Skip to main content
SpringerLink
Log in
Book cover

Transactions on Computational Science VI pp 324–341Cite as

Geostatistics in Historical Macroseismic Data Analysis

  • Chapter

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Tobler, W.R.: A Computer Model Simulating Urban Growth in the Detroit Region. Economic Geography 46, 234–240 (1970)

    Article  Google Scholar 

  2. Gatrell, A.C., Bailey, T.C., Diggle, P.J., Rowlingson, B.S.: Spatial Point Pattern Analysis and Its Application in Geographical Epidemiology. Transaction of Institute of British Geographer 21, 256–271 (1996)

    Article  Google Scholar 

  3. Bailey, T.C., Gatrell, A.C.: Interactive Spatial Data Analysis. Longman Higher Education, Harlow (1995)

    Google Scholar 

  4. O’Sullivan, D., Wong, D.W.S.: A Surface-Based Approach to Measuring Spatial Segregation. Geographical Analysis, 147–168 (2007)

    Google Scholar 

  5. Danese, M., Lazzari, M., Murgante, B.: Kernel Density Estimation Methods for a Geostatistical Approach in Seismic Risk Analysis: the Case Study of Potenza Hilltop Town (southern Italy). In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008, Part I. LNCS, vol. 5072, pp. 415–429. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  6. Danese, M., Lazzari, M., Murgante, B.: Integrated Geological, Geomorphological and Geostatistical analysis to study macroseismic effects of 1980 Irpinian earthquake in urban areas (southern Italy). In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M. (eds.) ICCSA 2009. LNCS, vol. 5592, pp. 302–9743. Springer, Heidelberg (in press, 2009)

    Google Scholar 

  7. Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Monographs on Statistics and Applied Probability. Chapman and Hall, London (1986)

    MATH  Google Scholar 

  8. Breiman, L., Meisel, W., Purcell, E.: Variable Kernel Estimates of Multivariate Densities. Technometrics 19, 135–144 (1977)

    Article  MATH  Google Scholar 

  9. Levine, N.: CrimeStat III: A Spatial Statistics Program for the Analysis of Crime Incident Locations. Ned Levine & Associates, Houston, TX, and the National Institute of Justice, Washington, DC (2004)

    Google Scholar 

  10. Sacks, J., Ylvisaker, D.: Asymptotically Optimum Kernels for Density Estimates at a Point. Annals of Statistics 9, 334–346 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  11. Borruso, G.: Network Density and the Delimitation of Urban Areas. Transaction in GIS, 7 2, 177–191 (2003)

    Article  Google Scholar 

  12. Miller, H.: A Measurement Theory for Time Geography. Geographical Analysis 37, 17–45 (2005)

    Article  Google Scholar 

  13. Burt, J.E., Barber, G.M.: Elementary Statistics for Geographers, 2nd edn. The Guilford Press, New York (1996)

    Google Scholar 

  14. Bailey, T.C., Gatrell, A.C.: Interactive Spatial Data Analysis. Longman Higher Education, Harlow (1995)

    Google Scholar 

  15. Epanechnikov, V.A.: Nonparametric Estimation of a Multivariate Probability Density. Theory of Probability and Its Applications 14, 153–158 (1969)

    Article  Google Scholar 

  16. Downs, J.A., Horner, M.W.: Characterising Linear Point Patterns. In: Proceedings of the GIS Research UK Annual Conference (GISRUK 2007), Maynooth, Ireland (2007)

    Google Scholar 

  17. Jones, M.C., Marron, J.S., Sheather, S.J.: A Brief Survey of Bandwidth Selection for Density Estimation. Journal of the American Statistical Association 91, 401–407 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  18. Sheather, S.J.: An Improved Data-Based Algorithm for Choosing the Window Width When Estimating the Density at a Point. Computational Statistics and Data Analysis 4, 61–65 (1986)

    Article  Google Scholar 

  19. Hall, P., Sheather, S.J., Jones, M.C., Marron, J.S.: On Optimal Data-Based Bandwidth Selection in Kernel Density Estimation. Biometrika 78, 263–269 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  20. Hazelton, M.: Optimal Rates for Local Bandwidth Selection. J. Nonparametr. Statist. 7, 57–66 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  21. Sillverman, B.W., Jones, M.C., Fix, E., Hodges, J.L.: An important contribution to nonparametric discriminant analysis and density estimation. International Statistical Review 57(3), 233–247 (1951)

    Google Scholar 

  22. Loftsgaarden, D.O., Quesenberry, C.P.: A Nonparametric Estimate of a Multivariate Density Function. Ann. Math. Statist. 36, 1049–1051 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  23. Clark, P.J., Evans, F.C.: Distance to Nearest Neighbour as a Measure of Spatial Relationships in Populations. Ecology 35, 445–453 (1994)

    Article  Google Scholar 

  24. Cao, R., Cuevas, A., González-Manteiga, W.: A Comparative study of several smoothing methods in density estimation. Computational Statistic and Data Analysis 17, 153–176 (1994)

    Article  MATH  Google Scholar 

  25. Wand, M., Jones, M.C.: Kernel Smoothing. Chapman & Hall, London (1995)

    MATH  Google Scholar 

  26. Simonoff, J.: Smoothing Methods in Statistics. Springer, New York (1996)

    MATH  Google Scholar 

  27. Chiu, S.T.: A comparative Review of Bandwidth Selection for Kernel Density Estimation. Statistica Sinica 6, 129–145 (1996)

    MATH  MathSciNet  Google Scholar 

  28. Devroye, L., Lugosi, T.: Variable Kernel Estimates: on the Impossibility of Tuning the Parameters. In: Giné, E., Mason, D. (eds.) High-Dimensional Probability. Springer, New York (1994)

    Google Scholar 

  29. Murgante, B., Las Casas, G., Danese, M.: Where are the slums? New approaches to urban regeneration. In: Liu, H., Salerno, J., Young, M. (eds.) Social Computing, Behavioral Modeling and Prediction, pp. 176–187. Springer US, Heidelberg (2008)

    Chapter  Google Scholar 

  30. Brunsdon, C.: Estimating Probability Surfaces for Geographical Points Data: An Adaptive Kernel Algorithm. Computers and Geosciences, 21 7, 877–894 (1995)

    Article  Google Scholar 

  31. Abramson, L.S.: On Bandwidth Variation in Kernel Estimates - A Square Root Law. Ann. Stat. 10, 1217–1223 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  32. Breiman, L., Meisel, W., Purcell, E.: Variable Kernel Estimates of Multivariate Densities. Technometrics 19, 135–144 (1977)

    Article  MATH  Google Scholar 

  33. Hall, P., Marron, J.S.: On the Amount of Noise Inherent in Band-Width Selection for a Kernel Density Estimator. The Annals of Statistics 15, 163–181 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  34. Hall, P., Hu, T.C., Marron, J.S.: Improved Variable Window Kernel Estimates of Probability Densities. Ann. Statist. 23, 1–10 (1994)

    Article  MathSciNet  Google Scholar 

  35. Sain, S.R., Scott, D.W.: On Locally Adaptive Density Estimation. J. Amer. Statist. Assoc. 91, 1525–1534 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  36. Wu, C.O.: A Cross-Validation Bandwidth Choice for Kernel Density Estimates with Selection Biased Data. Journal of Multivariate Analysis 61, 38–60 (1999)

    Article  Google Scholar 

  37. Härdle, W.: Smoothing Techniques with Implementation in S. Springer, New York (1991)

    MATH  Google Scholar 

  38. Bowman, A.W., Azzalini, A.: Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S - Plus Illustrations. Oxford Science Publications, Oxford University Press, Oxford (1997)

    MATH  Google Scholar 

  39. Murgante, B., Las Casas, G., Danese, M.: The periurban city: Geo-statistical methods for its definition. In: Coors, Rumor, Fendel, Zlatanova (eds.) Urban and Regional Data Management, pp. 473–485. Taylor & Francis Group, London (2008)

    Google Scholar 

  40. Chainey, S., Reid, S., Stuart, N.: When is a Hotspot a Hotspot? A Procedure for Creating Statistically Robust Hotspot Maps of Crime. In: Kidner, D., Higgs, G., White, S. (eds.) Innovations in GIS 9: Socio-Economic Applications of Geographic Information Science, pp. 21–36. Taylor and Francis, Abington (2002)

    Google Scholar 

  41. Moran, P.: The interpretation of statistical maps. Journal of the Royal Statistical Society 10, 243–251 (1948)

    MATH  Google Scholar 

  42. Anselin, L.: Local Indicators of Spatial Association – LISA. Geographical Analysis 27, 93–115 (1995)

    Google Scholar 

  43. Getis, A., Ord, J.K.: The analysis of spatial association by use of distance statistics. Geographical Analysis 24, 189–206 (1992)

    Google Scholar 

  44. Gizzi, F.T., Lazzari, M., Masini, N., Zotta, C., Danese, M.: Geological-Geophysical and Historical-Macroseismic Data Implemented in a Geodatabase: a GIS Integrated Approach for Seismic Microzonation. The Case-Study of Potenza Urban Area (Southern Italy). Geophysical Research Abstracts 9(09522), SRef-ID: 1607-7962/gra/EGU2007-A-09522 (2007)

    Google Scholar 

  45. Grünthal, G.G.: European Macroseismic Scale 1998. In: Conseil de l’Europe Cahiers du Centre Européen de Géodynamique et de Séisomologie, Luxembourg, vol. 15 (1998)

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maurizio Lazzari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Beniamino Murgante
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

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

Download citation

  • 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

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation