## 1 Introduction
The use of spatial and temporal statistical techniques, like the Average Nearest Neighbor (ANN) analysis [18], is crucial for understanding the spatial distribution of seismic events. By evaluating the average distance between neighboring earthquakes, ANN helps in identifying whether the seismicity is clustered, dispersed, or randomly distributed. This information is crucial for understanding the underlying tectonic processes and for assessing the likelihood of future seismic events in the region. Quadrat Count Analysis (QCA) [19] further complements ANN. This method involves dividing the study area into equal-sized quadrats and counting the number of earthquakes within each. By comparing these counts with a theoretical random distribution, patterns in the spatial distribution of seismicity could be discerned. This technique is particularly useful in the Red Sea region for identifying zones of heightened seismic activity, which may correlate with fault lines. Global Moran's I (GMI) [20] is a measure of spatial autocorrelation that quantifies how seismic events are related to each other over the entire study area. In the Red Sea, a high GMI value indicates a clustered pattern, which suggests that these seismic events are part of a distributed seismicity, influenced by underlying tectonic processes, rather than occurring in an entirely random manner. This global perspective is essential for a comprehensive understanding of the region's seismicity. Complementing GMI, Local Moran's I (LMI) [14; 20] provides a more detailed perspective with precise localized clusters of seismic activity. This technique is particularly useful for pinpointing areas within the Red Sea region where earthquake activity is significantly higher or lower than average. LMI can reveal localized seismic hotspots or areas of heightened seismic activity. Integrating results from these methods enhances our understanding of the region's complex tectonic dynamics. This understanding is invaluable for developing more effective earthquake monitoring, hazard assessment, and mitigation strategies, ultimately contributing to the safety and resilience of the affected regions. This study explores the transformative role of advanced spatial and temporal statistical techniques in analyzing seismic behavior in the Red Sea region. Focused on small-to-moderate-magnitude earthquakes, which are predominant in this area, our research aims to deepen the understanding of seismic patterns and trends. This enhanced knowledge is critical for assessing and mitigating future seismic hazards or risks. This article aims to pinpoint the spatial localization of seismic hotspots with unprecedented granularity. It demonstrates how methods such as Average Nearest Neighbor (ANN), Quadrat Count Analysis, Global Moran's I (GMI), and Local Moran's I (LMI) can be effectively employed to analyze and interpret the spatial and temporal patterns of seismic events in this tectonically active rift system. The adoption of these techniques serves to elucidate the intricate inner mechanisms that underlie seismic phenomena in the Red Sea area. The Average Nearest Neighbor facilitates the examination of clustering patterns, Quadrat Count Analysis provides insights into the distribution of seismic events, while Global and Local Moran's I offer a robust assessment of spatial autocorrelation, shedding light on the interconnectivity and localized variations within seismic data. By achieving this, this article seeks to contribute to a broader understanding of the region's seismic dynamics, emphasizing the correlation between seismic patterns and underlying geological structures. Additionally, the goals include highlighting the importance of these statistical techniques in enhancing earthquake monitoring and hazard assessment, thus aiding in the development of more robust earthquake preparedness and mitigation strategies.