A Statistical Exploratory Analysis of Inventoried Slide-Type Movements for South Tyrol (Italy)
Steger, Stefan, Mair, Volkmar, Kofler, Christian, Pittore, Massimiliano, Zebisch, Marc and Schneiderbauer, Stefan, "A Statistical Exploratory Analysis of Inventoried Slide-Type Movements for South Tyrol (Italy)" in Understanding and Reducing Landslide Disaster Risk ed. Guzzetti, Fausto, Snježana, Arbanas Mihalić, Reichenbach, Paola, Sassa, Kyoji, Bobrowsky, Peter T. and Takara, Kaoru (Cham: Springer Nature Switzerland AG, 2020), 305-311.
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Author Steger, Stefan
Mair, Volkmar
Kofler, Christian
Pittore, Massimiliano
Zebisch, Marc
Schneiderbauer, StefanBook Editor Guzzetti, Fausto
Snježana, Arbanas Mihalić
Reichenbach, Paola
Sassa, Kyoji
Bobrowsky, Peter T.
Takara, KaoruChapter Title A Statistical Exploratory Analysis of Inventoried Slide-Type Movements for South Tyrol (Italy) Book Title Understanding and Reducing Landslide Disaster Risk Publication Date 2020-12-23 Place of Publication Cham Publisher Springer Nature Switzerland AG Start page 305 End page 311 Language eng Abstract Landslides of the slide-type movement represent common damaging phenomena in the Italian province of South Tyrol. Up to January 2019, the landslide inventory of the province lists 1928 accurately mapped landslides that required intervention by e.g. the local road service or the provincial geological survey. Thus, this landslide data set mainly includes events that caused damage. The aim of this contribution was to investigate and critically interpret statistical associations between the inventoried slide-type movements and a variety of spatial environmental variables. The assessment of conditional frequencies and the discriminatory power of single variables revealed conditions that are typically present at landslide mapping locations, e.g. topography, land cover, rock types, and proximity to infrastructure. A critical interpretation of thestatistical results highlighted the need to consider the landslide data origin (i.e. background information) in order to avoid misleading statements and wrong inferences. The findings of the here presented work show that the availability of detailed landslide information does not always ensure that valid process-related conclusions can be drawn from subsequent statistical analyses (e.g. identification of important landslide controls). Despite considerable methodical advancements in the field of statistical data analysis and machine learning, we conclude that the principle ‘correlation does not necessarily imply (geomorphic) causation’ remains of particular relevance. Keyword Landslide inventory
South Tyrol
Exploratory data analysis
IFFI sampling bias
SusceptibilityCopyright Holder Springer Nature Switzerland AG Copyright Year 2021 Copyright type All rights reserved ISBN 9783030602260
9783030602277DOI 10.1007/978-3-030-60227-7_34 -
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