Ayda Kianmehr, & Duygu Pamukcu. (2022). Analyzing Citizens’ Needs during an Extreme Heat Event, based on 311 Service Requests: A Case Study of the 2021 Heatwave in Vancouver, British Columbia. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 174–182). Tarbes, France.
Abstract: Heat waves are becoming more common and intense with global climate change, which requires deploying resilience strategies of governments to prepare for long-term trends of higher temperatures and carefully plan emergency responses for such extreme heat events. The British Columbia province of Canada is one of the regions severely affected by extreme climatic events in 2021, which resulted in several deaths and put hundreds of thousands of people scrambling for relief. This study examines the public reactions to one of these extreme climatic events, the 2021 Pacific Northwest heatwave, in a non-emergency service request platform to uncover the types of municipal service needs during severe climatic disasters. City of Vancouver 311 system data is used to identify the impact of the heatwave on the frequency and types of service needs and examine the significance of the relationship between climatic conditions and the non-emergency service volumes.
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Viktor Sköld Gustafsson, Tobias Andersson Granberg, Sofie Pilemalm, & Martin Waldemarsson. (2022). Managing Natural Hazards in Sweden – Needs for Improved Information and Decision Support Systems. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 376–384). Tarbes, France.
Abstract: This paper explores opportunities for information systems to support emergency response to multiple natural hazards. Interviews were conducted with 12 representatives from actors of the Swedish emergency response system about response to multiple natural hazards. Challenges and needs connected to five themes influencing the response effort were identified: Cooperation, Resource management, Command and control, Common operational picture, and Risk management. The results illuminate a lack of technology to support decisions and analyses during emergency response to both single and multiple natural hazards. Based on this, the paper suggests and discusses information systems and decision support tools to assist in satisfying the identified needs. The findings can inform policy makers in emergency response of where to concentrate the development of collaborative preparedness and response work, and the scientific community of future research directions.
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Xinyuan Zhang, & Nan Li. (2020). Assessment of the Correlation between Extreme Weather Event-Induced Human Mobility Perturbation in Urban Areas and their Spatial Characteristics based on Taxi Trajectories. 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. 366–380). Blacksburg, VA (USA): Virginia Tech.
Abstract: Extreme weather events (EWEs) are significant threats to urban regions. One major reflection of such impact is the EWE-induced perturbation to urban human mobility, which has been documented in a number of recent studies. This study aims to examine the spatial distribution of such perturbation within a city among different areas that are characterized by the type of function and the distance to city center. A case study was conducted on a major rainstorm in the City of Nanjing, China in 2017, based on trajectories of all taxis in the city before and during the rainstorm. It was found that the rainstorm caused decrease in people's travel demand throughout the city, although the magnitude of perturbation and level of resilience notably differed among areas of different functional types. In addition, the urban mobility in areas distant from the city center were relatively less influenced by the rainstorm.
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Shane Errol Halse, Aurélie Montarnal, Andrea Tapia, & Frederick Benaben. (2018). Bad Weather Coming: Linking social media and weather sensor data. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 507–515). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: In this paper we leverage the power of citizen supplied data. We examined how both physical weather sensor data (obtained from the weather underground API) and social media data (obtained from Twitter) can serve to improve local community awareness during a severe weather event. A local tornado warning was selected due to its small scale and isolated geographic area, and only Twitter data found from within this geo-locational area was used. Our results indicate that during a severe weather event, an increase in weather activity obtained from the local weather sensors does correlate with an increase in local social media usage. The data found on social media also contains additional information from, and about the community of interest during the event. While this study focuses on a small scale event, it provides the groundwork for use during a much larger weather event.
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Flavio Horita, Ricardo Vilela, Renata Martins, Danielle Bressiani, Gilca Palma, & João Porto de Albuquerque. (2018). Determining flooded areas using crowd sensing data and weather radar precipitation: a case study in Brazil. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 1040–1050). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Crowd sensing data (also known as crowdsourcing) are of great significance to support flood risk management. With the growing volume of available data in the past few years, researchers have used in situ sensor data to filter and prioritize volunteers' information. Nevertheless, stationary, in situ sensors are only capable of monitoring a limited region, and this could hamper proper decision-making. This study investigates the use of weather radar precipitation to support the processing of crowd sensing data with the goal of improving situation awareness in a disaster and early warnings (e.g., floods). Results from a case study carried out in the city of São Paulo, Brazil, demonstrate that weather radar data are able to validate flooded areas identified from clusters of crowd sensing data. In this manner, crowd sensing and weather radar data together can not only help engage citizens, but also generate high-quality data at finer spatial and temporal resolutions to improve the decision-making related to weather-related disaster events.
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