Earthquake Loss Estimation for India Based on Macroeconomic Indicators

Dunbar, P. K.; Bilham, Roger; Laituri, Melinda

doi:10.1007/978-94-010-0167-0_13

Cited by 21 publications

(17 citation statements)

References 6 publications

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“…Several researchers have developed GDP and population data-based models to broadly estimate the seismic vulnerability and risk of an area [20][21][22][23]. However, the risk implications of the results of these studies are not specific or detailed relative to those of conventional and mainstream LRC and DPM-based methods.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Method Combining Remote Sensing Data and Local Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China

Zhang

et al. 2015

Remote Sensing

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Rapid socioeconomic development in earthquake-prone areas can cause rapid changes in seismic loss risks. These changes make it difficult to ensure that risk reduction strategies are realistic, practical and effective over time. To overcome this difficulty, ongoing changes in risk should be captured timely, definitively, and accurately and then specific and well-timed adjustments of the relevant strategies should be made. However, methods for rapidly characterizing such seismic disaster risks over a large area have not been sufficiently developed. By focusing on building loss risks, this paper presents the development of an integrated method that combines remote sensing data and local OPEN ACCESSRemote Sens. 2015, 7 2544 knowledge to resolve this problem. This method includes two key interdependent steps.(1) To extract the heights and footprint areas of a large number of buildings accurately and quickly from single high-resolution optical remote sensing images; (2) To estimate the floor areas, identify structural types, develop damage probability matrixes, and determine economic parameters for calculating monetary losses due to seismic damage to the buildings by reviewing building-relevant local knowledge based on these two parameters (i.e., the building heights and footprint areas). This method is demonstrated in the Tangshan area of China. Based on the integrated method, the total floor area of the residential and public office buildings in central Tangshan in 2009 was 3.99% lower than the corresponding area number obtained by a conventional earthquake loss estimation project. Our field-based verification indicated that the mean relative error of the method for estimating the floor areas of the assessed buildings was 2.99%. A simulation of the impacts of the 1976 Ms 7.8 Tangshan earthquake using this method indicated that the total damaged floor area of the residential and public office buildings and the associated direct monetary loses in the study area could have been 8.00 and 28.73 times greater, respectively, than in 1976 if this earthquake had recurred in 2009, which is a strong warning to the local people regarding the increasing challenges they may face.

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Section: Introductionmentioning

confidence: 99%

An Integrated Method Combining Remote Sensing Data and Local Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China

Zhang

et al. 2015

Remote Sensing

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“…The data contained in the DCW were used to generate a 1:1,000,000-scale vector database covering the entire surface of the earth [32]. Population density was obtained from the LandScan 2000 Global Population 30-arc-second Database (LandScan 2000) which was developed by Oak Ridge National Laboratory for the United States Department of Defense [33]. The population …”

Section: Bioclimatic Variables and Datamentioning

confidence: 99%

Bioclimatic Classification of Northeast Asia Reflecting Social Factors: Development and Characterization

et al. 2017

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Biodiversity is rapidly declining globally and efforts are needed to mitigate this continually increasing loss of species. Clustering of areas with similar habitats can be used to prioritize protected areas and distribute resources for the conservation of species, selection of representative sample areas for research, and evaluation of impacts due to environmental changes. In this study, Northeast Asia (NEA) was classified into 27 bioclimatic zones using statistical techniques, and it was re-clustered into 14 groups to identify the environmental characteristics of these zones. In particular, we added land cover variables into the clustering to reflect not only simple climate but also social factors influencing biological habitats. In total, 53 bioclimatic variables were constructed, and principal components were generated using correlation analysis and principal component analysis (PCA). The iterative self-organizing data analysis technique algorithm (ISODATA) was used to cluster the principal components and land cover variable. The constructed bioclimatic zones were assigned codes and descriptive names based on aridity, seasonality, and naturality, and their environmental characteristics were identified. This study is significant in that it improved the understanding of biological habitats of NEA and established a basis for monitoring the distribution of species and for efficient and systematic management of biodiversity.

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“…Building level data are able to provide a more detailed population database than the data sets developed for gridded population dynamic models. There are different components in population loss estimation, such as estimation of deaths and injuries, homelessness, or postdisaster displacement (Dunbar et al 2003). Spence and So (2009) classified the available earthquake population loss estimation into three estimation methods: empirical, semiempirical, and analytical.…”

Section: Population Distribution and Loss Estimationmentioning

confidence: 99%

“…These quantification processes are very complex but are a useful tool for developing an emergency preparedness plan and reducing future seismic risk (Agrawal and Ajay 2004). Loss estimation can be done by methods using GIS and remote sensing techniques (Chen et al 1998), GDP and population distribution data (Chen et al 1997;Dunbar et al 2003), as well as specialized computer-based modeling approaches. These methodologies provide basic information about the physical, social, and economic losses caused by an earthquake.…”

Section: Introductionmentioning

confidence: 99%

Impact of Temporal Population Distribution on Earthquake Loss Estimation: A Case Study on Sylhet, Bangladesh

Ara

2014

Int J Disaster Risk Sci

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To estimate human loss in an earthquake-prone area, it is necessary to analyze the role played by the spatiotemporal distribution of the area's resident population. In order to evaluate earthquake impact, this article focuses on the spatiotemporal distribution of population and five scenario earthquakes that form the basis for loss estimation in the city of Sylhet, Bangladesh. Four temporal contexts (weekday, weekly holiday, the 30 days of Ramadan, and strike days) expand the more typical daytime and nighttime settings in which to examine hazard risk. The population distribution for every 2 hour interval in a day is developed for each type of day. A relationship between the occupancy classes and average space (persons per 100 m 2 ) is used to distribute people in each building regardless of building locations. A total daytime and nighttime population is obtained for each building and the estimated nighttime population is used to model the population for four temporal scenarios in a year based on different factors and weights. The resulting data are employed to estimate population loss for each of the temporal and earthquake scenarios. This study used building-specific human vulnerability curves developed by the Central American Probabilistic Risk Assessment (CAPRA) to obtain possible loss of life estimates. The results reveal that there is a high positive correlation between the spatiotemporal distribution of population and the potential number of casualties.

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Earthquake Loss Estimation for India Based on Macroeconomic Indicators

Cited by 21 publications

References 6 publications

An Integrated Method Combining Remote Sensing Data and Local Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China

An Integrated Method Combining Remote Sensing Data and Local Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China

Bioclimatic Classification of Northeast Asia Reflecting Social Factors: Development and Characterization

Impact of Temporal Population Distribution on Earthquake Loss Estimation: A Case Study on Sylhet, Bangladesh

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