Dimension Hopper: A Tool for Exploring Multidimensional Data in Design Research

Design research methods frequently involve the collection of field data - such as sensor data or subjective experience - to understand human activities and practices. However, exploring multidimensional data is challenging for designers, as it requires switching between different data types and relating them to identify larger patterns and understand the context behind observations. Our study addresses this problem by presenting Dimension Hopper - an interactive data visualization tool that uses Multiple Coordinated Views (MCV) to facilitate insight discovery. We demonstrate how the tool can aid in data exploration by presenting examples of how it can be used to identify behavioral patterns in field data. We also discuss the challenges that emerged in our initial exploration of the tool, leading to factors that need to be considered when designing visualization tools for exploring multidimensional field data in design research.