## 4 Methodology
### Data Processing and Analysis  
For the localization of charging points targeting _private EVs and e-bikes_, a spatial analysis was performed to detect suitable areas for the installation of charging points for EVs and e-bikes for individual use and to improve the capillarity of the network.. To do this, the authors adopted and adapted a method for the allocation of charging infrastructures jointly developed by [27] (see Figure 3). Figure 2: The study area of South Tyrol. Source: own elaboration. As illustrated in the flowchart, the first step of the analysis was to partition the territory into cells of equal area and shape. This process, called \"tessellation\", is used to compare different data for mapping because it allows one to normalize geographies [43]. The tessellation can be of equilateral triangles, squares, or hexagons, as these three polygon shapes are the only three that can create an evenly spaced grid [43]. In this case, the authors decided to use hexagons, because this option is the best one to minimize the inevitable errors linked to the normalization of a spatial distribution [44]. In fact, hexagons have the lowest perimeter-to-area ratio and can reduce the sampling bias due to the edge effects of the grid shape [45]. Then, the hexagon grid was filtered by (i) selecting the cells intersected by the transportation network (given by municipal, provincial/regional, and national roads in the case of EVs and by cycleways and municipal roads in the case of e-bikes) and (ii) excluding the cells within a certain distance from existing charging points (a buffer of 5 km was considered in the case of EVs and of 2 km in the case of e-bikes). The remaining cells represent areas with potential infrastructure needs. However, not all of them are suitable for the installation of new charging points. Usually, the spatial distribution of infrastructures is not homogeneous in the territory, but it is strongly correlated with the spatial distribution of POIs [46]. According to OpenStreetMap Wiki, a POI is a specific point location that someone may find useful or interesting and that may therefore attract people. Since a POI affects the suitability of areas for the installation of the new infrastructures, the most relevant ones in terms of private electric mobility were included in the analysis and were grouped into three categories according to their general function (see Table 3). \begin{table}
\begin{tabular}{c c c} \hline
**POI Category/Subcategory** & **Average Score for EVs** & **Average Score for E-Bikes** \\ \hline Tourist amenities & 3.6 & 3.6 \\
1. Accommodations & 5 & 5 \\
2. Alpine huts & 1.5 & 5 \\
3. Historical sites & 3 & 3 \\
4. Cultural sites & 4 & 4 \\
5. Sites for outdoor activities & 3.5 & 3.5 \\
6. Natural parks & 3 & 3 \\
7. Sites for indoor activities & 3 & 2 \\
8. Shopping malls & 4.5 & 3.5 \\
9. Public establishments & 4.5 & 3.5 \\ \hline \end{tabular}
\end{table} 
Table 3: List of POIs with the relative score retrieved from the evaluation questionnaire. Source: own elaboration. Figure 3: Flowchart of the suitability analysis for the installation of charging points for EVs and e-bikes. Source: own elaboration. These POI may not have the same influence on the allocation choice, as, for example, higher importance of accommodations for the development of infrastructure. In order to solve this issue, an expert-driven approach was used. An evaluation questionnaire was designed and administered by an online tool to the project partners (6 representatives of the case studies) for the attribution of a subjective score between 1 (minimum) and 5 (maximum) at each POI and at each POI category. The averages of the scores (see Table 3) were included in the (experience-based) calculation process to properly weigh the different influences of POIs on the allocation choice. Specifically, first, the command \"Spatial Join\" was used to join attributes from point-type layers (POIs) to polygon-type layers (hexagon grids) based on their spatial relationship.