Constructing Damage Indices Based on Publicly Available Spatial Data: Exemplified by Earthquakes and Volcanic Eruptions in Indonesia

Emmanuel Skoufias; Eric Strobl; Thomas Tveit

doi:10.1007/s13753-021-00348-4

Volume 12 Issue 3

Dec. 2021

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Article Navigation > International Journal of Disaster Risk Science > 2021 > 12(3): 410-427

Emmanuel Skoufias, Eric Strobl, Thomas Tveit. Constructing Damage Indices Based on Publicly Available Spatial Data: Exemplified by Earthquakes and Volcanic Eruptions in Indonesia[J]. International Journal of Disaster Risk Science, 2021, 12(3): 410-427. doi: 10.1007/s13753-021-00348-4

Citation:

Emmanuel Skoufias, Eric Strobl, Thomas Tveit. Constructing Damage Indices Based on Publicly Available Spatial Data: Exemplified by Earthquakes and Volcanic Eruptions in Indonesia[J]. International Journal of Disaster Risk Science, 2021, 12(3): 410-427. doi: 10.1007/s13753-021-00348-4

Citation:

Emmanuel Skoufias, Eric Strobl, Thomas Tveit. Constructing Damage Indices Based on Publicly Available Spatial Data: Exemplified by Earthquakes and Volcanic Eruptions in Indonesia[J]. International Journal of Disaster Risk Science, 2021, 12(3): 410-427. doi: 10.1007/s13753-021-00348-4

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Constructing Damage Indices Based on Publicly Available Spatial Data: Exemplified by Earthquakes and Volcanic Eruptions in Indonesia

doi: 10.1007/s13753-021-00348-4

1 Poverty and Equity Global Practice, World Bank, Washington, DC 20433, USA;
2 Department of Economics, University of Bern, 3012 Bern, Switzerland

Available Online: 2021-12-25
Publish Date: 2021-12-25

Abstract

Abstract

This article demonstrates the construction of earthquake and volcano damage indices using publicly available remote sensing sources and data on the physical characteristics of events. For earthquakes we use peak ground motion maps in conjunction with building type fragility curves to construct a local damage indicator. For volcanoes we employ volcanic ash data as a proxy for local damages. Both indices are then spatially aggregated by taking local economic exposure into account by assessing nightlight intensity derived from satellite images. We demonstrate the use of these indices with a case study of Indonesia, a country frequently exposed to earthquakes and volcanic eruptions. The results show that the indices capture the areas with the highest damage, and we provide overviews of the modeled aggregated damage for all provinces and districts in Indonesia for the time period 2004 to 2014. The indices were constructed using a combination of software programs—ArcGIS/Python, Matlab, and Stata. We also outline what potential freeware alternatives exist. Finally, for each index we highlight the assumptions and limitations that a potential practitioner needs to be aware of.
- Damage indices,
- Indonesia,
- Natural hazard modeling,
- Remote sensing data

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References(65)

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