2. Methodology

This research is of a univariate and two-dimensional descriptive nature and aims to dissect the visualisations in the quality data journalism publications. In line with previous works (Loosen, Reimer, and Schmidt, 2017; Ojo and Heravi, 2018; Young, Hermida, and Fulda, 2018; Córdoba-Cabús, 2020), a content analysis was applied as a methodological tool to the stories nominated for the 2019 Data Journalism Awards. These awards were organised by the Global Editors Network and have been recognised since 2012 as the highest international award in this specialisation to evaluate both the work with figures and the form and content of the project (Global Editors Network, 2019). These publications mark future trends in data journalism since they incorporate the most formal innovations and the highest quality and variety of resources.

There were a total of 103 works in the 2019 call for entries. This study’s central focus is the specific data journalism publications; in other words, those nominations are data units or teams, journalists’ portfolios, and entire websites were excluded from the sample. The final sample was composed of 42 items from the categories: research of the year, best use of data in breaking news (within the first 36 hours), data visualisations of the year, innovation in data journalism, and the audience’s choice award.

The data journalism items’ analysis included the following variables: number, typology, functions, information representation, and visualisation ratio. Following Stalph’s (2017) assessment and due to the high number of visualisations, a maximum of eight per item was examined. In the works with several screens, only the main one was analysed, and when the story was represented through an infographic, it was categorised as a single visualisation.

2.1. Types of visualisations

The visualisations were classified by combining the typologies used in different studies (Wijk, 2005; Segel and Heer, 2010; Knight, 2015; Loosen, Reimer and Schmidt, 2015; Tandoc and Soo-Kwang, 2017; Stalph, 2017; Young, Hermida, and Fulda, 2018; Córdoba-Cabús, 2018; Córdoba-Cabús, 2020), to frame any visualisation in some of the following options:

Tables and lists with or without interactivity. They show information in a table or list. The tables or lists are included in this part, making it possible to rearrange the figures, however, if the interactive functions expand the journalistic con-tent or incorporate more elements to the visual representation, they are classified as infographics (Salvatierra, 2008).

Static graphs. Represent numerical information in two dimensions. The legend and the title are considered compo-nents of the graph (Arteaga, 2008). The type is not specified (bars, lines, areas, sectors, etc.). Annotated static graphs are categorised as infographics (Salvatierra, 2008).

Interactive graphs. Represent information in two dimensions. As in the static ones, the legend and title are considered as components of the graph (Arteaga, 2008). If it is interactive, and these functions expand the journalistic content or incorporate more elements in the visual representation, it is classified as an infographic (Salvatierra, 2008). If they are moving graphs (with automatic changes), they are grouped in the option “Animations.”

Maps with or without interactivity. They provide a geographical representation of the information. As with the gra-phics, both the title and the legend are considered part of the map. The static maps with annotations and those that