Social vulnerability plays a vital role in understanding how various societal characteristics influence communities during extreme events. This study aimed to systematically identify key indicators and methodological approaches used in Social Vulnerability Index (SVI) research by utilizing the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The study specifically addresses gaps in indicator selection and emphasizes incorporating land-related and diverse variables for improved applications across various contexts. Social variables essential for SVI development were collected, and their applications across studies were analyzed. A total of 30,443 articles were identified from multiple databases, with 72 meeting the inclusion criteria after rigorous evaluation. Key aspects such as methodologies, weighting schemes, and primary variables used in SVI computation were outlined. Principal component analysis emerged as the most commonly employed method, though diverse approaches have gained traction in recent years. Significant variability was observed in the variables across studies, with demographic indicators being the most frequently utilized. The identified variables were categorized into 21 domains, comprising 61 indicators. While the findings of this study focus on improving the understanding of SVI development and its diverse applications, they also hold potential for informing sustainable land management and disaster resilience strategies, particularly in tailoring interventions to region-specific vulnerabilities.

The accurate assessment of landslide hazards is important in order to reduce the casualties and damage caused by landslides. Landslide hazard assessment combines the evaluation of spatial and temporal probabilities. Although various statistical approaches have been used to estimate spatial probability, these methods only evaluate the statistical relationships between factors that have triggered landslides in the past rather than the slope failure process. Therefore, a physically based approach with probabilistic analysis was adopted here to estimate the spatial distribution of landslide probability. Meanwhile, few studies have addressed temporal probability because historical records of landslides are not available for most areas of the world. Therefore, an indirect approach based on rainfall frequency and using extreme value analysis and the Gumbel distribution is proposed and used in this study. In addition, to incorporate the nonstationary characteristics of rainfall data, an expanding window approach was used to evaluate changes in the mean annual maximum rainfall and the location and scale parameters of the Gumbel distribution. Using this approach, the temporal probabilities of future landslides were estimated and integrated with spatial probabilities to assess and map landslide hazards.