# Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
## 4 Results
### Visual Space  
Regarding the **Visual Space** (Figure 7), we summarise in Table 2 the reviewed works in terms of Visual Mapping components (graphical elements, identification and type of visualisation or substrate) and View Transformation details (use of single or multiple views and the type of interactions). Most of the articles employed explicitly _basic_ visualisations (14/20, 70%) [39; 40; 81; 82; 83; 84; 85; 86; 87; 89; 90; 91; 92]. The most common methods are bar charts, line charts and scatter plots. Most of the V-NLIs include a combination of these common methods [40; 82; 83; 84; 86]. Additionally, Chat2 Vis [83] includes a box plot, Talk2 Data [64] has a pie chart and Gamebot [86] offers users game shot charts about football and basketball games, as well as displays games statistics in tables. Moreover, some V-NLIs include a map chart, while [39; 90; 91] have 2D maps in addition to the most popular methods, and [70] includes a 3D map. There are five V-NLIs that have only one visualisation method: GeCoAgent [87] has a pie chart, Ava [81] has a line chart, Valetto [92] and Iris [89] have scatter plots, and DataBreeze [85] uses dots to visualise every data point individually. Only a small percentage of these studies used _advanced_ visualisation methods (6/20, 30%) (see Figure 8). For example, [PERSON] and [PERSON] [80] and Orko [15] implemented network visualisations as their main visualisation. In addition, Orko includes additional basic visualisation methods such as a bar chart to support its main visualisation. On the other hand, Tansvis [63] uses a line graph as the main visualisation for analysing transient data (quality of the software system over time), though it has a network graph for displaying the overview of the software system, where users can select a part to explore transient behaviours. ConVisQA [69] created a novel design to show the hierarchical structure of conversations using stacked bar charts with indentations to show the hierarchy. ConVisQA  
Figure 7: View Space overview and the main characteristics of the Visual Mapping and the View Transformation steps. also displays the conversations on the right-hand side of the screen. InChorus [88] supports popular basic visualisations such as bar, line and scatter, and they also included one complex option, parallel plots. FlowNL [70] used flow visualisation to show flows occurring on the earth (e.g., hurricanes). FlowNL also included basic visualisation methods for giving additional information such as a bar chart displaying the velocity of the hurricanes. Moreover, all V-NLIs, including basic and advanced visualisations, used abstract graphical elements (lines, points, bars), though we did not encounter any use of symbolic graphical elements such as icons or glyphs. We examined the **Visual Mapping Identification** in the previous works and found that 50% (10/20) of them only have _fixed_ visual mapping [15; 63; 69; 70; 80; 84; 85; 87; 90; 92]. Meanwhile, only 10% (2/20) of them support _user-defined_ mapping [81; 89], while 20% (4/20) use only the _rule-based_ visual mappings [40; 64; 82; 86] method. Finally, there are V-NLIs that support a combination of two visual mappings [39; 83; 88; 91]. For example, Data@Hand [84] is a mobile application on which users can track their daily steps and sleep time, among others. It has basic fixed visualisations that are displayed when users open the application. On V-NLIs such as Orko [15], ConVisQA [69], Miva [90], and DataBreeze [85], when the dataset is uploaded, data are directly displayed with one pre-defined visualisation method. FlowNL [70] displays flow visualisations on a 3D world map with NL commands. GeCoAgent [80.87] both have fixed visualisations that are updated with NL queries. TransVis [63] and Valetto [92] automatically generate visualisations from natural language, though both of these systems have only one fixed visualisation.