## 4 Discussion  
This study introduces a novel, integrated dataset to advance the analysis of road safety dynamics in Poland. Unlike traditional sources such as SEWIK, STATS19 (UK), or BAAC (France), which rely solely on police-reported accident data, our dataset incorporates these records with spatially interpolated meteorological measurements and sociodemographic indicators. Key variables are unified into a single table to simplify analysis while main  
Figure 12: Correlation coefficients between (**a**) road accident fatalities and (**b**) total accidents with socioeconomic factors at county level (2022). The figures reveal distinct patterns: fatality correlations are generally weaker than those for total accidents. For fatalities, improved paved surfaces and paved roads show the strongest associations (both 0.57), followed closely by vehicle types (trucks and motor vehicles at 0.56, passenger cars at 0.54). For total accidents, vehicle-related factors dominate with stronger correlations—trucks, motor vehicles, and passenger cars at 0.82 each, and buses at 0.73. Population density shows inverse relationships: slightly negative for fatalities (\(-\)0.02) but strongly positive for total accidents (0.45). Similarly, municipal roads per 10,000 inhabitants demonstrates negligible correlation with fatalities (0.01) but exhibits a substantial negative association with total accidents (\(-\)0.29). taining detail. Unlike global datasets like IRTAD, which offer only aggregated statistics, our micro-level approach enables fine-grained spatial and temporal analyses. To address challenges in linking localized weather conditions to accident sites, we employed universal kriging with elevation drift, producing terrain-aware weather surfaces--particularly valuable in Poland's topographically diverse southern regions. While the dataset represents a significant step forward in integrating diverse sources of information for road safety analysis, certain limitations remain. The accuracy of interpolated meteorological data is influenced by the spatial distribution and density of weather stations, which is particularly challenging for highly variable factors like precipitation. Additionally, socioeconomic data aggregated at the county level may mask local disparities within counties, such as differences between urban and rural communities. These sources of uncertainty should be considered when interpreting results. The analytical potential of this integrated dataset is demonstrated by initial correlational analyses. Distinct patterns emerge in the relationships between accidents and various factors. Meteorological analyses revealed that temperature exhibits a positive association with incident counts (correlation coefficient 0.10 weekly, 0.09 yearly), while humidity shows a negative relationship (\(-\)0.07 weekly, \(-\)0.10 yearly), and precipitation demonstrates a negligible impact at the weekly scale (\(-\)0.01) but a moderate negative correlation yearly (\(-\)0.05). Regarding sociodemographic factors, the data suggest that different elements influence accident frequency versus severity: vehicle-related factors (trucks, motor vehicles, and passenger cars, all with correlations of 0.82) showed strong positive associations with total accident numbers, whereas improved road infrastructure correlated more significantly with fatalities (0.57). Population density presented an inverse pattern--negligible correlation with fatalities (\(-\)0.02) but strong positive association with total accidents (0.45). While these correlations do not imply causation, they collectively suggest that accident occurrence and severity may be driven by separate mechanisms. Although some of the mentioned correlations are weak, they can still provide useful information in multivariate predictive models. When combined with other factors, they may contribute to improved model performance through interaction effects or additive contributions.