The present study addresses the pattern distribution of recent landslides in Italy. The main objective is the detection and mapping of spatio-temporal clusters of landslides that occurred in the period 2010–2017 in the country. To this aim, a subdivision of the national area into 158 warning zones, as identified by the 21 civil protection regional centres to deal with weather-induced hydro-geological hazards, is adopted. Information on landslides comes from FraneItalia, a geo-referenced catalogue developed consulting online news sources. Analyses are performed both at national scale and at a regional scale, focusing on the Campania region. The space–time permutation scan statistics model is applied to detect statistically significant clustering, accounting for the geographical spatial dimension and for the temporal dimension. Two types of analyses are performed: annual, considering each single year; and multi-annual, encompassing the entire 8-year study period. In both cases, spatio-temporal cluster analyses are able to detect areas and frame-periods characterised by relevant and recurrent landslide activity. Finally, the obtained results are compared with a standard landslide density map, highlighting the complementarity of the two approaches.

Spatio-temporal cluster analysis of recent Italian landslides

Pecoraro G.
;
Calvello M.
2020-01-01

Abstract

The present study addresses the pattern distribution of recent landslides in Italy. The main objective is the detection and mapping of spatio-temporal clusters of landslides that occurred in the period 2010–2017 in the country. To this aim, a subdivision of the national area into 158 warning zones, as identified by the 21 civil protection regional centres to deal with weather-induced hydro-geological hazards, is adopted. Information on landslides comes from FraneItalia, a geo-referenced catalogue developed consulting online news sources. Analyses are performed both at national scale and at a regional scale, focusing on the Campania region. The space–time permutation scan statistics model is applied to detect statistically significant clustering, accounting for the geographical spatial dimension and for the temporal dimension. Two types of analyses are performed: annual, considering each single year; and multi-annual, encompassing the entire 8-year study period. In both cases, spatio-temporal cluster analyses are able to detect areas and frame-periods characterised by relevant and recurrent landslide activity. Finally, the obtained results are compared with a standard landslide density map, highlighting the complementarity of the two approaches.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4757253
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