## 4 Discussion
Additionally, small but consistent effects can be important in large-scale analyses where even subtle trends may influence decision-making. Even at this preliminary stage, before applying advanced prognostic methods, the dataset offers valuable insights for road safety interventions. By integrating previously disparate data sources, it provides comprehensive contextual information that was unavailable when analyzing these sources separately. The weather-incident correlations can already inform targeted safety campaigns, suggesting enhanced monitoring during warmer periods when incident rates increase as well as improved driver education about visibility challenges in varying humidity conditions. The strong association between vehicle fleet composition and accident totals points to the need for enhanced vehicle safety standards and inspection programs, while the relationship between road infrastructure quality and fatalities suggests that improvement projects should prioritize roads with high fatality rates rather than high accident frequency alone. These differentiated approaches could help authorities allocate limited resources more effectively to reduce both accident numbers and their human cost. Despite efforts to enhance spatial precision through kriging with elevation drift, certain challenges persist due to the inherent nature of meteorological data and the scale of socioeconomic indicators. In regions with sparse station coverage, interpolation may fail to capture localized weather events such as sudden downpours, fog formation, or micro-climatic temperature shifts. These limitations are especially pronounced for precipitation, which is not only spatially erratic but also temporally dynamic, often varying significantly even within short timeframes and small areas. While our approach accounts for terrain effects, it cannot fully overcome the data scarcity problem. Additionally, precipitation is treated in this work as a single variable without distinguishing between rainfall, snowfall, or nail, due to limited data availability. Integrating supplementary sources such as weather radar or satellite-based observations could improve both spatial and temporal resolution in future iterations and allow for more nuanced differentiation between types of precipitation and their specific impacts. Similarly, the use of county-level socioeconomic data--though suitable for national-scale analysis--may obscure important intra-county variation. A single county may contain both high-density urban environments with heavy traffic and sparsely populated rural areas with different risk profiles, infrastructure quality, and road usage patterns. Without more fine-grained data, such heterogeneity cannot be fully addressed, potentially limiting the sensitivity of subsequent analyses. The dataset's structure positions it as a resource for advanced analytical frameworks, including Bayesian mentioned in our related work [43]. These methods can quantify probabilistic relationships between weather extremes and accident severity, offering actionable insights for policy formulation. Future research should aim to incorporate real-time, localized traffic flow data to better capture the dynamic nature of road usage and congestion patterns. More granular socioeconomic data would allow for a more accurate assessment of how demographic and infrastructural disparities influence road safety outcomes, especially in mixed urban-rural regions. In addition, behavioral indicators such as historical records of speeding and impaired driving could help account for human factors that are often critical yet difficult to quantify in large-scale datasets. Integrating high-resolution numerical weather prediction models or satellite-derived data could improve spatial and temporal precision of meteorological inputs, particularly for short-lived or rapidly changing weather conditions.