Spatio-Temporal Graph-Based Hotspot Analysis of Earthquake Events Using Spatial Autocorrelation and Community Detection in Indonesia - Ika Arfiani; Herman Yuliansyah; Nur Rochmah Dyah Puji Astuti; Arfiani Nur Khusna (Journal of Information System and Informatics, 2026)
Analysis of clustered seismic regions is important for understanding seismic activity patterns in tectonic regions such as Indonesia. However, conventional spatial statistical approaches generally analyze earthquake events independently and fail to capture complex spatio-temporal relationships. This study proposes a graph-based spatio-temporal hotspot analysis approach integrating spatial autocorrelation and community detection to identify regional seismic interaction patterns. The dataset used consists of 3,000 earthquake events from 2008–2025. Spatial autocorrelation was analyzed using Moran’s I, while earthquake relationships were modeled using a spatio-temporal graph with spatial and temporal thresholds of ≤400 km and ≤60 days. The results showed significant positive spatial autocorrelation with Moran’s I = 0.3367 (p = 0.001). The resulting graph consisted of 3,000 nodes and 22,896 edges, revealing substantial regional-scale connectivity and 14 major clusters with a modularity score of 0.7405, indicating a strong community structure. Degree centrality analysis identified highly connected nodes with a maximum degree of 77. These findings indicate that integrating spatial autocorrelation and graph analysis provides a more comprehensive representation of seismic interaction patterns and may support future seismic risk assessment in tectonically active regions.