One of the most effective methods for exploratory analysis is visualization. Game data analysis and communication of results of such analysis are not easy processes. The sequential and contextual nature of gameplay actions make it hard to understand the users’ behavior and infer users’ goals, intent, plans, and preferences. In this chapter, we will discuss how visualization can be used to allow analysts to make such inferences. However, to be able to do this, the visualization system itself needs to be capable of handling multi- dimensional spaces. Therefore, in discussing how visualization can be used as part of the analysis process we will also discuss visualization systems and tools that have been proposed and are currently used for this purpose.

Game Data Science book

Player journey maps visualize all player actions in chronological order. Such visualization can help researchers reveal the sequential relationship of player actions and understand their play experiences. This also generalizes to customer journey maps for websites and mobile applications. Visualizations of journey maps can help analyze player behaviors, player strategies, as well as allow designers to validate their own assumptions of player experience.  One critical challenge for such visualizations is the clarity and readability of the visualized graphs. If one visualizes all players' actions in a granular manner, then graphs can become cluttered. INSPECT is an interactive visualization tool that was developed to overcome this problem. It gives designers the ability to generate player journey maps. Moreover, it includes several features that can help simplify graphs and segment players to allow designers to focus on specific player groups. See below for more information and demonstrations. 

The Data Triangulation System is designed to integrate and visualize multiple streams of data from gameplay sessions, including eye movements, emotional variations, player event logs, webcam video (player facial recordings), and gameplay screen captures. The interface consists of four sections:

1. a feature legend for selecting data types,

2. a central area for visualizing timelines of player events and eye-tracking data,

3. a labeling tool for creating annotation marks along the timeline,

4. and an AI assistant that provides insights and context about the project.

The system aligns different data sources on a unified timeline to help researchers better understand player behavior and decision-making processes. The overall goal is to create a flexible tool for analyzing and reflecting on gameplay and learning data.