## 2 Dataset and Methodology
### Dataset and Criteria  
Although each study area has its unique characteristics, it is observed that similar criteria are generally used for solar PV farm site selection. The criteria essential for PV farm site selection were determined by analyzing those commonly used in the literature [13; 18]. Additionally, the criteria were chosen based on the available and accessible data specific to the study area. Table 3 shows the data sources and criteria. These criteria include the global horizontal irradiation, land use, slope, aspect, distance to roads, faults, and powerlines, and wind speed. These criteria collectively address technical, environmental, and economic considerations, ensuring that selected locations are both efficient and sustainable for solar energy projects. The criteria were considered in order to evaluate areas with regard to  
\begin{table}
\begin{tabular}{c c} \hline
**Numerical Value** & **Preference** \\ \hline
1 & Equally \\ \hline
3 & Moderately \\ \hline
5 & Strongly \\ \hline
7 & Very Strongly \\ \hline
9 & Extremely \\ \hline
2, 4, 6, 8 & Preferences between the above values \\ \hline \end{tabular}
\end{table}
Table 1: Nine quantities for pairwise comparison between criteria [11; 40].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**n** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline RI & 0.00 & 0.00 & 0.58 & 0.90 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 & 1.49 \\ \hline \end{tabular}
\end{table}
Table 2: Reference values for RI [11; 12].  
distance from disaster events, high levels of energy production, solar radiation, high winds, extent of urbanization, and avoidance of use of agricultural areas. As the city has no historical record of disasters other than earthquakes, data related to other types of disasters were disregarded. Upon reviewing the literature, threshold values for the criteria were determined. Based on these values, four sub-criteria were created, which are very suitable, suitable, less suitable, and unsuitable. In this study, the sub-criteria were developed according to the threshold values derived from literature reviews and existing data. Additionally, since the analysis was conducted with data of different spatial resolutions, the data were resampled to a 30 m spatial resolution before performing the analysis. The resolutions were determined as cell sizes while raster data were clipped according to the provincial boundary, and vector data were rasterized accordingly. Distances were calculated using vector data processed through Euclidean distance analysis based on the provided datasets. Suitability ranges were then determined by classifying these distances into categories, reflecting increasing levels of appropriateness for the intended use.  
Existing PV farms were identified through an examination of 2023 and 2024 satellite imagery from Google Earth to show the relationship between suitable areas and existing PV farms. These analyses revealed that PV installations were primarily roof-top PV systems in urban areas, whereas outside urban areas, they were predominantly PV farms. Both types of PV installations data were digitized from the satellite imagery to enable a detailed spatial analysis. This approach allowed for the assessment of whether existing PV installations align with the regions identified as highly suitable for solar energy development, providing insights into the effectiveness of current site selection practices. Additionally, it supports planning for future PV projects by comparing installed capacities with geographic and environmental suitability criteria.