Visualization in Motion in Video Games for Different Types of Data
Federica Bucchieri, Lijie Yao, Petra Isenberg
TL;DR
The paper addresses how motion affects the readability of visualizations conveying data in video games, aiming to understand its impact on player performance. It employs a systematic review of 160 moving visualizations from 50 games across 17 genres, collecting data via Metacritic top-title selections from 2011–2022. The analysis reveals that quantitative data (notably health) are commonly represented by bars, while categorical data (e.g., game element types) are often encoded with signs and color cues; motion factors and transparency influence readability. The work contributes to motion-aware visualization design in games and motivates an empirical readability study to validate representations under motion and to develop actionable guidelines for game designers.
Abstract
We contribute an analysis of situated visualizations in motion in video games for different types of data, with a focus on quantitative and categorical data representations. Video games convey a lot of data to players, to help them succeed in the game. These visualizations frequently move across the screen due to camera changes or because the game elements themselves move. Our ultimate goal is to understand how motion factors affect visualization readability in video games and subsequently the players' performance in the game. We started our work by surveying the characteristics of how motion currently influences which kind of data representations in video games. We conducted a systematic review of 160 visualizations in motion in video games and extracted patterns and considerations regarding was what, and how visualizations currently exhibit motion factors in video games.