Advertisement

An Ecology of Conflicts

Using Network Analytics to Explore the Data of Building Design
  • Daniel Cardoso LlachEmail author
  • Javier Argota Sánchez-Vaquerizo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1028)

Abstract

The scale and socio-technical complexity of contemporary architectural production poses challenges to researchers and practitioners interested in their description and analysis. This paper discusses the novel use of network analysis techniques to study a dataset comprising thousands of design conflicts reported during design coordination of a large project by a group of architects using BIM software. We discuss in detail three approaches to the use of network analysis techniques on these data, showing their potential to offer topological insights about the phenomenon of contemporary architectural design and construction, which complement other forms of architectural analysis.

Keywords

Architecture Network analysis Design ecology BIM Data visualization 

Notes

Acknowledgements

The authors wish to thank Dr. Kathleen M. Carley and the CASOS Group at CMU for their valuable guidance in employing network science techniques.

References

  1. 1.
    Cardoso Llach, D.: Tracing design ecologies: collecting and visualizing ephemeral data as a method in design and technology studies. In: Vertesi, J., Ribes, D. (eds.) DigitalSTS Handbook. Princeton University Press (2019)Google Scholar
  2. 2.
    Cardoso Llach, D.: Builders of the Vision: Software and the Imagination of Design. Routledge, London, New York (2015)CrossRefGoogle Scholar
  3. 3.
    Brandes, U., et al.: What is network science? Netw. Sci. 1, 1–15 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Hopkins, A.L.: Network pharmacology. Nat. Biotechnol. 25, 1110–1111 (2007)CrossRefGoogle Scholar
  5. 5.
    Arquilla, J., Ronfeldt, D.: Networks and Netwars: The Future of Terror, Crime, and Militancy. RAND, Santa Monica (2001)Google Scholar
  6. 6.
    Weingart, S.: Topic modeling and network analysis. The Scottbot irregular, 15 November 2011. http://www.scottbot.net/HIAL/?p=221
  7. 7.
    McCallum, A., Corrada-Emmanuel, A., Wang, X.: Topic and role discovery in social networks. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, IJCAI 2005, pp. 786–791. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2005). http://dl.acm.org/citation.cfm?id=1642293.1642419
  8. 8.
    Wu, L., et al.: Mining face-to-face interaction networks using sociometric badges: predicting productivity in an IT configuration task. In: Proceedings of the International Conference on Information Systems (2008)Google Scholar
  9. 9.
    Porta, S., Crucitti, P., Latora, V.: The network analysis of urban streets: a primal approach. Environ. Plann. B: Plann. Des. 33, 705–725 (2006)CrossRefGoogle Scholar
  10. 10.
    Alexander, C.: A city is not a tree. Archit. Forum 122, 58–62 (1965)Google Scholar
  11. 11.
    He, Y., Luo, J.: Novelty, conventionality, and value of invention. In: Gero, J.S. (ed.) Design Computing and Cognition ’16, pp. 23–38. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-44989-0_2CrossRefGoogle Scholar
  12. 12.
    Sarkar, S., Gero, J.S.: The topology of social influence and the dynamics of design product adoption. In: Gero, J.S. (ed.) Design Computing and Cognition ’16, pp. 653–665. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-44989-0_35CrossRefGoogle Scholar
  13. 13.
    Ammon, S., Hinterwaldner, I. (eds.): Architecture and the Structured Image in Imagery in the Age of Modelling: Operative Artifacts in the Design Process in Architecture and Engineering. Springer, Heidelberg (2017)Google Scholar
  14. 14.
    Drieger, P.: Semantic network analysis as a method for visual text analytics. Procedia Soc. Behav. Sci. 79, 4–17 (2013)CrossRefGoogle Scholar
  15. 15.
    Carley, K., et al.: ORA User’s Guide 2013. ISR, SCS, Carnegie Mellon University, Pittsburgh (2013)Google Scholar
  16. 16.
    Borgatti, S.P., Everett, M.G.: Network analysis of 2-mode data. Soc. Netw. 19, 243–269 (1997)CrossRefGoogle Scholar
  17. 17.
    Latapy, M., Magnien, C., Del Vecchio, N.: Basic notions for the analysis of large two-mode networks. Soc. Netw. 30(1), 31–48 (2008)CrossRefGoogle Scholar
  18. 18.
    Eades, P.: A heuristic for graph drawing. Congr. Numer. 42, 149–160 (1984)MathSciNetGoogle Scholar
  19. 19.
    Carley, K.: Smart agents and organizations of the future. Handb. New Media 12, 206–220 (2002)Google Scholar
  20. 20.
    Fagan, S., Gençay, R.: An introduction to textual econometrics. In: Handbook of Empirical Economics and Finance, pp. 133–153. CRC Press (2010)Google Scholar
  21. 21.
    Johnson-Laird, P.: Mental Models. Harvard University Press, Cambridg (1983)Google Scholar
  22. 22.
    Pfitzner, R., et al.: Betweenness preference: quantifying correlations in the topological dynamics of temporal networks. Phys. Rev. Lett. 110, 198701: 1–5 (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Daniel Cardoso Llach
    • 1
    Email author
  • Javier Argota Sánchez-Vaquerizo
    • 1
  1. 1.Computational Design Laboratory, School of ArchitectureCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations