## 3 Results
The restricted area that meets the criteria at a national scale is 455.063 km\({}^{2}\), and the available PV power output in this area is 6784 GWh (within the range of 9.4 to 15.4 MWh, with a mean, median, and standard deviation of 13.531 MWh, 13.754 MWh, and 0.920 MWh, respectively). Moreover, the evaluated PV power outputs selected at suitable sites at a national scale were sorted into specific regions (district of Ucheon-myeon in South Korea) for a detailed visualization in the BIM environment. The PV output production in the Ucheon-myeon case area (area of 1.669 km\({}^{2}\)) is computed as a total of 8.227 GWh, within the range of 11.2 MWh to 14.4 MWh, with a mean, a median, and a standard deviation of 13.407 MWh, 12.782 MWh, and 0.827 MWh, respectively. Based on these results, we may infer that solar-power production can be affected by spatial analyses and changes at the regional scale. For example, the power generation output (29,002 GWh to 6784 GWh) and range of available production (7.9-16.7 MWh to 9.4-15.4 MWh) are reduced by applying the spatial analysis to the sites selected for PV plants. In addition, the power-generation output (6784 GWh to 8.227 GWh) and range of available production (9.4-15.4 MWh to 11.2-14.4 MWh) is reduced by applying the spatial analysis to the sites selected for PV plants. Meanwhile, the mean and median power output per unit area has increased as selecting adequate PV sites using spatial analysis. Mean monthly PV power output per unit area has increased from 7.324 kWh/km\({}^{2}\), 29.734 kWh/km\({}^{2}\), to 8032.954 kWh/km\({}^{2}\) along with site restrictions. In addition, the non-installation areas of power plants at a national scale, which account for 7.196% of all candidate sites, were excluded through the spatial analysis proposed in this study. It implies that the suitable sites to install power plants for more centralized and efficient electricity production are presented. Additionally, to visually explore the geographical patterns of PV power outputs in the case area, the distribution map presented in Table 2 was generated by classifying it into five classes for visualization, as shown in Figure 8a. Figure 8a shows a trend in which energy production is lower in the east. In addition, the heatmap in Figure 8b, which draws PV power-output features as the representative surface of the relative density (i.e., indicating density values, ranging from cool (i.e., sparse density) to hot (high density), was created, as shown in Figure 8a. Unlike the PV power-output distribution shown in Figure 8a, the heatmap tends to cluster in areas with low production. This implies that, although the energy distribution located in the eastern region is lower than that in the western region, the eastern region is considered more suitable for installing PV plants because the candidate sites are densely clustered. Figure 8: (**a**) Distribution map for energy production trends, and (**b**) heatmap of representative surface of the density in suitable sites for PV-plant installation near highway networks in the case area. The above results (compared to Table 2) confirm that regional differences (scale change of the analysis region) can affect the solar-power generation using a specific date of PV output data (i.e., March 2017). Thus, this study also identified the monthly trend of PV power-output production at different scales of candidate sites by computing the average monthly PV power output (Figure 9). Overall, the monthly PV power output on a national scale (blue line, Figure 9) achieves a mean, median, and standard deviation of 5,823,123 MWh, 5,793,198 MWh, and 969,678 MWh, respectively. When investigating the trend of PV power on a monthly basis, the highest power generation of 7,592,664 MWh was observed in May 2017, while the lowest power generation of 4,374,704 MWh was observed in December 2017.