Community detection in spatial correlation graphs: Application to non-stationary ground motion modeling

In this paper, we propose a community detection method to find regions in spatial data with higher correlations. We construct ‘correlation deviation graph’ that considers the influence of global correlation caused by node's geographical location. compare performance graph conventional construction approaches for detection, using synthetic data, and show proposed has best presence structure. also signed spectral clustering Louvain algorithm on graphs, performs best. apply our algorithms simulated earthquake ground motion Los Angeles region. The results suggest communities high shaking tend be associated common geological conditions relative location along rupture strike direction.