A Bi-Dimensional Identification Model of Seismic Vulnerability and Intelligent Sensing Capability: A Case of Seoul

Abstract

Seismic disasters pose increasingly complex risks to large metropolitan areas. Identifying spatial disparities between seismic vulnerability and governance capacity has become a critical issue for enhancing urban resilience. This study proposes a bi-dimensional identification model integrating seismic vulnerability and intelligent sensing capability, using the 25 districts of Seoul as a case. A comprehensive three-level indicator system of seismic vulnerability was developed. Simultaneously, the spatial density of S-DoT sensors, representing the city’s intelligent sensing infrastructure, was adopted as a proxy for district-level sensing capability. Methodologically, spatial data processing was conducted using ArcGIS Pro, while Z-score standardization and K-means clustering were performed in Python. To ensure the scientific rigor of the numerical model, a multi-scenario sensitivity analysis was conducted to evaluate the impact of multicollinearity among indicators. The clustering stability was further validated through the Adjusted Rand Index (ARI) and centroid shift metrics, and cross-checked with hierarchical clustering, confirming consistent typological structures. The results identified three types: (1) high vulnerability–low sensing, (2) moderate to low vulnerability–high sensing, and (3) moderate to low vulnerability–low sensing. These types exhibited distinct spatial clustering patterns and imply differentiated governance priorities and responses. The primary contribution of this research lies in introducing a spatially coupled model that integrates intelligent sensing into seismic vulnerability assessment. This approach moves beyond traditional static, one-dimensional frameworks, offering improved visual interpretability and decision support. The findings offer insights for resilienceoriented governance in Seoul and other high-density cities.OPEN ACCESS Received: 11/10/2025 Accepted: 20/01/2026

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