Persistent Spatio-Temporal Outage Hotspot Detection for Infrastructure Resilience Planning

Abstract

Extreme weather events are producing persistent geographic patterns of power-grid disruption across the United States, yet outage hotspot detection and infrastructure cascade modeling are often studied separately. This paper presents a data-driven geospatial framework that links persistent outage vulnerability with downstream cascade impact in interdependent power-communication networks. Using a national outage dataset from 2015-2023, we introduce the Hotspot Persistence Index (HPI), a severity-aware metric for identifying counties that repeatedly emerge as outage hotspots over time. We then apply a multi-scale DBSCAN refinement procedure to convert persistent county-level hotspots into geographically interpretable regional failure scenarios characterized by recurrence, severity, and spatial extent. To evaluate their system-level relevance, these empirically derived scenarios are injected into the Modified Implicative Interdependency Model (MIIM), which captures cascading behavior across coupled power and communication layers. Results show that three persistent regional clusters account for 54.4% of total HPI-weighted cascade impact, while communication-layer entities fail at 2.5X the rate of power buses under high-persistence scenarios. HPI-guided hardening reprioritizes protection candidates relative to a degree- and betweenness-centrality baseline, identifying high-value buses that topology-only rankings overlook. These results demonstrate how persistent geospatial outage patterns can support targeted and empirically grounded infrastructure resilience planning.