## 7 Final Remarks and Future Work  
The objective of this research was to evaluate the visual perception of hue, saturation, lightness, texture, orientation, shape, and text visual variables when used for the visual mapping of categorical data and gradually increasing the level of partial overlap (0%, 50%, 60%, and 70%) and different visual encoding values (3, 4, and 5) in the context of location search tasks [16] with a single-value target item. In the evaluation tests, 48 participants were divided into four groups of 12, one for each level of partial overlap [25]. The results obtained demonstrate that the primary objective of this study, the formulation of a classification for visual mapping of categorical data, was achieved. The classification resulting from this research could help to direct future studies or may provide opportunities for application when visual variables are analyzed by professionals in the fields of information visualization, data science, and the development of data-driven interfaces. The recommendations presented here are based on the findings of this study. Analyzing the classification obtained in this study, the hue visual variable, which produced the best results for both quantitative and qualitative data, was found to be the best visual variable analyzed for the visual mapping of categorical data. The lightness and shape visual variables performed well according to the evaluation metrics employed in this study, and, despite the presence of some negative comments from participants via the qualitative questionnaires, they were classified as good visual variables for performing the visual mapping of categorical data with a high level of partial overlap and number of different visual encoding values. These variables were placed as the second and third top visual variables that were analyzed. The text visual variable showed positive findings for the control group (0% occlusion); however, it was weaker when the level of partial overlap was gradually increased. For this visual variable, care should be taken when mapping categorical data, evaluating the visualization technique to be used, and determining whether there is a possibility of graphic elements overlapping. Due to the high number of errors occurring at all levels of partial overlap and variation in the number of different visual encoding values, the texture visual variable was not classified as a good option for the visual mapping of categorical data, suggesting that, if the user chooses to use it, several factors must be evaluated (e.g., occurrence of partial overlap of elements, number of visual coding values, etc.). The orientation visual variable was found to be unsuitable for performing the visual mapping of categorical data because it yielded unsatisfactory results across all the evaluation scenarios considered in this study (all levels of partial overlap and number of different visual encoding values). Even though the subset of three different visual encoding values yielded good results for all levels of partial overlap, the performance of the visual variable saturation was significantly impacted by a gradual increase in the number of different visual encoding values (four and five). Therefore, it is suggested that this visual variable be used to map categorical data in scenarios where mapping a small number of different visual encoding values (three or less) is required.