danese murgante lazzari risk

Kernel Density Estimation Methods for a Geostatistical Approach in Seismic Risk Analysis: the Case Study of Potenza Hilltop Town (southern Italy)

Maria Danese*, **, Maurizio Lazzari*, Beniamino Murgante**

* National Counsil of Research Archaeological and Monumental Heritage Institute,

**Università degli Studi della Basilicata,Dipartimento di Architettura, Pianificazione ed

Infrastrutture di Trasporto

International Conference on Computational Science and Its Applications (ICCSA 2008) - 30June -3July 2008 - Perugia, Italy

Maria Danese, Maurizio Lazzari, Beniamino Murgante - Maria Danese, Maurizio Lazzari, Beniamino Murgante - Kernel Density Estimation Methods for a Kernel Density Estimation Methods for a Geostatistical Approach in Seismic Risk Analysis: the Case Study of Potenza Hilltop Town (southern Italy)Geostatistical Approach in Seismic Risk Analysis: the Case Study of Potenza Hilltop Town (southern Italy)

The problem: the seismic approach

Program of prevision, prevention and protection

Seismic event consequences evaluation and

damages quantification

Analysis of seismic damage scenarios


Analysis of seismic damage scenarios: instruments

European Macroseismic Macroscale 1998

Vulnerability classes

Damage levels

Historical macroseismic scenarios

Relationships between macroseismic intensity

and damage levels


Seismogeological effects

Mutual pounding of adjacent buildings

Analysis of seismic damage scenarios: limits

Factors that needs a better evaluation

“The increase in shaking due to soil amplification or topographical conditions is part of the effects that intensity is a record of, and part of the hazard to which the built environment is exposed” *

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

Importance of the spatial component in seismic damage scenarios


Methods: the Kernel Density Estimation (KDE)

)A ..,,A ,A ,y ,(xL n21iii

n

i

issk

ss

12

)(1

)(

1)(ˆ


KDE: intensity“3rd dimension” of the distribution

Measure identifying event strength

Connected to the case study nature


KDE: intensity and its measures

First order effects(Absolute location)

Second order effects(Relative location)

ji

ji

dsdsji dsds

dsYdsYEss

ji

))()((lim),(

0,

Properties of a spatial distribution*

*Gatrell et al. (1996)


KDE: kernel functions

. 1)( dxxk


KDE: grid resolution


KDE: bandwidth

Smaller Bandwidth (Levine, 1999)

Greater Bandwidth (Levine, 1999)

Relation between the bandwidth dimension and the study area


KDE: bandwidth approaches


KDE: a method for the right choose of bandwidth

Nearest-Neighbor Index

rand

dNNI min n

ssd

d

n

iji

1min

min

),(

5.0n

Adran

Nearest-Neighbor Expected Distance

NNI > 1 observed distance is higher than the expected distance; events are more scattered than expected.

NNI < 1 observed distance is smaller than expected distance

Nearest-Neighbor Observed Distance


KDE: classification of results


The case of study: Potenza hilltop town1857

1930 1980

Over-consolidated clayey substratum

Sandy-conglomerate deposit lays

Narrow asymmetrical

ridge


The case of study: Potenza hilltop town


The case of study: parameters selection

Intensity choice

4. Building as autonomous scale (EMS98)

9. Relationship within buildings through reciprocal proximity and morphological factors



Intensity choice

65D5

54D4

33D3

22D2

11D1

Intensity in northern sector

Intensity in middle-southern sectorDamage level



Kernel choice

Cell size choice 0.1m



6.8Nearest neighbour mean calculated for whole point pattern

Fixed for whole point pattern1

KD mapt (m)Methods used to estimate t Bandwidth approach

Case

Bandwidth choice



Sum of two resultant raster

3.9

6.8

D1-2-3 damage level: average of building’s minimum semi-dimension.

D4-5 damage level: nearest neighbour meancalculated for whole point pattern.

Two different fixed bandwidths2


Case

Bandwidth choice



Sum of two resultant raster

1.4÷9.9

6.8

4.1

D1-2-3 damage level: building’s minimum semi-Dimension

D4-5 damage level:Building’s area mean + sd nearest neighbour ≤mean calculated for whole point pattern

Building’s area > mean + sd nearest neighbour mean calculated for whole point pattern multipliedby correction.

One KDE with Fixed method, one with Adaptive method

3


Case

Bandwidth choice


Results


Conclusions and future developments•The use of geostatistics to process historical macro-seismic data is a new field of application of these techniques and represents a new approach to territorial analysis.

•Developments of the spatial analysisConsidering more parameters that can influence the result

•Fields of applicationPossibility to define urban areas historically most exposed to seismic risk, achieving useful knowledge bases for emergency planning in case of earthquakes. This work could be also a good basis for Civil Defence Plan re-examination concerning the definition of waiting and refuge areas and strategic points of entrance to old town centre.

Possibility to define monumental heritage and strategic buildings that could be most exposed to seismic risk

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