Studying the rates, characteristics, causes, and consequences of road traffic accidents (RTAs) has become a focal point for safety professionals and transportation agencies worldwide. Recent research efforts have explored various methodologies to attain more precise results concerning the spatial and temporal trends of RTAs. Among these, geographic information systems (GIS) have emerged as a widely adopted approach for analyzing RTAs, enabling the examination of their spatial and temporal distributions. The primary objective of this study is to introduce a GIS-based approach for the comprehensive analysis of the spatial and temporal distribution of RTAs. Additionally, this research endeavors to pinpoint accident-prone areas, commonly referred to as 'hotspots,' and high-density crash clusters. This was achieved this through spatial autocorrelation analysis, incorporating techniques such as inverse distance weighting, Moran's Index, and the Getis Ord Gi* Statistics tool, all applied within ArcGIS Pro 3.0.3. By focusing on eight years of crash data (2015-2022) in the Sharjah emirate of the United Arab Emirates, this study contributes to a deeper understanding of the distribution of traffic accidents. The findings underscore the effectiveness of the analytical methods employed, which can be harnessed for the identification and prioritization of crash hotspots.