The Evolving World of Data Journalism
The Evolving World of Data Journalism
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Journalism has been defined as the process of disseminating news and information. Although originally, journalism must have been the purview of the town crier, journalism that is more recent was published in books, newspapers, and magazines, and letter broadcast on radio and television. The most modern forms of journalism take advantage of all technological forms of communication, including website publishing, email, and streaming channels. In tandem with technology, journalistic media has evolved as well. Photojournalists communicate through captured images. Today, those that report news and information about data and analysis are known as data journalists. One of their primary tools is data visualization.
How Data Journalism differs from Photojournalism
Text-based media relied on the descriptive capabilities of language to communicate environments and events. Adding photographic images to text opened a completely new channel of information. It added all the myriad levels of detail that would otherwise be difficult or unwieldy to convey through text. At that time, the adage “a picture is worth a thousand words” came into its own. However, in the information age, modern journalists had to devise a way to represent data in the same compelling manner as photographs represented people, places, and physical actions. Early pioneers of data journalism such as The Journal, did this by presenting data using representational pictures and diagrams.
Data Visualization Elements
Visualizations have to be simple in order to convey their information is a quick, concise manner. Designers use the different design parameters to present different dimensions of the data. In this way, the audience will see the visualization, and quickly understand the story told by the data. The design parameters include:
- Position – location of objects on the diagram. A change in height could represent a value change while a change in left-to-right position could represent a time difference.
- Shape – different shapes could represent different object. For instance, a round shape could indicate a person, while a square shape indicates a city location.
- Object Size – the size of the object can be varied to show intensity, for instance, a larger circle could represent a larger group of people.
- Connection Size – if groups of people are being related to cities, connector lines can show the relationship. A stronger relationship can be indicated by a bolder line.
- Color – color changes can represent a change in a parameter. For instance, a red circle could represent people in their teens, a yellow circle represent young adults, and so on. This is a difficult parameter as the color must work together artistically to keep the visual attractive.
- Labels – in general, labeling is avoided as it destroys the holistic view of the data if the viewer has to read each label entry. However, axis can be labeled to give spatial perspective, and a few key data points may be labeled.
An important constraint of combining these parameters is that they must not fight against each other, that is, a change in one parameter does not mask or overpower a change in another. It must also not be overly complex, in order to confuse the viewer. Finally, it must look good. If it is not aesthetically pleasing, the viewer will have difficult observing the visualization.
Melding the Worlds of Data Journalists and Designers
Effective visuals are generally the effort of a data journalist and a designer. The journalist investigates the data, analyzes the data to determine the story behind the data, and creates the first drafts for the completed visual to tell this story. The designer carries the rough drafts to completion by applying the design elements in a way to preserve aesthetics while presenting the graphics in a way that speaks to the natural cognitive processes of the viewers. These designers often take advantage of business intelligence (BI) software to automate the design process.
Data visualization continues to evolve as data journalists find newer, more effective ways to communicate the information behind abstract data to the public. In this way, they allow their subscribers to understand breakthroughs, events, and important business information contained in modern data sources.
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