The Gamma Programming tools for data journalism

Tomas Petricek

Presented at Computation + Journalism Symposium 2015

Data journalism encourages reader interaction. This is often done through simple user interfaces. For more advanced readers, there is typically a download with the raw data behind the visualization. However, there is an interesting gap between the two. What if the reader wants to change a parameter of a visualization that is not exposed through the user interface? What if they want to re-create the same visualization, but using data from a different source?

We believe that the fundamental reason for this inflexibility is the fact that accessing data and building interactive visualizations is a difficult programming problem. As a result, data journalists use a wide range of tools, often involving manual steps, which makes it hard to publish the entire process as a reproducible program.

In this paper, we present The Gamma, a project that reduces the number of steps needed to link a data source to an end-user visualization. The Gamma uses programming language techniques to make data sources easier to access and to automatically build user interfaces that let readers modify parameters of visualizations. But behind the visualization and the user interface, there is full source code, which makes reports transparent, more reproducible and more accountable.

Paper and more information

• Download the extended abstract (PDF)
• Talk slides from Cambridge Spark Data Science Summit 2017
• For more information, see The Gamma project homepage

Watch the talk

I talked about The Gamma project as part of the Fellow Short Talks series organised by the Alan Turing Institute. This provides an accessible introduction to the project. You can watch the talk below.

Bibtex

If you want to cite the paper, you can use the following BibTeX information.

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@misc{data-journalism,
  author       = {Tomas Petricek},
  title        = {The Gamma: Programming Tools for Data Journalism},
  year         = {2015},
  howpublished = {Extended abstract presented at Computation + Journalism Symposium, New York},
  url          = {https://tomasp.net/academic/papers/data-journalism/extended-abstract.pdf},
}

If you have any comments, suggestions or related ideas, I'll be happy to hear from you! Send me an email at tomas@tomasp.net or get in touch via Twitter at @tomaspetricek.