68,192 research outputs found
Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data
Despite the importance of predicting evacuation mobility dynamics after large
scale disasters for effective first response and disaster relief, our general
understanding of evacuation behavior remains limited because of the lack of
empirical evidence on the evacuation movement of individuals across multiple
disaster instances. Here we investigate the GPS trajectories of a total of more
than 1 million anonymized mobile phone users whose positions are tracked for a
period of 2 months before and after four of the major earthquakes that occurred
in Japan. Through a cross comparative analysis between the four disaster
instances, we find that in contrast with the assumed complexity of evacuation
decision making mechanisms in crisis situations, the individuals' evacuation
probability is strongly dependent on the seismic intensity that they
experience. In fact, we show that the evacuation probabilities in all
earthquakes collapse into a similar pattern, with a critical threshold at
around seismic intensity 5.5. This indicates that despite the diversity in the
earthquakes profiles and urban characteristics, evacuation behavior is
similarly dependent on seismic intensity. Moreover, we found that probability
density functions of the distances that individuals evacuate are not dependent
on seismic intensities that individuals experience. These insights from
empirical analysis on evacuation from multiple earthquake instances using large
scale mobility data contributes to a deeper understanding of how people react
to earthquakes, and can potentially assist decision makers to simulate and
predict the number of evacuees in urban areas with little computational time
and cost, by using population density information and seismic intensity which
can be observed instantaneously after the shock
Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change
This Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) has been jointly coordinated by Working Groups I (WGI) and II (WGII) of the Intergovernmental Panel on Climate Change (IPCC). The report focuses on the relationship between climate change and extreme weather and climate events, the impacts of such events, and the strategies to manage the associated risks. The IPCC was jointly established in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP), in particular to assess in a comprehensive, objective, and transparent manner all the relevant scientific, technical, and socioeconomic information to contribute in understanding the scientific basis of risk of human-induced climate change, the potential impacts, and the adaptation and mitigation options. Beginning in 1990, the IPCC has produced a series of Assessment Reports, Special Reports, Technical Papers, methodologies, and other key documents which have since become the standard references for policymakers and scientists.This Special Report, in particular, contributes to frame the challenge of dealing with extreme weather and climate events as an issue in decisionmaking under uncertainty, analyzing response in the context of risk management. The report consists of nine chapters, covering risk management; observed and projected changes in extreme weather and climate events; exposure and vulnerability to as well as losses resulting from such events; adaptation options from the local to the international scale; the role of sustainable development in modulating risks; and insights from specific case studies
Flooding through the lens of mobile phone activity
Natural disasters affect hundreds of millions of people worldwide every year.
Emergency response efforts depend upon the availability of timely information,
such as information concerning the movements of affected populations. The
analysis of aggregated and anonymized Call Detail Records (CDR) captured from
the mobile phone infrastructure provides new possibilities to characterize
human behavior during critical events. In this work, we investigate the
viability of using CDR data combined with other sources of information to
characterize the floods that occurred in Tabasco, Mexico in 2009. An impact map
has been reconstructed using Landsat-7 images to identify the floods. Within
this frame, the underlying communication activity signals in the CDR data have
been analyzed and compared against rainfall levels extracted from data of the
NASA-TRMM project. The variations in the number of active phones connected to
each cell tower reveal abnormal activity patterns in the most affected
locations during and after the floods that could be used as signatures of the
floods - both in terms of infrastructure impact assessment and population
information awareness. The representativeness of the analysis has been assessed
using census data and civil protection records. While a more extensive
validation is required, these early results suggest high potential in using
cell tower activity information to improve early warning and emergency
management mechanisms.Comment: Submitted to IEEE Global Humanitarian Technologies Conference (GHTC)
201
Architecture of Environmental Risk Modelling: for a faster and more robust response to natural disasters
Demands on the disaster response capacity of the European Union are likely to
increase, as the impacts of disasters continue to grow both in size and
frequency. This has resulted in intensive research on issues concerning
spatially-explicit information and modelling and their multiple sources of
uncertainty. Geospatial support is one of the forms of assistance frequently
required by emergency response centres along with hazard forecast and event
management assessment. Robust modelling of natural hazards requires dynamic
simulations under an array of multiple inputs from different sources.
Uncertainty is associated with meteorological forecast and calibration of the
model parameters. Software uncertainty also derives from the data
transformation models (D-TM) needed for predicting hazard behaviour and its
consequences. On the other hand, social contributions have recently been
recognized as valuable in raw-data collection and mapping efforts traditionally
dominated by professional organizations. Here an architecture overview is
proposed for adaptive and robust modelling of natural hazards, following the
Semantic Array Programming paradigm to also include the distributed array of
social contributors called Citizen Sensor in a semantically-enhanced strategy
for D-TM modelling. The modelling architecture proposes a multicriteria
approach for assessing the array of potential impacts with qualitative rapid
assessment methods based on a Partial Open Loop Feedback Control (POLFC) schema
and complementing more traditional and accurate a-posteriori assessment. We
discuss the computational aspect of environmental risk modelling using
array-based parallel paradigms on High Performance Computing (HPC) platforms,
in order for the implications of urgency to be introduced into the systems
(Urgent-HPC).Comment: 12 pages, 1 figure, 1 text box, presented at the 3rd Conference of
Computational Interdisciplinary Sciences (CCIS 2014), Asuncion, Paragua
Fiscal Storms: Public Spending and Revenues in the Aftermath of Natural Disasters
Recent research in both the social and natural sciences has been devoted to increasing our ability to predict disasters, prepare for them and mitigate their costs. Curiously, we appear to know very little about the fiscal consequences of disasters. The likely fiscal impact of a natural disaster has not been examined before in any comparable or comparative framework. We estimate and quantify the fiscal consequences of natural disasters using quarterly fiscal and disaster data for a large panel of countries. In our estimations, we employ a panel VAR framework that is similar to Burnside et al. (Journal of Economic Theory, 2004), that also controls for the business cycle. We find fiscal behavior in the aftermath of disasters in developed countries that can best be characterized as counter-cyclical. In contrast, we find pro-cyclical decreased spending and increasing revenues in developing countries following large natural disasters. We quantify these effects.natural disasters, fiscal policy
- …