# Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
## 2 Background
### Visual Space  
The second space involved in the data visualisation process is the **Visual Space** (shown in blue in the upper-middle part of Figure 1), which refers to how to map the data in visual structures (the Visual Mapping Step) and how to display them in a viewport (the View Transformation Step). The Visual Mapping Step involves the definition of the next three aspects:  
* The spatial substrate--i.e., the space and the layout used to map the data;
* The graphical elements--i.e., marks such as points, lines, images, glyphs, lines, etc. ;
* The graphical properties--also called retinal properties, i.e., size, colour, orientation, etc. [23]. In the spatial substrate, a wide variety of layouts for displaying data have been proposed, from the simplest, such as those based on coordinate axes, to the more complex, such as those representing networks [31; 32]. In fact, the more basic and simple they are, the more they are exploited in different applications. In our work, we classify these layouts as **basic** and **advanced**. Basic layouts refer to chart-based layouts, which have x and y axes (e.g., bar, line, scatter plot), table-based layouts and map-based layouts (such as a bubble map). We consider advanced layouts to be those that deal with higher dimensionalities(e.g., parallel coordinates) and with connections (e.g., radial tree, circle packing, network graph, sunburst diagram, chord diagram). Even with this simple classification into basic and advanced, we still have a wide range of basic and advanced layouts, and identifying the appropriate layout is therefore complex, especially if the users who analyse the data are not experts. Again, depending on the data types, some layouts fit better (i.e., a 3-aligned axis is a good choice to show quantitative spatial 3D data where each axis corresponds to one coordinate, and the circle packing layout fits well for simple hierarchical data). Moreover, once the layout has been selected, the next challenge is how to map the data attributes onto it. End-users can select and assign these characteristics manually, i.e., **user-defined**[33], but systems commonly use **pre-defined** layouts that only fit specific data. For example, Ref. [34] maps conversational hierarchical data with specific labelled attributes (e.g., negative or positive) to a stacked bar layout that is custom-designed for their data with indentations showing the hierarchy, and is therefore not flexible enough to be adapted to other data. Indeed, other approaches propose **rule-based** strategies to choose the layouts and their configuration dynamically according to the analysed data. These rule-based approaches are commonly used in commercial systems such as PowerBI [35] and Tableau in [36]. Tableau integrated the \"Show Me\" algorithm [37], which selects and maps layouts depending on data type (text, date, date and time, numeric or boolean), data role (measure or dimension) and data interpretation (discrete or continuous). For example, to create a bar chart, users need to place at least one quantitative attribute and one categorical attribute to the y and x axes, respectively, and Tableau then automatically creates the bar chart. Similarly, Tableau needs two quantitative attributes to automatically create a scatter plot. Several academic studies used the \"Show Me\" algorithm to select visualisation methods [38; 39; 40]. Another rule-based method [30] deals with hierarchical data and infers the tree-based layout depending on the shape of the data hierarchy, i.e., they use tree layouts for elongated trees and radial layouts for compact structures. More **intelligent** approaches infer the most suitable layout using some visual examples given by users [41], while others recommend layouts from among five key design choices [42] and use pre-trained NN models that map data to predefined chart templates [28]. Additionally, the Visual Mapping step must consider which **graphical elements** to use and their properties.