That's Rough! Encoding Data into Roughness for Physicalization

Xiaojiao Du, Kadek Ananta Satriadi, Adam Drogemuller, Brandon Matthews, Ross T. Smith, James Walsh, Andrew Cunningham

CHI Conference on Human Factors in Computing Systems. Holulu, Hawaii. 2024.

While visual channels (e.g., color, shape, size) have been explored for visualizing data in data physicalizations, there is a lack of understanding regarding how to encode data into physical material properties (e.g., roughness, hardness). This understanding is critical for ensuring data is correctly communicated and for potentially extending the channels and bandwidth available for encoding that data. We present a method to encode ordinal data into roughness, validated through user studies. In the first study, we identified just noticeable differences in perceived roughness from this method. In the second study, we 3D-printed proof of concepts for five different multivariate physicalizations using the model. These physicalizations were qualitatively explored (N=10) to understand people's comprehension and impressions of the roughness channel. Our findings suggest roughness may be used for certain types of data encoding, and the context of the data can impact how people interpret roughness mapping direction.

Honorable Mention Award