## 3 Results  
### Regional Analysis of Coordinate Differences  
Although the overall agreement in coordinates is excellent, analyzing the spatial distribution of differences can help identify whether systematic regional patterns exist. For instance, some provinces may consistently exhibit coordinate offsets of a few millimeters, which could stem from unmodeled regional effects or specific choices in network adjustment (e.g., peripheral areas with weaker constraints or regionally clustered processing). In this study, we grouped the stations by province--nine administrative provinces of the \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Bernese**} & \multicolumn{2}{c}{**Gamit**} & \multicolumn{2}{c}{**ITRF2020**} & \multicolumn{2}{c}{**GSRM v2.1**} & \multicolumn{2}{c}{**APKIM2005-IGN**} \\ \cline{2-10}
**Region** & **Crustal** & **Velocity** & **Azimuth** & **Crustal** & **Velocity** & **Azimuth** & **Crustal** & **Velocity** & **Azimuth** & **Crustal** & **Velocity** & **Azimuth** \\ \hline SUWN & 32.82 & 110.15\({}^{\circ}\) & 29.14 & 109.41\({}^{\circ}\) & 27.91 & 114.90\({}^{\circ}\) & 29.69 & 112.86\({}^{\circ}\) & 28.53 & 114.10\({}^{\circ}\) \\ WULJ & 32.98 & 107.63\({}^{\circ}\) & 29.35 & 106.12\({}^{\circ}\) & 27.86 & 115.98\({}^{\circ}\) & 29.65 & 113.98\({}^{\circ}\) & 28.5 & 115.26\({}^{\circ}\) \\ MUJU & 33.05 & 108.38\({}^{\circ}\) & 28.61 & 107.47\({}^{\circ}\) & 27.99 & 115.10\({}^{\circ}\) & 29.77 & 113.09\({}^{\circ}\) & 28.6 & 114.35\({}^{\circ}\) \\ KWNJ & 32.89 & 116.4\({}^{\circ}\) & 29.38 & 112.75\({}^{\circ}\) & 28.07 & 114.67\({}^{\circ}\) & 29.85 & 112.66\({}^{\circ}\) & 28.65 & 113.91\({}^{\circ}\) \\ PUSN & 32.50 & 110.47\({}^{\circ}\) & 28.10 & 108.94\({}^{\circ}\) & 28.01 & 115.69\({}^{\circ}\) & 29.79 & 113.71\({}^{\circ}\) & 28.61 & 115.00\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Crustal velocity (mm/year) and azimuth (\({}^{\circ}\)) at five Korean GNSS stations from Bernese, GAMIT, ITRF2020, GSRM v2.1, and APKIM2005-IGN models. and directionality of regional biases as well as the spatial consistency of the different processing strategies. Table 2 presents the comparison between the GAMIT/GLOBK-derived coordinates and KGD2002. While overall agreement is high, several provinces display small yet consistent systematic offsets. Notably, Gyeongbuk and Gyeongnam show positive mean values in the north component (\(\Delta\)N: +0.30 cm and +0.12 cm, respectively), suggesting that the official static coordinates are slightly north of those derived from GAMIT processing. Conversely, southwestern provinces such as Jeonbuk and Jeonnam exhibit negative \(\Delta\)N means (\(-\)0.24 cm and \(-\)0.37 cm, respectively), implying the opposite spatial relationship. These patterns hint at a subtle north-south gradient in residuals that may reflect either regional network geometry or processing-related modeling differences. More significantly, the largest root mean square errors (RMSEs) are observed in Jeonnam, with \(\Delta\)N RMSE reaching 2.52 cm. This elevated scatter suggests reduced spatial coherence in that region, possibly due to weaker geometric constraints from adjacent stations, long baselines, or unmodeled local geophysical phenomena (e.g., ground subsidence or ocean loading along the coast). Table 3 summarizes the Bernese-KGD2002 comparison. In general, this solution exhibits slightly smaller mean offsets and lower RMSE values than the GAMIT-derived solution. Most provinces show mean \(\Delta\)N and \(\Delta\)E values that are statistically close to zero, indicating a high level of consistency with the official KGD2002 coordinates. This may reflect the internal adjustment strategy of Bernese, which emphasizes rigorous network least squares processing with globally consistent constraints. Nevertheless, Jeonnam again stands out with relatively high variability (RMSE \(\approx\) 0.70 cm), although the scatter is notably reduced compared to the GAMIT-based result.