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Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression

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PiCCL: Data-Driven Composition of Bespoke Pictorial Charts.

Haoyan Shi, Yunhai Wang, Junhao Chen

IEEE Transactions on Visualization and Computer Graphics

|November 21, 2025

Summary

This study introduces Pictorial Chart Composition Language (PiCCL), a novel system for easily creating pictorial charts. PiCCL simplifies chart creation through parametric representation and automatic data synthesis, enhancing usability.

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Area of Science:

Computer Graphics
Data Visualization
Human-Computer Interaction

Background:

Creating expressive pictorial charts often involves complex manual composition and fine-tuning of visual properties.
Existing methods lack systematic approaches for integrating data-driven generation with graphical composition.
There is a need for intuitive tools to facilitate the creation of sophisticated pictorial charts.

Purpose of the Study:

To introduce Pictorial Chart Composition Language (PiCCL), a novel language for simplified pictorial chart creation.
To address the challenges of manual composition and fine-tuning in pictorial chart authoring.
To enable systematic construction of data-driven pictorial charts with integrated graphical composition.

Main Methods:

Developed PiCCL, a language featuring a parametric representation for integrating data-driven generation and graphical composition.
Implemented PiCCL as piccl.js, a JavaScript-based library.
Conducted a comprehensive analysis of real-world pictorial chart examples to ground the language design.

Main Results:

PiCCL enables users to easily create pictorial charts using simple operators.
A lazy data-binding mechanism in PiCCL automatically synthesizes charts.
A user study demonstrated the usability of the piccl.js implementation.

Conclusions:

PiCCL offers a systematic and user-friendly approach to authoring expressive pictorial charts.
The parametric representation and lazy data-binding mechanism effectively address key authoring challenges.
Further research can explore PiCCL's potential and limitations for advanced data visualization applications.