# CircleVis: A Visualization Tool for Circular Labeling Arrangements and Overlap Removal
## 4 Evaluation
### Evaluation and Results
#### 4.2.3 Results and Discussion  
Three different evaluations were made during this work. First, various arrangements were assessed with three datasets containing 17, 16, and 10 elements (see Figure 14), respectively. Second, CircleVis functionalities were evaluated. Finally, user evaluations of the proposed options to remove overlaps were given.  
User evaluations for the label arrangement methods are illustrated in Table 2. The majority of users chose the \(A1\) arrangement for each data source because of the small arc weight and extended label length. Furthermore, Figure 15 indicates different datasetvisualizations using the proposed arrangements. The obtained results show that labels in \(A1\) do not overlap in short arcs. Unlike the \(A2\) arrangement, labels are not completely written because they are long and related to small arcs (arrangement fails). The \(A3_{L}\) structure was preferred, particularly with the Europe and America datasets. This was due to label length having no bearing on the structure. Moreover, the Europe dataset's arcs are a bit wide despite its 17 elements, and the America dataset contains only 10 elements and three minor arcs that cause a small overlap. For the Europe and Africa datasets, most users accepted the \(A3_{Ln}\) and \(A4\) arrangements because they do not produce label overlaps in all test dataset. This was commonly accepted despite having two or three overlaps, in contrast to America (which has the fewest elements (10)), which has six or seven overlaps that are unacceptable or incomprehensible.  
Figure 14: Visualization of proposed datasets with various label/arc sizes.  
Table 3 illustrates the user evaluations of CircleVis functionalities. It was noted that the randomize function was selected by 54% of users because it was useful to test or compare datasets with different sizes, arc weights, and label lengths. Other functions, such as merge/split or add/delete items, were regarded as useful by the majority of users. Arc color and label color were not evaluated because they were user suggestions.  
Finally, user evaluations for overlap-removal options are given in Table 4. The most efficient technique was \"changing the font size locally\" because it was useful where there are few overlaps, as in the European dataset. \"Semantic zoom\" and \"global font size change\" were useful, especially with _A1_, _A3\({}_{Ln}\)_, and _A4_. However, \"semantic zoom\" is not the best option for _A2_, because it required significant zoom to remove overlaps. Thus, the illustration may leave the arrangement screen (SVG limit is 600 pixels). Last, due to the fact that removing overlaps is only used for one dimension on each circle side, this algorithm is more efficient for the _A3\({}_{L}\)_ approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & _A1_ & _A2_ & _A3\({}_{L}\)_ & _A3\({}_{Ln}\)_ & _A4_ \\ \hline
**Europe** & _7,7_ & _4,A_ & 6 & 5,5 & 5,2 \\ \hline
**Africa** & _7,6_ & _4,3_ & \(7\) & \(5\) & _4,2_ \\ \hline
**America** & _7,7_ & \(4\) & \(6\) & \(4\) & _4,7_ \\ \hline \hline \end{tabular}
\end{table}
Table 2: User evaluations (/10) of different arrangements using various datasets.  
Figure 15: Statistical results of COVID-19 in Africa on 2 September 2021 using _A1_.