Spatio-temporal cluster analyses of landslides in Italy at national and regional scale
Authors: Tonini Marj , Romailler Kim , Pecoraro Gaetano , Calvello Michele 
Published: March 2020

&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; Landslides, FraneItalia, cluster analysis, spatio-temporal point process&lt;/p&gt;&lt;p&gt;In Italy landslides pose a significant and widespread risk, resulting in a large number of casualties and huge economic losses. Landslide inventories are critical to support investigations of where and when landslides have happened and may occur in the future, i.e. to establish reliable correlations between triggering factors and landslide occurrences. To deal with this issue, statistical methods originally developed for spatio-temporal stochastic point processes can be useful for identifying correlations between events in space and time and detecting a significant excess of cases within large landslide datasets.&lt;/p&gt;&lt;p&gt;In the present study, the authors propose an approach to analyze and visualize spatio-temporal clusters of landslides occurred in Italy in the period 2010-2017, considering the weather warning zones as territorial units. Besides, a regional analysis was conducted in Campania region considering the municipalities as territorial units. Data on landslide occurrences derived from the FraneItalia catalog, an inventory retrieved from online Italian news. The database contains 8931 landslides, grouped in 4231 single events and 938 areal events (records referring to multiple landslides triggered by the same cause in the same geographic area). Analyses were performed both annually, considering each year individually, and globally, considering the entire frame period. We applied the spatio-temporal scan statistics permutation model (STPSS, integrated in SaTScan&lt;sup&gt;TM&lt;/sup&gt; software), which allowed detecting clusters&amp;#8217; location and estimating their statistical significance. STPSS is based on cylindrical moving windows which scan the area across the space and in time counting the number of observed and expected occurrences and computing the likelihood ratio. The statistical inference (p-value) is evaluated by Monte Carlo sampling and finally the most likely clusters in the real and randomly generated datasets are compared.&lt;/p&gt;&lt;p&gt;Although more detailed analyses are required for the determination of cause-effect relationships among landslides and other variables, some relations with the local topographic and meteorological conditions can already be argued. At national scale, spatio-temporal clusters of landslides are mainly recurrent in two zones: the area enclosing Liguria Region &amp;#8211; Northern Tuscany at north-west and the area between Abruzzo and Molise regions at centre-east. During the year, landslide clusters are particularly abundant between October and March. as most of the events in the FraneItalia catalog are rainfall-induced, strongly influenced by seasonal rainfall patterns. Concerning the regional analysis, most of the clusters are located in the Lattari mountains, the Pizzo d&amp;#8217;Alvano massif and the Picentini mountains, areas highly susceptible to landslide occurrence due to geomorphological factors.&lt;/p&gt;&lt;p&gt;In conclusion, the application of spatio-temporal cluster analysis at various scale allowed the identification of frame periods with greater landslide activity. The question of whether this increase in activity depends climate conditions or topographic factors is still open and request further investigations.&lt;/p&gt;&lt;p&gt;REFERENCES&lt;/p&gt;&lt;p&gt;Calvello, M., Pecoraro, G. FraneItalia: a catalog of recent Italian landslides. &lt;em&gt;Geoenvironmental Disasters&lt;/em&gt;. 5: 13 (2018)&lt;/p&gt;&lt;p&gt;Tonini, M. &amp; Cama, M. Spatio-temporal pattern distribution of landslides causing damage in Switzerland. &lt;em&gt;Landslides&lt;/em&gt; 16 (2019)&lt;/p&gt;