## 3 Results
### Dataset
Maps show interpolated values for (**a**) temperature (°C), (**b**) humidity (%), and (**c**) precipitation intensity (mm). Elevation-informed kriging highlights spatial patterns, especially in the mountainous southern regions where elevation significantly influences the variables. bution. The precipitation comparison demonstrates broadly similar patterns, with a slight underrepresentation of incidents during minimal precipitation conditions. As shown in Table 6, temperature values in the incident data range from \(-19.9\)\({}^{\circ}\)C to \(36.0\)\({}^{\circ}\)C (mean \(12.6\)\({}^{\circ}\)C), while historical weather temperatures range from \(-22.0\)\({}^{\circ}\)C to \(36.0\)\({}^{\circ}\)C (mean \(9.6\)\({}^{\circ}\)C). Humidity during incidents spans from \(9.6\%\) to \(100\%\) (mean \(71.5\%\)), compared to the historical range of \(4.4\%\) to \(100\%\) (mean \(77.4\%\)). Precipitation at incident times rarely exceeds \(0.1\) mm, with \(75\%\) of incidents occurring in conditions with no measurable precipitation. The spatial distribution of incidents, both raw and normalized by different socioeconomic factors, provides insights into regional accident patterns, as illustrated in Figure 10. The raw incident count shows concentration in urban centers. However, normalizing by population density reveals higher per-capita incident rates, particularly in and around major urban areas. When normalized by vehicle ownership, the pattern shifts again, displaying a more even distribution with only a few focal points. The road infrastructure normalization identifies counties with high incident rates per kilometer of paved road and most closely resembles the original low-incident map, suggesting a less significant relationship between road infrastructure and incident occurrence. \begin{table}
\begin{tabular}{l r r r r r r r} \hline  & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline
**Incidents** & & & & & & & \\ Temperature (\({}^{\circ}\)C) & 12.6 & 8.6 & \(-19.9\) & 5.4 & 13.1 & 19.3 & 36.0 \\ Humidity (\%) & 71.5 & 19.2 & 9.6 & 56.7 & 75.5 & 88.2 & 100.0 \\ Hourly Precipitation (mm) & 0.1 & 0.6 & 0.0 & 0.0 & 0.0 & 0.0 & 34.0 \\ Cumulative Precipitation (mm) & 1.7 & 4.6 & 0.0 & 0.0 & 0.0 & 1.2 & 131.3 \\ \hline
**Historical Weather** & & & & & & & \\ Temperature (\({}^{\circ}\)C) & 9.6 & 7.9 & \(-22.0\) & 3.4 & 9.4 & 16.2 & 36.0 \\ Humidity (\%) & 77.4 & 11.9 & 4.4 & 69.3 & 78.7 & 87.0 & 100.0 \\ Daily Precipitation (mm) & 1.8 & 4.7 & 0.0 & 0.0 & 0.2 & 1.7 & 245.5 \\ \hline \end{tabular}
\end{table} 
Table 6: Weather data statistics in the dataset. Figure 9: Distribution comparison of weather parameters between all weather station measurements (blue) and weather conditions during traffic incidents (red). Incident data were interpolated using universal kriging. (**a**) Temperature distribution shows incidents occurring more frequently at higher temperatures compared to average weather conditions. (**b**) Humidity distribution. Incidents show a comparatively smaller peak at the highest humidity levels. (**c**) Precipitation distribution demonstrates similar patterns between general weather and incident conditions, with a notably lower number of incidents at the lowest registered precipitation. Our dataset allows for multi-scale correlation analyses between meteorological conditions and accident patterns. Figure 11 presents these correlations at both weekly and yearly temporal scales, revealing distinct relationships. Figure 11: Correlation between weather parameters and traffic incident counts at county level across different time scales. (**a**) Weekly correlations reveal that temperature has a positive association with incidents (0.10), while humidity (\(-\)0.07) shows a negative relationship and precipitation (\(-\)0.01) has almost no impact. (**b**) Yearly analysis demonstrates that humidity has the strongest negative correlation (\(-\)0.10), with temperature maintaining a positive association (0.09) and precipitation showing a moderate negative correlation (\(-\)0.05).