Cluster Analysis of Neighborhood-Level Earthquake Risk Profiles in Istanbul: A Data-Driven Approach to a Magnitude 7.5 Mw Scenario

Urban seismic risk assessments in Istanbul have predominantly focused on district level loss estimates, even though mitigation and emergency response decisions are implemented at much finer administrative units. This study develops a neighborhood-based classification of earthquake risk for all 959 neighborhoods under a deterministic Mw 7.5 scenario. The analysis relies on the official Istanbul Earthquake Loss Estimation Update dataset prepared by Istanbul Metropolitan Municipality in cooperation with the Kandilli Observatory. Eight scenario-based outcome indicators, including four structural damage and four human impact variables, are first transformed using Yeo-Johnson and Min-Max scaling and then reduced through Principal Component Analysis, which explains 96.3 percent of the total variance in two components. Within this reduced space, K-Means, K-Medoids, Gaussian Mixture Models, Spectral Clustering, and HDBSCAN are systematically compared. Model selection is guided by internal validation criteria, the Gap Statistic, and bootstrap stability analysis. Based on this combined assessment, K-Medoids with k equal to 2 emerges as the most parsimonious and stable clustering solution. The resulting High Impact and Low Impact profiles show statistically significant differences across all indicators and remain highly consistent across 300 bootstrap resamples, with a mean Adjusted Rand Index of 0.976. The identified medoid neighborhoods provide concrete reference cases for targeted planning interventions. Spatially, higher impact areas are concentrated along the Marmara coastal corridor and older urban cores, whereas lower impact neighborhoods are more common in northern districts. By converting detailed scenario outputs into stable neighborhood level risk categories, the study provides a structured basis for prioritizing mitigation investments, preparedness actions, and emergency response planning at the local scale.