## 4 Results  
### Model Results  
Two binary logistic regression models were developed, and the results are shown in Table 2. The goodness of fit of the models shows that the data fit well in the models, as the log-likelihood ratio test statistics were statistically significant at the 1% level for both models. Most of the variables were statistically significant at the 1% level, with the expected signs. The estimated Z-scores indicated significant differences in the estimated coefficients of the same variables for the two study regions, suggesting that the charging demand patterns differed between the study regions. The coefficient of the intercept for the Jeju-do model was significantly higher than that of the Gangneung-si model (the Z-score was significant at the level of 0.01), implying that, overall, the charging demand was higher in Jeju-do than in Gangneung-si. The results show that charger characteristics are correlated with the charging demand. The charging demand tended to be higher in facilities with free parking, which was consistent with our expectations. However, the signs of the estimated coefficients for the charger-type variables (whether fast or slow chargers) were not consistent with our expectation that fast chargers would be more actively used for charging. The negative signs of the \"charger type\" variable indicated that the charging demand tended to be higher on slow chargers than on fast chargers. It is conjectured that as the charging speed of slow chargers is generally slower than that of fast chargers, it is more likely that EVs occupy slow chargers for a longer charging period. However, this can lead to a misunderstanding of the underlying charging patterns; therefore, future research may need to consider not only the occupancy rate (as in this study) but also the number of EVs occupying a charger for charging. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{4}{c}{**Model for Juju-do**} & \multicolumn{4}{c}{**Model for Canergame-si**} & \multicolumn{2}{c}{**\(\mathbf{Z=\frac{B-B_{t}}{\sqrt{4}\text{dB}(9+\text{dB})}}\)**} \\ \multicolumn{1}{c}{**Confinement, \(\beta_{t}\)**} & \multicolumn{1}{c}{**Standard Error,**} & \multicolumn{1}{c}{**\(\mathbf{p\text{-Value}}\)**} & \multicolumn{1}{c}{**Oda Ratio**} & \multicolumn{1}{c}{**Conficient, \(\beta_{t}\)**} & \multicolumn{1}{c}{**Standard Error,**} & \multicolumn{1}{c}{**Oda Ratio**} & \multicolumn{1}{c}{**Z**} & \multicolumn{1}{c}{**\(\mathbf{p\text{-Value}}\)**} \\ \hline \multicolumn{1}{c}{Charger type (1 if fast charge, 0 if slow charger)} & \(-0.201\) & \(0.024\) & \(0.000\) & \(0.818\) & \(-0.812\) & \(0.003\) & \(0.000\) & \(0.444\) & \(7.046\) & \(0.000\) \\ \hline \multicolumn{1}{c}{Free parking (1 if two, 0 otherwise)} & \(0.309\) & \(0.015\) & \(0.000\) & \(1.362\) & \(0.610\) & \(0.007\) & \(0.000\) & \(1.840\) & \(-3.821\) & \(0.000\) \\ \hline \multicolumn{1}{c}{Government offices} & \(1.068\) & \(0.024\) & \(0.000\) & \(2.910\) & \(2.606\) & \(0.173\) & \(0.000\) & \(13.546\) & \(-8.817\) & \(0.000\) \\ \cline{2-11} \multicolumn{1}{c}{Public facilities} & \(0.763\) & \(0.026\) & \(0.000\) & \(2.145\) & \(1.235\) & \(0.202\) & \(0.000\) & \(3.438\) & \(-2.314\) & \(0.027\) \\ \cline{2-11} \multicolumn{1}{c}{Facility type} & \multicolumn{1}{c}{Cummical facilities} & \(-0.007\) & \(0.028\) & \(0.002\) & \(0.977\) & \(1.441\) & \(0.183\) & \(0.000\) & \(4.112\) & \(-8.009\) & \(0.000\) \\ \cline{2-11} \multicolumn{1}{c}{(Reference  
The facility type was also closely associated with the charging demand. Overall, the charging demand was higher in facilities other than private offices (the signs of most coefficients were positive compared with the reference variable \"private offices\"). More specifically, the charging demand in government offices was significantly higher in both study regions. This may be attributed to the aforementioned fact that the Korean government has replaced government vehicles with EVs.