DOI: 10.5061/dryad.1jwstqk42
title: Data from: Using network science to evaluate vulnerability of landslides on Big Sur Coast, California, USA

description: 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.
description: Open-source code R is used to create the networks and Matlab to
 run the community detection algorithm. The code can be found on https://github.com/vddesai-97/networkLandslide.git, which uses the community detection algorithim from https://github.com/GenLouvain/GenLouvain. The dataset contains 17 sub-regions with corresponding edge lists, spatial grids, and edge weights. In addition, the interferogram list for the InSAR data used for analysis and the shapefiles for the 44 landslide polygons are included.
description: # Data from: Using network science to evaluate vulnerability of landslides
 on Big Sur Coast, California, USA
 [https://doi.org/10.5061/dryad.1jwstqk42](https://doi.org/10.5061/dryad.1jwstqk42) This folder contains edge lists, spatial coordinates, shapefiles, and multilayer weighted edges for the 17 sub-regions used in the paper. There are two components to the data -- the spatial coordinates of each node, which represents a patch of area, and then the edge list which identifies node pairs that are connected based on nearest neighbors. Information about the system, such as velocity and slope, is then connected to the edges as weights, which is stored as an matrix where each row represents an edge, the first two columns represents the node pairs, and then the preceding columns represent information for each time layer within the dataset corresponding to the interferogram list. There are 284 InSAR time slices, and the corresponding combinations of SAR images used are included in the interferogram list. In addition,  the shape file of the 44 landslide polygons is included. ## Description of the data and file structure 'Coords': Each row represents a node, with 'ID' corresponding to the edgelist and [x,y] coordinates in utm, or meters. (N X 3 matrix) 'EdgeList': Each row represents an edge with [node A, node B] pairing, where the ID corresponds to the row in 'Coords'. (E X 2 matrix) 'VelocityWeightedEdges': Each row represents an edge; where 2+t columns represents the velocity weights for time layer <i>t</i>. (E X (2+T) matrix) 'SlopeWeightedEdges': Each row represents an edge; where column 3 represents the slope for the edge. (E X 3 matrix) 'InterferogramList: Combinations of SAR satellite images that lead to the InSAR time slice used in analysis. 'InSAR': The first column is the layer number corresponding to the multilayer network, and the second column is the corresponding InSAR time slide date. 'Landslides': Folder containing the shapefiles for the 44 slow-moving landslides. 'SubRegions': Folder containing the shapefiles for the 17 sub-regions. ## Sharing/Access information On-Demand InSAR products were downloaded via Alaska Satellite Facility's HyP3 platform, availabe at \url{[https://hyp3-docs.asf.alaska.edu](https://hyp3-docs.asf.alaska.edu/)}.  Copernicus DEMs, used to derive the slope weights, are available at \url{[https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model](https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model)}. ## Code/Software The code used to create the multilayer network is found on [https://github.com/vddesai-97/networkLandslide](\url{https://github.com/vddesai-97/networkLandslide}), and the code for running the community detection is [https://github.com/GenLouvain/GenLouvain](https://github.com/GenLouvain/GenLouvain).  The Miami INsar Time-Series software in PYthon (MintPy) is available at \url{[https://github.com/insarlab/MintPy](https://github.com/insarlab/MintPy)}.