## 2 Materials and Methods  
The methodological framework (Figure 1) identifies four inter-related phases: knowledge (K), methods (M), tools (T), and outcome (O). The main goal of the proposed approach concerns UES evaluation, planning, and monitoring within a 3D virtual environment, in order to improve the visualisation of the spatial relationships among the service allocation and the urban fabric density. The framework of a Spatial Decision-Making Support System (SDMSS), where GIS tools, Multi-Criteria Decision Analysis (MCDA) methods, and Light Detection and Ranging (LiDAR) technology work together and support one another, represents the context of our experimentation. The first phase seeks to identify the main topics concerning the recognition of UES, their characteristics, and their spatial benefits in a selected focus area. As the preparatory step of the proposed approach involves data gathering and processing, both authoritative sources and Volunteered Geographic Information (VGI) were considered useful for enhancing the knowledge related to the urban context. A specific data set was structured and elaborated through GIS tools, within the GIS environment, which is able to identify and classify the different types of information and data gathered [61]. Figure 1: The methodological workflow. The second phase concerns the application of a multi-scale approach and a grid-based analysis to normalise the data on a standard surface. A grid of \(500\times 500\) m per cell, which extends over the boundaries of the selected focus area, was assumed as the MMU, as it better includes the natural and built features in the examined environment. The use of a regular-shaped (i.e., a rectangular or square) grid is generally preferred in environmental studies, as the orthogonal co-ordinate system and the raster format are the most common parameters in the release of spatial data [62]. The multi-scale approach allows for assessing spatial indicators--referred to proxies for detecting UES features and dynamics--through the mapping of geographical entities that produce benefits [12; 63]. This approach requires choosing a homogeneous statistical surface on which indicators with different sources, formats, attributes, and spatial resolutions can be processed, analysed, and compared [64]. In this phase, the selected spatial indicators are represented by 3D modelling. The third phase aims to choose suitable MCDA methods to integrate with LiDAR technology. The spatial MCDA allows for computing the values of the normalised indicators representing the performance of each cell, in terms of UES supply. LiDAR technology was adopted to combine the UES values with the 3D city visualisation, as it has been broadly applied in 3D urban modelling [65; 66]. The fourth phase allows for producing a twofold outcome. The first outcome shows the 3D data mapping of three UES categories, while the subsequent outcome overlays the UES grid with the 3D city model to enhance the spatial results and to permit the comparison of UES indicators per cell. The expected results address an application of the proposed multidisciplinary research, combining and involving different academic disciplines or professional specialisations (e.g., urban and landscape planning, forestry, agriculture, and UM disciplines) to solve some critical issues. To provide some examples, it can facilitate:  
* The resolution of spatial problems that involve the allocation of resources/services in a high-density context;
* The 3D UES visualisation of indicator values at multiple scales and locations;
* The spatial assessment of multiple scenarios related to stakeholder preferences within a virtual decision-making environment;
* The development of a dynamic web-GIS platform, through which planning demands and \"integrated evaluation\" tools can be matched with one another. In the proposed methodological process, a key role is assumed for the data that can be used for the structuring of the 3D model.