## 4 Discussion  
The webapp is an interactive tool that is very useful to promote awareness of how vegetation can be a valid support for improving comfort for visitors of the garden. It can act both as a simulation tool and for real-time mapping of comfort indices in the garden. Using weather forecasting tools, which are quite accurate at predicting weekly values of the climatic factors together with the model, can map comfort indices and thus support visits. In particular, elderly people or persons that have issues that limit or modify perception, e.g., Alzheimer's disease, can make sure to visit the garden at dates and times when the comfort indices are ideal. To our knowledge, this is the first time that such a combination of IoT devices and 3D models are used together to provide an accurate geospatial map of comfort indices. Scientific literature at the regional and urban scale are common. The former is exemplified by the work of authors in [22], where satellite data is used to provide a global  
Figure 10: Time distribution (27 July 2022) of the fraction of direct-beam/diffuse-only radiation (i.e., light vs. shadow) and corresponding solar radiation values from the pyranometers. All data are normalized between 0 and 1. Figure 9: Time distribution of comfort index and radiation values (27 July 2022): comfort index at the position of device 4815 (green color), 4816 (blue color), and solar radiation (red) from the weather station. The black line is ideal comfort. map of mean radiant temperature, which is an input in functions for comfort indices (see Equations (1)-(4)). The same authors provide a global mapping of comfort indices in [23]. At the urban scale, much work has been performed for energy efficiency modelling using 3D models [31]. At the urban scale, [32] used geographic information systems (GIS) to map bioclimatic comfort. The work of [33] maps comfort levels at an urban scale by considering streets in the city. Authors in [34] have used GIS at the scale of an urban park, also mapping visitor behavior with a significant dataset, with similarities to [1]. The main point of sensor data is to estimate the mean radiant temperature. As mentioned in [6], this can be estimated by cloud cover, time of the year, and type of surface cover. In this work, we estimate it by integrating sensors and a 3D point cloud model. Cloud cover is integrated into the model when directly using data sensors. Cloud cover is a factor that indirectly is considered by solar radiation sensors, e.g., pyranometers. A network of low-cost sensors can directly measure sunlight/shadow and temperature, thus providing comfort indices that are not based on a model, but on direct measures. This solution would provide accurate indices, but only for the specific location where the sensors are placed. Light/shadow characteristics can vary greatly within a small distance, especially in complex scenarios with trees and buildings. This variability would require a large number of sensors to represent the study area. For example, in a summer day at the latitude of the study area, a person positioned in direct sunlight will feel too hot, whereas moving even one meter to where the vegetation can provide shadow and result in a much increased comfort. To catch this variability, sensors would have to be very dense, and this would increase maintenance costs. Additionally, to cover the entire area, data should also be spatially interpolated, thus adding uncertainty to the final values. Our proposed solution, being based on sensor data, but also on a physical model that defines the ratio of direct light beam and diffuse light only (shadow), provides a continuous cover of comfort values of the study area. Cross-checking the hourly temperature distribution statistics from the two pyranometers and the weather station (Figures 9 and 10) shows that the comfort indices reflect the temperature readings. This is an important validation as it means that the mapping of the comfort index accounts correctly for the spatial distribution of radiation.