"Supporting Data Journalism through Compilers for Visual Inputs" by Parker Ziegler

Traditionally, we view compilers as translational machines between two symbolic representations—a source language and a target language. This model excels for textual inputs, but what if we want to transform a visual input, such as a data graphic or thematic map, into a program? This question looms large in today’s newsrooms, where data journalists often need to turn story sketches, SVG graphics, Figma files, and more into interactive programs.

In this talk, we’ll discuss our research on compilers for visual inputs and explore their application in the newsroom. We’ll highlight two projects—reviz and cartokit—that allow users to (1) reverse engineer data visualizations from the DOM into parameterized programs and (2) develop interactive thematic maps through direct manipulation. Along the way, we’ll discuss the unique compilation challenges that come with treating visual inputs as source programs. We’ll also share lessons learned from our time embedding with data journalists.

Parker Ziegler
Ph.D. Student, University of California, Berkeley
@parker_ziegler

Parker Ziegler is a Ph.D. student in Computer Science at the University of California, Berkeley, where he is advised by Professor Sarah E. Chasins. His research focuses on developing novel programming languages and programming interfaces for data journalism, with an emphasis on geospatial analysis and cartographic design. His research has been published in the ACM CHI Conference on Human Factors in Computing Systems and the Journal of the American Association of Geographers. Prior to beginning his Ph.D., Parker was a software engineer at Formidable working on next-generation open-source tools and libraries in the TypeScript, React, GraphQL, and Node.js ecosystems. He is also a StrangeLoop alum, having spoken about his work in the ReasonML community at StrangeLoop 2019.

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Recorded Sept 21, 2023 at Strange Loop 2023 in St. Louis, MO.
https://thestrangeloop.com