Data from: Using network science to evaluate vulnerability of landslides on Big Sur Coast, California, USA

Landslide events, ranging from slips to catastrophic failures, pose
significant challenges for prediction. In this study, a physically
inspired framework is employed to assess landslide vulnerability at a
regional scale (Big Sur Coast, California). Our approach integrates
techniques from the study of complex systems combined with multivariate
statistical analysis to identify unstable areas vulnerable to landslide
events. We successfully apply a technique originally developed on the 2017
Mud Creek landslide, Big Sur, and refine our statistical metrics to
characterize landslide vulnerability within a larger geographical area.
Our results successfully classify four landslide events that occurred in
the winter year of 2022-2023 as areas that are vulnerable to slope
failure. The performance of our methods is compared to factors such as
landslide location, slope, cumulative displacement, precipitation, and
InSAR coherence, via a multivariate statistical analysis. We conclude that
our network analyses, which provide a natural way to incorporate
spatiotemporal dynamics, perform better as a monitoring technique than
traditional methods. Our method has the potential for monitoring multiple
landslide sites in real time, and evaluating which landslide sites are
more vulnerable.