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DS-Viz: a method for visualising design spaces

Published online by Cambridge University Press:  16 May 2024

Esdras Paravizo  and
Nathan Crilly
Esdras Paravizo*
Affiliation:
University of Cambridge, United Kingdom
Nathan Crilly
Affiliation:
University of Cambridge, United Kingdom
*
ep650@cam.ac.uk

Abstract

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Problems, solutions, and design itself have been framed as spaces in design research. Visualising the design space and how designers explore it, can give insight into the design process. This paper reports on a novel method for creating Design Space Visualisations (DS-Viz) that generates 2D and 3D representations of design spaces. We show how DS-Viz can be used to investigate designer behaviour, design processes and outcomes using a game-based design activity as an example. We discuss DS-Viz implications for design research highlighting potential benefits to design education and practice.

Keywords

design space explorationdesign landscapesdesign creativitydesign searchidea generation

Information

Type
Human Behaviour and Design Creativity
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 1035 - 1044
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Agrawal, A. and McComb, C. (2022), “Comparing Strategies for Visualizing the High-Dimensional Exploration Behavior of CPS Design Agents”, 2022 IEEE Workshop on Design Automation for CPS and IoT (DESTION), IEEE, Milano, Italy, pp. 64–69, https://dx.doi.org/10.1109/DESTION56136.2022.00017.CrossRefGoogle Scholar
Agrawal, A. and McComb, C. (2023), “Reinforcement Learning for Efficient Design Space Exploration With Variable Fidelity Analysis Models”, Journal of Computing and Information Science in Engineering, Vol. 23 No. 4, p. 041004, https://dx.doi.org/10.1115/1.4056297.CrossRefGoogle Scholar
Bayırlı, Ü. and Börekçi, N.A.G.Z. (2022), “Correlation between idea generation effort and resulting design solution success: An empirical study using RNEV as a new assessment technique”, Thinking Skills and Creativity, Vol. 44, p. 101036, https://dx.doi.org/10.1016/j.tsc.2022.101036.CrossRefGoogle Scholar
Beaty, R.E., Johnson, D.R., Zeitlen, D.C. and Forthmann, B. (2022), “Semantic Distance and the Alternate Uses Task: Recommendations for Reliable Automated Assessment of Originality”, Creativity Research Journal, Vol. 34 No. 3, pp. 245–260, https://dx.doi.org/10.1080/10400419.2022.2025720.CrossRefGoogle Scholar
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I.W.H., Ng, L.G., Ginhoux, F., et al. (2019), “Dimensionality reduction for visualizing single-cell data using UMAP”, Nature Biotechnology, Vol. 37 No. 1, pp. 38–44, https://dx.doi.org/10.1038/nbt.4314.CrossRefGoogle Scholar
Danhaive, R. and Mueller, C.T. (2021), “Design subspace learning: Structural design space exploration using performance-conditioned generative modeling”, Automation in Construction, Vol. 127, p. 103664, https://dx.doi.org/10.1016/j.autcon.2021.103664.CrossRefGoogle Scholar
Fischer, G.R., Kipouros, T. and Savill, A.M. (2014), “Multi-objective optimisation of horizontal axis wind turbine structure and energy production using aerofoil and blade properties as design variables”, Renewable Energy, Vol. 62, pp. 506–515, https://dx.doi.org/10.1016/j.renene.2013.08.009.CrossRefGoogle Scholar
Gero, J. and Milovanovic, J. (2022), “Creation and characterization of design spaces”, presented at the DRS2022: Bilbao, https://dx.doi.org/10.21606/drs.2022.265.CrossRefGoogle Scholar
Goldschmidt, G. (2006), “Quo vadis, design space explorer?”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 20 No. 2, pp. 105–111, https://dx.doi.org/10.1017/S0890060406060094.CrossRefGoogle Scholar
Halskov, K. and Lundqvist, C. (2021), “Filtering and Informing the Design Space: Towards Design-Space Thinking”, ACM Transactions on Computer-Human Interaction, Vol. 28 No. 1, pp. 1–28, https://dx.doi.org/10.1145/3434462.CrossRefGoogle Scholar
Hatchuel, A. and Weil, B. (2009), “C-K design theory: an advanced formulation”, Research in Engineering Design, Vol. 19 No. 4, pp. 181–192, https://dx.doi.org/10.1007/s00163-008-0043-4.CrossRefGoogle Scholar
Hay, L., Duffy, A.H.B., Grealy, M., Tahsiri, M., McTeague, C. and Vuletic, T. (2020), “A novel systematic approach for analysing exploratory design ideation”, Journal of Engineering Design, Vol. 31 No. 3, pp. 127–149, https://dx.doi.org/10.1080/09544828.2019.1662381.CrossRefGoogle Scholar
Huang, H., Wang, Y., Rudin, C. and Browne, E.P. (2022), “Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization”, Communications Biology, Vol. 5 No. 1, p. 719, https://dx.doi.org/10.1038/s42003-022-03628-x.CrossRefGoogle ScholarPubMed
Hummel, M., Edelmann, D. and Kopp-Schneider, A. (2017), “Clustering of samples and variables with mixed-type data”, edited by Deng, Z. PLOS ONE, Vol. 12 No. 11, p. e0188274, https://dx.doi.org/10.1371/journal.pone.0188274.Google Scholar
Jansson, D.G. and Smith, S.M. (1991), “Design fixation”, Design Studies, Vol. 12 No. 1, pp. 3–11, https://dx.doi.org/10.1016/0142-694X(91)90003-F.CrossRefGoogle Scholar
Kosub, S. (2016), “A note on the triangle inequality for the Jaccard distance”, arXiv, 8 December.Google Scholar
Kyriakou, H., Nickerson, J. and Majchrzak, A. (2022), “Novelty and the Structure of Design Landscapes: A Relational View of Online Innovation Communities”, MIS Quarterly, Vol. 45 No. 3, pp. 1691–1720, https://dx.doi.org/10.25300/MISQ/2022/15059.CrossRefGoogle Scholar
Maher, M.L. and Poon, J. (1996), “Modeling Design Exploration as Co-Evolution”, Microcomputers in Civil Engineering, Vol. 11, pp. 195–209.CrossRefGoogle Scholar
Munzner, T. (2014), Visualization Analysis and Design, 0 ed., A K Peters/CRC Press, https://dx.doi.org/10.1201/b17511.CrossRefGoogle Scholar
Nadel, S. and Pritchett, L. (2016), “Searching for the Devil in the Details: Learning About Development Program Design”, SSRN Electronic Journal, https://dx.doi.org/10.2139/ssrn.2847122.CrossRefGoogle Scholar
Nickel, J., Duimering, P.R. and Hurst, A. (2022), “Manipulating the design space to resolve trade-offs: Theory and evidence”, Design Studies, Vol. 79, p. 101095, https://dx.doi.org/10.1016/j.destud.2022.101095.CrossRefGoogle Scholar
Paravizo, E. and Crilly, N. (2023), “The effects of creative performance feedback on design outcomes”, Registration, Open Science Framework, https://dx.doi.org/10.17605/OSF.IO/ZAVXC.CrossRefGoogle Scholar
Romero, P.A. and Arnold, F.H. (2009), “Exploring protein fitness landscapes by directed evolution”, Nature Reviews Molecular Cell Biology, Vol. 10 No. 12, pp. 866–876, https://dx.doi.org/10.1038/nrm2805.CrossRefGoogle ScholarPubMed
Shah, J.J., Smith, S.M. and Vargas-Hernandez, N. (2003), “Metrics for measuring ideation effectiveness”, Design Studies, Vol. 24 No. 2, pp. 111–134, https://dx.doi.org/10.1016/S0142-694X(02)00034-0.CrossRefGoogle Scholar
Simon, H.A. (2019), The Sciences of the Artificial, Third edition., The MIT Press, Cambridge, Massachusetts.CrossRefGoogle Scholar
Westerlund, B. (2005), “Design space conceptual tool – grasping the design process”, presented at the Nordes 2005: In the Making, https://dx.doi.org/10.21606/nordes.2005.048.CrossRefGoogle Scholar