## 4 Discussion
The spatial distribution of radiation influences the mean radiant temperature (Tmr) by modulating the arguments in Equation (4) i.e., the fraction of direct-beam radiation vs. diffuse radiation. Another package in R, the _comf_ package [35], also provides comfort indices, but not the PET index. It must be noted that it provides information related to room comfort and not for open environments. It does not include variables related to sun illumination. These variables can be determined by the shadow-casting map and Equation (4). In our application, this package was used for SET and PMV index by providing a mean radiant temperature factor calculated with the outdoor prediction of solar radiation. A similar approach has been used by other authors to map a single point-in-space comfort index [9; 17]. It is worth noting that most comfort indices include solving a non-linear system of equations using iterations, and this results in computational effort. Innovative solutions to improve calculation speed, such as using a neural network as proposed in [36], can provide great benefits by speeding up the process, even if care must be used on the training and testing of the model. A potential use of maps of comfort indices can be related to energy efficiency. Both indoors and outdoors, a human being tries to be as comfortable as possible, using energy to modify the environment that she/he is living in. The ability to predict the areas where comfort is highest can allow for better planning of urban environments [31; 37]. It is worth noting that the model requires the accurate 3D representation of the environment through the point cloud model. It is reasonable to think that over the years the environment might change. For example, trees or branches might be removed, and, over a longer period of time, vegetation will grow and other trees might be planted or buildings might be built. In this case, of course the model must be updated. Removing points from the model is quite straight forward and can be executed by any operator using open-source software (e.g., Cloud Compare). More complex changes require more complex editing and further surveying using laser scanners or photogrammetric methods, depending on the characteristics of the area that have changed. The new survey can substitute the old one if complete, or the two surveys (point clouds) can be merged and updated. This work does not consider some aspects that will be the focus of future developments. One aspect is the resolution of the point cloud, that must have a minimum amount of point density to provide an accurate shadow representation. If points are too sparse, the model will over-estimate the amount of light reaching the reference grid. Another aspect is the difference between vegetation and buildings with respect to the behaviour of light. Building elements, walls and roofs, block light completely and this should therefore be taken into consideration. If the point cloud model is dense and well distributed, the building walls will be represented well by the necessary points and thus the model that predicts shadow casting will operate correctly. The best solution would be to classify the points to discriminate above-ground points further in vegetation and buildings. Having such information allows differential treatment of points depending on the class and improves the shadow-casting model.