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Transforming Large-Scale Participation Data Through Topic Modelling in Urban Design Processes

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Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries (CAAD Futures 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1819))

Abstract

The advancements in digital tools and data collection methods ensure the continuing growth of textual data obtained through large-scale participation processes in urban contexts. In order to extract the thematic content of such underutilized textual datasets, topic modeling (TM) and content analysis have been deployed as promising AI-based Natural Language Processing (NLP) techniques. Yet, implementing such techniques has not been exploited in urban design domains due to the complexity of textual datasets and the lack of a systematic evaluation framework. In this paper, we addressed the challenges in the utilization of large textual data by using a real-world dataset collected via a digital participation platform in Madrid, Spain. Firstly, we identified prominent data structures and potential information embedded into the dataset by using a document-oriented NoSQL database. In this step, we systematically discussed data pre-processing steps to convert them into a series of structured data collections. Secondly, we evaluated three different TM algorithms, i.e. LDA, LSI, and HDP, according to a number of hyperparameters controlling the learning process. This step aimed to reveal the required number of topics to extract meaningful content through the algorithms. Lastly, we presented possible textual data visualization techniques to enable the use of textual information in digital participation processes. Consequently, this paper facilitates the use of large textual datasets by investigating data structures & processing, revealing the potentials of different TM algorithms, and eventually analyzing the results with the support of urban big data analytics and computational linguistic techniques for informed urban design processes.

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Acknowledgment

This research is supported by “Designing mobile-friendly cartograms for visualising geospatial data” Grant, from the Ministry of Education, Singapore, under its Academic Research Fund Tier 2 programme (award number MOE-T2EP20221-0007) and by Singapore International Graduate Award (SINGA).

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Authors and Affiliations

  1. Architecture and Sustainable Design, Singapore University of Technology and Design, Singapore, Singapore

    Cem Ataman & Bige Tunçer

  2. Information Systems Technology and Design, Singapore University of Technology and Design, Singapore, Singapore

    Simon Perrault

Authors
  1. Cem Ataman
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  2. Bige Tunçer
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  3. Simon Perrault
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Corresponding author

Correspondence to Cem Ataman .

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Editors and Affiliations

  1. Delft University of Technology, Delft, The Netherlands

    Michela Turrin

  2. Delft University of Technology, Delft, The Netherlands

    Charalampos Andriotis

  3. Delft University of Technology, Delft, The Netherlands

    Azarakhsh Rafiee

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Ataman, C., Tunçer, B., Perrault, S. (2023). Transforming Large-Scale Participation Data Through Topic Modelling in Urban Design Processes. In: Turrin, M., Andriotis, C., Rafiee, A. (eds) Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries. CAAD Futures 2023. Communications in Computer and Information Science, vol 1819. Springer, Cham. https://doi.org/10.1007/978-3-031-37189-9_18

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  • DOI: https://doi.org/10.1007/978-3-031-37189-9_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37188-2

  • Online ISBN: 978-3-031-37189-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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