Articulo_4_EN_Doxa_31_Visualisations_critical_information

as a type of visualisation, with a graphic and visual nature, composed of iconic resources- photographs, graphs, maps, etc.- and typographic or verbal resources (Colle, 2004; Valero, 2008; Cairo, 2016; Ivars-Nicolás, 2019).

Visualisations in data journalism enable complex and abstract information to be transformed, thus turning large data sets into simple realities (Sánchez-Bohenví and Ribera, 2014; Kennedy, Hill, Aiello and Allen, 2016; Sánchez and Sánchez, 2018). Manovich (2008, 2011, 2014) states that these data visualisations’ purpose is to make figures visually understandable and facilitate news consumption.

Visualisations are becoming more widespread, mainly due to journalists’ training in artistic and audiovisual disciplines (Ivars-Nicolás, 2019). Its use has also increased in parallel with the profession’s digitalisation and greater access to the free tools for creating them.

Cairo (2014; 2017) states that visualisations are not only designed to be seen but also to be interpreted and judged. Rogers (2014) adds that it must be the data that defines and allows the journalist to select the type of visualisation. Therefore, it is essential to determine the audience, goal, and context in which it is shown (Sánchez-Bohenví and Ribera, 2014).

Stikeleather (2013) highlights that for a visualisation to be effective, it must comply with these three principles: understand the audience, have a structure or clear diagram and tell a story. The visualisation will only achieve its purpose if the audience can understand the sense of the information.

In this sense, data representation has become an intrinsic part of the lingua franca, a common form of communication independent of each territory’s policies and cultures (Barlow, 2014).

1.1. The study of visualisations within data journalism

There are three types of studies related to data journalism in the scientific literature: those focused on the conceptualisation and theorisation of data journalism (p.e.: Gray, Bounegru and Chambers, 2012; Howard, 2014; Royal and Blasingame, 2015; Coddington, 2015; Borges-Rey, 2016), those that examine the actors involved in its production (e.g., Paraise and Darigal, 2012; Appelgren and Nygren, 2014; De-Maeyer, Libert, Domingo, Heinderyckx a Le-Cam, 2015; Fink and Anderson, 2015; Uskali y Kuutti, 2015; Tabary, Provost and Trottier, 2016; Hermida andy Young, 2017) and those that carry out an analysis of this media practice (e.g., Knight, 2015; Tandoc and Soo-kwang, 2017; Loosen, Reimer and Schmidt, 2017; Young, Hermida, and Fulda, 2018; Ojo and Heravi, 2018).

In the latter group, the investigations that dissect the visualisations in data journalism works are included, all of which apply content analysis as a method for drawing conclusions. Although most are not studies focused exclusively on the visual aspects, the works focus on elements related to the visualisations’ quantity, typology, interactive options, and functions. The results obtained are different due to the lack of consensus on the criteria for examining this media practice.

Knight (2015), Loosen, Reimer, and Schmidt (2015, 2017), and Stalph (2017) agree that the publications include an average of two news graphic representations per item, most often incorporating only one that is structured as a story. These are mainly concerned with showing changes over time and comparing values.