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Haiyan Hao, & Yan Wang. (2020). Hurricane Damage Assessment with Multi-, Crowd-Sourced Image Data: A Case Study of Hurricane Irma in the City of Miami. 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. 825–837). Blacksburg, VA (USA): Virginia Tech.
Abstract: The massive crowdsourced data generated on social networking platforms (e.g. Twitter and Flickr) provide free, real-time data for damage assessment (DA) even during catastrophes. Recent studies leveraging crowdsourced data for DA mainly focused on analyzing textual formats. Crowdsourced images can provide rich and objective information about damage conditions, however, are rarely researched for DA purposes. The highly-varied content and loosely-defined damage forms make it difficult to process and analyze the crowdsourced images. To address this problem, we propose a data-driven DA method based on multi-, crowd-sourced images, which includes five machine learning classifiers organized in a hierarchical structure. The method is validated with a case study investigating the damage condition of the City of Miami caused by Hurricane Irma. The outcome is then compared with a metric derived from NFIP insurance claims data. The proposed method offers a resource for rapid DA that supplements conventional DA methods.
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Lida Huang, Guoray Cai, Hongyong Yuan, Jianguo Chen, Yan Wang, & Feng Sun. (2018). Modeling Threats of Mass Incidents Using Scenario-based Bayesian Network Reasoning. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 121–134). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Mass incidents represent a global problem, putting potential threats to public safety. Due to the complexity and uncertainties of mass incidents, law enforcement agencies lack analytical models and structured processes for anticipating potential threats. To address such challenge, this paper presents a threat analysis framework combining the scenario analysis method and Bayesian network (BN) reasoning. Based on a case library
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Lida Huang, Tao Chen, Yan Wang, & Hongyong Yuan. (2015). Forecasting Daily Pedestrian Flows in the Tiananmen Square Based on Historical Data and Weather Conditions. 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: It is important to forecast the pedestrian flows for organizing crowd activities and making risk assessments. In this article, the daily pedestrian flows in the Tiananmen Square are forecasted based on historical data, the distribution of holidays and weather conditions including rain, wind, temperature, relative humidity, and AQI (Air Quality Index). Three different methods have been discussed and the Support Vector Regression based on the Adaptive Particle Swarm Optimization (APSO-SVR) has been proved the most reliable and accurate model to forecast the daily pedestrian flows. The results of this paper can help to conduct security pre-warning system and enhance emergency preparedness and management for crowd activities.
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Nadia Saad Noori, Yan Wang, Tina Comes, Philipp Schwarz, & Heide Lukosch. (2017). Behind the Scenes of Scenario-Based Training: Understanding Scenario Design and Requirements in High-Risk and Uncertain Environments. 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. 948–959). Albi, France: Iscram.
Abstract: Simulation exercises as a training tool for enhancing preparedness for emergency response are widely adopted in disaster management. This paper addresses current scenario design processes, proposes an alternative approach for simulation exercises and introduces a conceptual design of an adaptive scenario generator. Our work is based on a systematic literature review and observations made during TRIPLEX-2016 exercise in Farsund, Norway. The planning process and scenario selection of simulation exercises impact directly the effectiveness of intra- and interorganizational cooperation. However, collective learning goals are rarely addressed and most simulations are focused on institution-specific learning goals. Current scenario design processes are often inflexible and begin from scratch for each exercise. In our approach, we address both individual and collective learning goals and the demand to develop scenarios on different layers of organizational learning. Further, we propose a scenario generator that partly automates the scenario selection and adaptively responds to the exercise evolvement.
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Philipp Schwarz, Yan Wang, Stephan Lukosch, & Heide Lukosch. (2017). Policy Gaming for Humanitarian Missions. 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. 814–823). Albi, France: Iscram.
Abstract: Aid workers increasingly face risks when working in crisis regions. In order to improve effectiveness and safety of humanitarians, it is of great importance to provide a well thought out real-time socio-technical support. Thus, new policies and innovative technological solutions need to be developed and integrated into humanitarian workflows. For the requirements elicitation process to realize this aspiration, we employ a board game approach that confronts players with situations aid workers experience in the field. From the first game session, we learned that the game is a valuable tool. It raises awareness to important challenges and trade-offs that humanitarians face. In addition, it is an effective catalyst for initiating a discussion on which system requirements are needed. Future work will include an update of the board game as well as sessions with the target group of practitioners to inform the development of a socio-technical system for humanitarian aid work.
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Shangde Gao, Yan Wang, & Lisa Platt. (2021). Modeling U.S. Health Agencies' Message Dissemination on Twitter and Users' Exposure to Vaccine-related Misinformation Using System Dynamics. 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. 333–344). Blacksburg, VA (USA): Virginia Tech.
Abstract: This research intends to answer: how do (i) generation frequency and (ii) retweeting count of health agencies' messages impact the exposure of the general users to vaccine-related misinformation on Twitter? We creatively employed a Susceptible-Infected-Recovered (SIR) System Dynamics paradigm to model interactions between message dissemination of 168 U.S. health agencies and proportions of users who are at different exposure statuses to misinformation, namely “Susceptible”, “Infected”, or “Recovered” status. The SIR model was built based on the vaccine-relevant tweets posted over November and December in 2020. Our preliminary outcomes suggest that augmenting the generation frequency of agencies' messages and increasing retweeting count can effectively moderate the exposure risk to vaccine-related misinformation. This model illustrates how health agencies may combat vaccine hesitancy through credible information dissemination on social media. It offers a novel approach for crisis informatics studies to model different information categories and the impacted population in the complex digital world.
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Tongshen Zheng, Shunjiang Ni, Shifei Shen, Yan Wang, & Yang Tai. (2016). Numerical Study of Radioactive Pollutants Dispersion in Radioactive ?Dirty Bomb? Events. 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: The simulation of radioactive pollutants dispersion is critical for emergency response of the nuclear terrorism. The radioactive ?dirty bomb?, also called radiological dispersion device (RDD), produced and used by the terrorist to make fearful and radioactive pollution in general, has a great risk on humans. Numerical investigation of the impact of different configurations on radioactive pollution release and dispersion in urban buildings is made in this paper. The numerical simulations used the OpenFOAM, a free and open source software for computational fluid dynamics (CFD), and the simulations can be implanted to the information system of the nuclear terrorism emergency decision support system(EDSS) as the consequence assessment subsystem conveniently. The study showed that the configurations of building canyon and the position relationship of the source item and the buildings both affect the concentration distributions around the buildings.
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Yan Wang, Hong Huang, Lida Huang, Minyan Han, Yiwu Qian, & Boni Su. (2017). An Agile Framework for Detecting and Quantifying Hazardous Gas Releases. 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. 42–49). Albi, France: Iscram.
Abstract: In response to the threat of hazardous gas releases to public safety and health, we propose an agile framework for detecting and quantifying gas emission sources. Emerging techniques like high-precision gas sensors, source term estimation algorithms and Unmanned Aerial Vehicles are incorporated. The framework takes advantage of both stationary sensor network method and mobile sensing approach for the detection and quantification of hazardous gases from fugitive, accidental or deliberate releases. Preliminary results on street-level detection of urban natural gas leakage is presented. Source term estimation is demonstrated through a synthetic test case, and is verified using Cramér-Rao bound analysis.
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Yan Wang, Hong Huang, & Wei Zhu. (2015). Stochastic source term estimation of HAZMAT releases: algorithms and uncertainty. 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: Source term estimation (STE) of hazardous material (HAZMAT) releases is critical for emergency response. Such problem is usually solved with the aid of atmospheric dispersion modelling and inversion algorithms accompanied with a variety of uncertainty, including uncertainty in atmospheric dispersion models, uncertainty in meteorological data, uncertainty in measurement process and uncertainty in inversion algorithms. Bayesian inference methods provide a unified framework for solving STE problem and quantifying the uncertainty at the same time. In this paper, three stochastic methods for STE, namely Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC) and ensemble Kalman filter (EnKF), are compared in accuracy, time consumption as well as the quantification of uncertainty, based on which a kind of flip ambiguity phenomenon caused by various uncertainty in STE problems is pointed out. The advantage of non-Gaussian estimation methods like SMC is emphasized.
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Yan Wang, & John E. Taylor. (2017). Tracking urban resilience to disasters: a mobility network-based approach. 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. 97–109). Albi, France: Iscram.
Abstract: Disaster resilience is gaining increasing attention from both industry and academia, but difficulties in operationalizing the concept remain, especially in the urban context. Currently, there is scant literature on measuring both spatial and temporal aspects of resilience empirically. We propose a bio-inspired quantitative framework to track urban resilience to disasters. This framework was built upon a daily human mobility network, which was generated by geolocations from a Twitter Streaming API. System-wide metrics were computed over time (i.e. pre-, during and post-disasters). Fisher information was further adopted to detect the perturbation and dynamics in the system. Specifically, we applied the proposed approach in a flood case in the metropolis of São Paulo. The proposed approach is efficient in uncovering the dynamics in human movements and the underlying spatial structure. It adds to our understanding of the resilience process in urban disasters.
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Yan Wang, Qi Wang, & John Taylor. (2021). Loss of Resilience in Human Mobility across Severe Tropical Cyclones of Different Magnitudes. 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. 755–765). Blacksburg, VA (USA): Virginia Tech.
Abstract: Severe tropical cyclones impose threats on highly populated coastal urban areas, thereby, understanding and predicting human movements plays a critical role in evaluating disaster resilience of human society. However, limited research has focused on tropical cyclones and their influence on human mobility resilience. This preliminary study examined the strength and duration of human mobility perturbation across five significant tropical storms and their affected eight urban areas using Twitter data. The results suggest that tropical cyclones can significantly perturb human movements by changing travel frequencies and displacement probability distributions. While the power-law still best described the pattern of human movements, the changes in the radii of gyration were significant and resulted in perturbation and loss of resilience in human mobility. The findings deepen the understanding about human-environment interactions under extreme events, improve our ability to predict human movements using social media data, and help policymakers improve disaster evacuation and response.
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