ClayPhys: Towards Toolkits that Support Making Expressive Data Physicalization

Paper Title

ClayPhys: Towards Toolkits that Support Making Expressive Data Physicalization

Publication Info

• Topic area: Data physicalization and toolkit design in HCI.
• Keywords: Data physicalization, expressive visualization, toolkit design, clay modeling, tangible user interfaces, interaction design, computational fabrication, creativity support tools, freeform materials, data mapping.

Background and Problem

• Problem / challenge: Existing constructive data physicalization toolkits rely on pre-made tokens, limiting personalization, expressivity, and the types of physical variables and interactions users can create.
• Significance: Supporting expressive and personalized data physicalizations is crucial for enabling creative exploration, reflection, and unique data representations, particularly in personal informatics and casual visualization contexts.
• Motivation and related work: Prior work has focused on ease of use for novices but has constrained expressivity. Studies have shown that flexible materials like clay can foster creativity but require additional support to enable expressive data physicalizations. This paper addresses the gap by exploring how to design toolkits that support expressivity through freeform materials.

Solution

• Proposed approach: ClayPhys, a low-fidelity data physicalization toolkit using clay, clay tools, wire, instruction handbooks, and warm-up activities to scaffold the design process.
• Novelty:
  1. Enables mapping data to diverse visual and physical variables.
  2. Facilitates a wide range of interactions through physical actions and data actions.
  3. Encourages expressive and personalized data representations using freeform materials.
  4. Provides implications for designing higher-fidelity hybrid toolkits integrating technology.
• Procedure and key techniques:
  • Toolkit components include clay, wire, blank canvas, clay tools, data mapping instructions, making instructions, documentation sheets, and a guidance handbook.
  • Warm-up activities familiarize users with clay manipulation techniques.
  • Participants create physicalizations in iterative cycles, exploring data mapping, interaction design, and metaphors.

Results

• Concrete findings:
  • Participants created 27 unique data physicalizations using four physical marks (point, line, area, volume) and 13 physical variables.
  • Nine physical actions (e.g., rotation, linear motion) supported 19 data actions (e.g., comparison, filtering, reflection).
  • Expressive metaphors were used to represent qualitative data (e.g., rough textures for mood, light for emotions).
• Advantage over baselines:
  • Unlike existing toolkits, ClayPhys supports both qualitative and quantitative data representation, diverse interactions, and personalized metaphors.
• Experiments / evaluation:
  • A one-day workshop with nine participants experienced in data visualization and physicalization.
  • Participants iteratively created three physicalizations each, followed by semi-structured interviews and thematic analysis of their creations and processes.
• Limitations and future work:
  • Constraints of air-dry clay (e.g., drying out) and challenges with precision in quantitative data mapping.
  • Limited exploration of other freeform materials and datasets (e.g., network or geospatial data).
  • Future work should explore hybrid toolkits with alternative materials and computational fabrication workflows.

Summary

ClayPhys is a low-fidelity toolkit designed to support expressive and personalized data physicalizations using clay and related tools. In a workshop with nine participants, the toolkit enabled diverse data mappings, interactions, and metaphors, resulting in 27 unique physicalizations. Findings highlight the toolkit's potential for fostering creativity and personalization, while identifying challenges such as precision and material constraints. The paper proposes four design implications for higher-fidelity hybrid toolkits, including personalized physical vocabulary, assembly models for interaction, support for novices and experts, and integration of data representation into computational fabrication workflows. These insights contribute to advancing the design of expressive data physicalization tools in HCI.