Interaction is critical for data analysis and sensemaking. However, designing interactive physicalizations is challenging as it
requires cross-disciplinary knowledge in visualization, fabrication, and electronics. Interactive physicalizations are typically produced in
an unstructured manner, resulting in unique solutions for a specific dataset, problem, or interaction that cannot be easily extended or
adapted to new scenarios or future physicalizations. To mitigate these challenges, we introduce a computational design pipeline to 3D
print network physicalizations with integrated sensing capabilities. Networks are ubiquitous, yet their complex geometry also requires
significant engineering considerations to provide intuitive, effective interactions for exploration. Using our pipeline, designers can readily
produce network physicalizations supporting selection—the most critical atomic operation for interaction—by touch through capacitive
sensing and computational inference. Our computational design pipeline introduces a new design paradigm by concurrently considering
the form and interactivity of a physicalization during fabrication. We evaluate our approach using (i) computational evaluations, (ii) three
usage scenarios focusing on general visualization tasks, and (iii) expert interviews. The proposed design paradigm shift enables us to
produce generalizable techniques that can lower the barrier to physicalization research, creation, and adoption.
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