Two Decades of Geospatial Evolution: Tracing the Analytical Journey towards Data-Driven Road Crash Prevention

Globally, road crashes impose massive burdens, and understanding the complex factors influencing crash patterns requires advanced analytical methods. This study reviews 20 years of published literature on geospatial crash analysis to chart key innovations shaping this critical field. The analysis reveals a progression from fundamental mapping approaches towards integrative predictive modelling and dynamic risk monitoring leveraging diverse data sources. While crash records and road networks remain core datasets, aerial imagery, social media, weather, infrastructure attributes, and traffic data have become increasingly incorporated. Techniques have evolved from hotspot analysis to sophisticated machine learning algorithms automating crash prediction and factor analysis. Research objectives now emphasize not just pattern identification but predictive risk modelling, and there is an increased focus on prevention through improved emergency response, infrastructure enhancements, and targeted countermeasures. Interactive 3D visualizations and virtual reality applications are enhancing geospatial communication and decision-making. As geospatial innovations and data integration accelerate, this continuously advancing field holds tremendous potential to guide proactive evidence-based road safety planning. However, validating analysis approaches and assessing geographic transferability remain critical research needs. By synthesizing two decades of developments, this study provides key perspectives to harness geospatial technology innovations and unlock new frontiers in data-driven road crash prevention worldwide.