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Geostatistics in Historical Macroseismic Data Analysis

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Transactions on Computational Science VI

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 5730))

Abstract

This paper follows a geostatistical approach for the evaluation, modelling and visualization of the possible local interactions between natural components and built-up elements in seismic risk analysis. This method, applied to old town centre of Potenza hilltop town, offers a new point of view for civil protection planning using kernel density and autocorrelation indexes maps to analyse macroseismic damage scenarios and to evaluate the local geological, geomorphological and 1857 earthquake’s macroseismic data.

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Author information

Authors and Affiliations

  1. National Research Council, Archaeological and monumental heritage institute, C/da S. Loia Zona industriale, 85050, Tito Scalo, (PZ), Italy

    Maria Danese & Maurizio Lazzari

  2. Laboratory of Urban and Territorial Systems, University of Basilicata, Via dell’Ateneo Lucano 10, 85100, Potenza, Italy

    Maria Danese & Beniamino Murgante

Authors
  1. Maria Danese
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  2. Maurizio Lazzari
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  3. Beniamino Murgante
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W, T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

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Danese, M., Lazzari, M., Murgante, B. (2009). Geostatistics in Historical Macroseismic Data Analysis. In: Gavrilova, M.L., Tan, C.J.K. (eds) Transactions on Computational Science VI. Lecture Notes in Computer Science, vol 5730. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10649-1_19

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  • DOI: https://doi.org/10.1007/978-3-642-10649-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10648-4

  • Online ISBN: 978-3-642-10649-1

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