# Relationship between landslide susceptibility and social lag in Mexico City: The case of the west periphery

## TL;DR

This study explores how landslide risks and social deprivation are linked in Mexico City's west periphery using machine learning and statistical models.

## Contribution

The novel integration of landslide susceptibility and social lag using Copula models and SHAP values in a machine learning framework.

## Key findings

- High social lag and landslide susceptibility co-occur in steep, deprived areas.
- Educational deprivation is the top vulnerability factor linked to landslide risks.
- Spatial inequities suggest the need for combined slope stabilization and social policies.

## Abstract

Landslides threaten sustainable development through economic and human losses. This study integrates machine learning methods to construct susceptibility maps, including topographic-hydrological indicators, to improve the inclusion of earthflow landslides. Furthermore, we aim to find relationships between landslide susceptibility and social lag using Copula models and SHAP values. Results reveal differentiated dependence across different partitions. Specifically, we found regime-specific co-occurrences of high social lag and high landslide susceptibility areas in steep, deprived areas, contrasting resilient affluent zones. Educational deprivation emerges as the top vulnerability factor, followed by healthcare access, overcrowding, and housing deficits. Highlighting spatial inequities, the analysis advocates targeted interventions blending slope stabilization and social policies.