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Transforming Urban Planning through Machine Learning: A Study on Planning Application Classification using Natural Language Processing

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  • Lin, Yang
  • Thackway, William
  • Soundararaj, Balamurugan
  • Eagleson, Serryn
  • Han, Hoon
  • Pettit, Christopher

Abstract

Planning for sustainable urban growth is a pressing challenge facing many cities. Investigating proposed changes to the built environment can provide planners and policymakers information to understand future urban development trends and related infrastructure requirements. It is in this context we have developed a novel urban analytics approach that utilises planning applications (PAs) data and Natural Language Processing (NLP) techniques to forecast the housing supply pipeline in Australia. Firstly, we implement a data processing pipeline which scrapes, geocodes, and filters PA data from council websites and planning portals to provide the first nationally available daily dataset of PAs that are currently under consideration. Secondly, we classify the collected PAs into four distinct urban development categories, selected based on infrastructure planning provisioning requirements. Of the five model architectures tested, we found that the fine-tuned DeBERTA-v3 model achieves the best performance with an accuracy and F1-score of 0.944. This demonstrates the suitability of fine-tuned Pre-trained Language Models (PLMs) for planning text classification tasks. Finally, the model is applied to classify and map urban development trends in Australia’s two largest cities, Sydney and Melbourne, from 2021-2022 and 2023-2024. The mapping affirms a face-validation test of the classification model and demonstrates the utility of PA insights for planners. Holistically, the paper demonstrates the potential for NLP to enrich urban analytics through the integration of previously inaccessible planning text data into planning analysis and decisions.

Suggested Citation

  • Lin, Yang & Thackway, William & Soundararaj, Balamurugan & Eagleson, Serryn & Han, Hoon & Pettit, Christopher, 2024. "Transforming Urban Planning through Machine Learning: A Study on Planning Application Classification using Natural Language Processing," OSF Preprints fs76e, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:fs76e
    DOI: 10.31219/osf.io/fs76e
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    References listed on IDEAS

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    1. Murray, Cameron, 2023. "Explainer: Planning and building approvals," OSF Preprints 8tb7v, Center for Open Science.
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    4. Biktimirov, Ernest N. & Sokolyk, Tatyana & Ayanso, Anteneh, 2024. "What is behind housing sentiment?," Finance Research Letters, Elsevier, vol. 60(C).
    5. Jessica Ferm & Ben Clifford & Patricia Canelas & Nicola Livingstone, 2021. "Emerging problematics of deregulating the urban: The case of permitted development in England," Urban Studies, Urban Studies Journal Limited, vol. 58(10), pages 2040-2058, August.
    6. Yingjie Hu & Chengbin Deng & Zhou Zhou, 2019. "A Semantic and Sentiment Analysis on Online Neighborhood Reviews for Understanding the Perceptions of People toward Their Living Environments," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(4), pages 1052-1073, July.
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