This study focuses on landslide susceptibility
mapping in the Daunia area (Apulian Apennines, Italy) and
achieves this by using a multivariate statistical method and
data processing in a Geographical Information System (GIS).
The Logistic Regression (hereafter LR) method was chosen
to produce a susceptibility map over an area of 130 000 ha
where small settlements are historically threatened by landslide
phenomena. By means of LR analysis, the tendency to
landslide occurrences was, therefore, assessed by relating a
landslide inventory (dependent variable) to a series of causal
factors (independent variables) which were managed in the
GIS, while the statistical analyses were performed by means
of the SPSS (Statistical Package for the Social Sciences) software.
The LR analysis produced a reliable susceptibility map
of the investigated area and the probability level of landslide
occurrence was ranked in four classes. The overall performance
achieved by the LR analysis was assessed by local
comparison between the expected susceptibility and an independent
dataset extrapolated from the landslide inventory.
Of the samples classified as susceptible to landslide occurrences,
85% correspond to areas where landslide phenomena
have actually occurred. In addition, the consideration of
the regression coefficients provided by the analysis demonstrated
that a major role is played by the “land cover” and
“lithology” causal factors in determining the occurrence and
distribution of landslide phenomena in the Apulian Apennines
This study focuses on landslide susceptibilitymapping in the Daunia area (Apulian Apennines, Italy) andachieves this by using a multivariate statistical method anddata processing in a Geographical Information System (GIS).The Logistic Regression (hereafter LR) method was chosento produce a susceptibility map over an area of 130 000 hawhere small settlements are historically threatened by landslidephenomena. By means of LR analysis, the tendency tolandslide occurrences was, therefore, assessed by relating alandslide inventory (dependent variable) to a series of causalfactors (independent variables) which were managed in theGIS, while the statistical analyses were performed by meansof the SPSS (Statistical Package for the Social Sciences) software.The LR analysis produced a reliable susceptibility mapof the investigated area and the probability level of landslideoccurrence was ranked in four classes. The overall performanceachieved by the LR analysis was assessed by localcomparison between the expected susceptibility and an independentdataset extrapolated from the landslide inventory.Of the samples classified as susceptible to landslide occurrences,85% correspond to areas where landslide phenomenahave actually occurred. In addition, the consideration ofthe regression coefficients provided by the analysis demonstratedthat a major role is played by the “land cover” and“lithology” causal factors in determining the occurrence anddistribution of landslide phenomena in the Apulian Apennines
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