Neda Mohammadi, John E. Taylor, & Ryan Pollyea. (2017). Spatiotemporal Dynamics of Public Response to Human-Induced Seismic Perturbations. 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. 666–672). Albi, France: Iscram.
Abstract: There is general consensus that subsurface wastewater injections associated with unconventional oil and gas operations are responsible for the rapid increase of earthquake activity in the mid-U.S. Understanding the public response to these earthquakes is crucial for policy decisions that govern developing situational awareness and addressing perceived risks. However, we lack sufficient information on the reactive and recovery response behavior of the public tending to occur in the spatiotemporal vicinity of these events. Here, we review the spatiotemporal distribution of public response to the September 3, 2016, M5.8 earthquake in Pawnee, Oklahoma, USA, via a social media network (Twitter). Our findings highlight a statistically significant correlation between the spatial and temporal distribution of public response; and suggest the possible presence of a spatial distance decay, as well as a temporal far-field eect. Understanding the underlying structure of these correlations is fundamental to establishing deliberate policy decisions and targeted response actions.
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Toshihiro Osaragi. (2016). Estimation of Transient Occupants on Weekdays and Weekends for Risk Exposure Analysis. 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: Understanding the characteristics of the spatiotemporal distribution of a population, which varies according to the time of the day and the day of the week (weekday or weekend), is one of the most important issues in the field of urban disaster mitigation planning. However, the existing Person Trip Survey data based on weekdays is not appropriate for estimating the spatiotemporal distribution of population on weekends. In the present study, we proposed a method for converting existing Person Trip Survey data from a weekday base to a weekend base and examined the differences in the spatiotemporal distribution of the population. Using these databases, we attempted to compare the number of deaths due to building collapse estimated for weekdays and weekends for various districts in the Tokyo Metropolitan area.
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Toshihiro Osaragi. (2015). Spatiotemporal Distribution of Automobile Users: Estimation Method and Applications to Disaster Mitigation Planning. 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: When discussing human casualties from a severe earthquake with regard to urban disaster mitigation planning, it is important to clarify the characteristics of the spatiotemporal distribution of people. In this paper, we construct a model that estimates the spatiotemporal distribution of automobile users using data from the Person Trip Survey and the Road Traffic Census. We use this model to estimate the spatiotemporal distribution of automobile users in Tokyo and demonstrate several ways to apply this data to urban disaster mitigation planning.
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Brian M. Tomaszewski, & Alan M. MacEachren. (2006). A distributed spatiotemporal cognition approach to visualization in support of coordinated group activity. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 347–351). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Technological advances in both distributed cooperative work and web-map services have the potential to support distributed and collaborative time-critical decision-making for crisis response. We address this potential through the theoretical perspective of distributed cognition and apply this perspective to development of a geocollaborationenabled web application that supports coordinated crisis management activities. An underlying goal of our overall research program is to understand how distributed cognition operates across groups working to develop both awareness of the geographic situation within which events unfold, and insights about the processes that have lead to that geographic situation over time. In this paper, we present our preliminary research on a web application that addresses these issues. Specifically, the application (key parts of which are implemented) enables online, asynchronous, map-based interaction between actors, thus supporting distributed spatial and temporal cognition, and, more specifically, situational awareness and subsequent action in the context of humanitarian disaster relief efforts.
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Sérgio Freire, & Christoph Aubrecht. (2011). Assessing spatio-temporal population exposure to tsunami hazard in the Lisbon Metropolitan Area. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: The coastal region of Lisbon, Portugal, is potentially subject to tsunami hazard. Mapping and assessing tsunami risk requires giving adequate consideration to the population exposure. In the present work we model and map the spatio-temporal distribution of population in the daily cycle and analyze it with a tsunami hazard map to better assess tsunami risk in the Lisbon Metropolitan Area. New high-resolution daytime and nighttime population distribution surfaces are developed using 'intelligent dasymetric mapping' to combine best-available census data and statistics with land use and land cover data. Mobility statistics are considered for mapping daytime distribution. Finally, the population distribution maps are combined with the Tsunami Inundation Susceptibility map to assess potential human exposure to tsunami in daytime and nighttime periods. Results show that a significant amount of population is potentially at risk, and its numbers increase from nighttime to daytime, especially in the zones of high susceptibility.
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