deaths during the 'first wave'. It provides a discussion about the coherence, traceability and limitations of quantitative data sources, as a basis to improve the quality of the data and its comparability between different countries and over time. Official data sources and non-official data sources are considered. Finally, suggestions of improvement and research needs are gathered, for the reliability of mortality data as a way to enhance learning and resilience for future crises.
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Vitaveska Lanfranchi, Suvodeep Mazumdar, & Fabio Ciravegna. (2014). Visual design recommendations for situation awareness in social media. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 792–801). University Park, PA: The Pennsylvania State University.
Abstract: The use of online Social Media is increasingly popular amongst emergency services to support Situational Awareness (i.e. accurate, complete and real-time information about an event). Whilst many software solutions have been developed to monitor and analyse Social Media, little attention has been paid on how to visually design for Situational Awareness for this large-scale data space. We describe an approach where levels of SA have been matched to corresponding visual design recommendations using participatory design techniques with Emergency Responders in the UK. We conclude by presenting visualisation prototypes developed to satisfy the design recommendations, and how they contribute to Emergency Responders' Situational Awareness in an example scenario. We end by highlighting research issues that emerged during the initial evaluation.
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Lennart Landsberg, David Ganske, Christopher Munschauer, & Ompe Aimé Mudimu. (2020). Using Existing Data to Support Operational Emergency Response in Germany – Current Use Cases, Opportunities and Challenges. 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. 406–415). Blacksburg, VA (USA): Virginia Tech.
Abstract: The availability of resources in the fire and ambulance services in Germany is facing a radical change. Demographic and social transition is reducing the availability of volunteer personnel, and increasing traffic congestion in cities is resulting in longer travel times for emergency vehicles. This paper presents the findings of the definition phase of a research project that addresses these changes. It shows the basic idea of how resilience of fire and ambulance services can be improved by analyzing operational data from past incidents using artificial intelligence (AI). The primary objective is the development of a decision support system for control center dispatchers, which ensures optimal use of available resources. As the result of the definition phase, this paper gives an overview of existing data, current as well as future use cases and also highlights risks and challenges that have to be considered.
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Louis Ngamassi, Abish Malik, Jiawei Zhang, & David Edbert. (2017). Social Media Visual Analytic Toolkits for Disaster Management: A Review of the Literature. 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. 785–797). Albi, France: Iscram.
Abstract: The past decade has seen a significant increase in the use of social media for disaster management. This is due especially to the widespread usage of mobile devices and also to the different data types and data formats that social media supports. In recent years, research in the area of social media visual analytics has also gained interest in the scientific community. Research in this area however, lacks a comprehensive overview on social media visual analytics for disaster management. Hence, this paper presents a synthesis of extant research on social media visual analytic and visualization toolkits for disaster management. We survey available literature on these tools with the goal to outline the major characteristics and features, and to examine the extent to which they cover the full cycle of disaster management. Our main purpose is to provide a foundation based on the current literature that can help to shape future research directions to enhance social media visual analytic tools for full cycle disaster management.
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Nathan Elrod, Pranav Mahajan, Monica Katragadda, Shane Halse, & Jess Kropczynski. (2021). An Exploration of Methods Using Social Media to Examine Local Attitudes Towards Mask-Wearing During a Pandemic. 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. 345–358). Blacksburg, VA (USA): Virginia Tech.
Abstract: During the COVID-19 health crisis, local public offcials expend considerable energy encouraging citizens to comply with prevention measures in order to reduce the spread of infection. During the pandemic, mask-wearing has been accepted among health offcials as a simple preventative measure; however, some local areas have been more likely to comply than others. This paper explores methods to better understand local attitudes towards mask-wearing as a tool for public health offcials' situational awareness when preparing public messaging campaigns. This exploration compares three methods to explore local attitudes: sentiment analysis, n-grams, and hashtags. We also explore hashtag co-occurrence networks as a starting point to begin the filtering process. The results show that while sentiment analysis is quick and easy to employ, the results oer little insight into specific local attitudes towards mask-wearing, while examining hashtags and hashtag co-occurrence networks may be used a tool for a more robust understanding of local areas when attempting to gain situational awareness.
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Rahul Pandey, Brenda Bannan, & Hemant Purohit. (2020). CitizenHelper-training: AI-infused System for Multimodal Analytics to assist Training Exercise Debriefs at Emergency Services. 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. 42–53). Blacksburg, VA (USA): Virginia Tech.
Abstract: The adoption of Artificial Intelligence (AI) technologies across various real-world applications for human performance augmentation demonstrates an unprecedented opportunity for emergency management. However, the current exploration of AI technologies such as computer vision and natural language processing is highly focused on emergency response and less investigated for the preparedness and mitigation phases. The training exercises for emergency services are critical to preparing responders to perform effectively in the real-world, providing a venue to leverage AI technologies. In this paper, we demonstrate an application of AI to address the challenges in augmenting the performance of instructors or trainers in such training exercises in real-time, with the explicit aim of reducing cognitive overload in extracting relevant knowledge from the voluminous multimodal data including video recordings and IoT sensor streams. We present an AI-infused system design for multimodal stream analytics and lessons from its use during a regional training exercise for active violence events.
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Anthony C. Robinson, Alexander Savelyev, Scott Pezanowski, & Alan M. MacEachren. (2013). Understanding the utility of geospatial information in social media. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 918–922). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Crisis situations generate tens of millions of social media reports, many of which contain references to geographic features and locations. Contemporary systems are now capable of mining and visualizing these location references in social media reports, but we have yet to develop a deep understanding of what end-users will expect to do with this information when attempting to achieve situational awareness. To explore this problem, we have conducted a utility and usability analysis of SensePlace2, a geovisual analytics tool designed to explore geospatial information found in Tweets. Eight users completed a task analysis and survey study using SensePlace2. Our findings reveal user expectations and key paths for solving usability and utility issues to inform the design of future visual analytics systems that incorporate geographic information from social media.
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Sven Schaust, Maximilian Walther, & Michael Kaisser. (2013). Avalanche: Prepare, manage, and understand crisis situations using social media analytics. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 852–857). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The recent rise of Social Media services has created huge streams of information which can be very valuable in a variety of scenarios. One specific scenario that has received interest is how Social Media analytics can be beneficial in crisis situations. In this paper, we describe our vision for a Social Media-ready command and control center. As motivation for our work, we present a short analysis of tweets issued in NYC during Hurricane Sandy in late October 2012 and we give an overview of the architecture of our event detection subsystem.
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Shannon Daly, & James A. Thom. (2016). Mining and Classifying Image Posts on Social Media to Analyse Fires. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: We propose a methodology to study the occurrence of fires through image posts on Flickr; crowd-sourcing information from a noisy social media dataset can estimate the presence of fires. We collect several years worth of photos and associated metadata using fire-related search terms. We use an image classification model to detect geotagged photos that are further analysed to determine if a fire event did occur at a particular time and place. Furthermore, a case study investigates image features and spatio-temporal elements in the metadata, as well as location information contained in camera EXIF data.
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Brian M. Tomaszewski, Anthony C. Robinson, Chris E. Weaver, Michael Stryker, & Alan M. MacEachren. (2007). Geovisual analytics and crisis management. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 173–179). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Increasing data heterogeneity, fragmentation and volume, coupled with complex connections among specialists in disaster response, mitigation, and recovery situations demand new approaches for information technology to support crisis management. Advances in visual analytics tools show promise to support time-sensitive collaboration, analytical reasoning, problem solving and decision making for crisis management. Furthermore, as all crises have geospatial components, crisis management tools need to include geospatial data representation and support for geographic contextualization of location-specific decision-making throughout the crisis. This paper provides an introduction to and description of Geovisual Analytics applied to crisis management activity. The goal of Geovisual Analytics in this context is to support situational awareness, problem solving, and decision making using highly interactive, visual environments that integrate multiple data sources that include georeferencing. We use an emergency support function example to discuss how recent progress in Geovisual Analytics can address the issues a crisis can present.
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Maximilian Walther, Sven Schaust, & Michael Kaisser. (2013). Social media-based event detection for crisis management in the al za'atari refugee camp. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 927–928). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: Social Media data allows for profound analyses of user-generated content in order to predict or manage disasters and crisis situations. In this paper, we present an analysis of tweets from and about Al Za'atari, a refugee camp in Jordan close to the Syrian border. Our results are based on the analysis of location-tagged tweets by our “Avalanche” system in order to support an accurate situational awareness picture for on-site and off-site operators from relief organizations on evolving events and challenges.
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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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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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