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Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling

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  • F. Dell’Acqua
  • P. Gamba
  • K. Jaiswal

Abstract

This paper discusses spatial aspects of the global exposure dataset and mapping needs for earthquake risk assessment. We discuss this in the context of development of a Global Exposure Database for the Global Earthquake Model (GED4GEM), which requires compilation of a multi-scale inventory of assets at risk, for example, buildings, populations, and economic exposure. After defining the relevant spatial and geographic scales of interest, different procedures are proposed to disaggregate coarse-resolution data, to map them, and if necessary to infer missing data by using proxies. We discuss the advantages and limitations of these methodologies and detail the potentials of utilizing remote-sensing data. The latter is used especially to homogenize an existing coarser dataset and, where possible, replace it with detailed information extracted from remote sensing using the built-up indicators for different environments. Present research shows that the spatial aspects of earthquake risk computation are tightly connected with the availability of datasets of the resolution necessary for producing sufficiently detailed exposure. The global exposure database designed by the GED4GEM project is able to manage datasets and queries of multiple spatial scales. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • F. Dell’Acqua & P. Gamba & K. Jaiswal, 2013. "Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(3), pages 1291-1309, September.
  • Handle: RePEc:spr:nathaz:v:68:y:2013:i:3:p:1291-1309
    DOI: 10.1007/s11069-012-0241-2
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    Citations

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    Cited by:

    1. Gordana Pavić & Marijana Hadzima-Nyarko & Borko Bulajić & Željka Jurković, 2020. "Development of Seismic Vulnerability and Exposure Models—A Case Study of Croatia," Sustainability, MDPI, vol. 12(3), pages 1-24, January.
    2. Robert Stewart & Marie Urban & Samantha Duchscherer & Jason Kaufman & April Morton & Gautam Thakur & Jesse Piburn & Jessica Moehl, 2016. "A Bayesian machine learning model for estimating building occupancy from open source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1929-1956, April.
    3. Hernán Santa María & Matías A. Hube & Felipe Rivera & Catalina Yepes-Estrada & Jairo A. Valcárcel, 2017. "Development of national and local exposure models of residential structures in Chile," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(1), pages 55-79, March.
    4. Blake Walker & Cameron Taylor-Noonan & Alan Tabbernor & T’Brenn McKinnon & Harsimran Bal & Dan Bradley & Nadine Schuurman & John Clague, 2014. "A multi-criteria evaluation model of earthquake vulnerability in Victoria, British Columbia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 1209-1222, November.
    5. Wenhua Qi & Guiwu Su & Lei Sun & Fan Yang & Yang Wu, 2017. "“Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth: a case study in Tangshan, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(1), pages 107-139, March.

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