Using Natural Language Processing to Read Plans: A Study of 78 Resilience Plans From the 100 Resilient Cities Network

Xinyu Fu, Chaosu Li, Wei Zhai

    Research output: Contribution to journalJournal articlepeer-review

    12 Citations (Scopus)

    Abstract

    Problem, research strategy, and findings

    Planners need to read plans to learn and adapt current practice. Planners may struggle to find time to read and study lengthy planning documents, especially in emerging areas such as climate change and urban resilience. Recently, natural language processing (NLP) has shown promise in processing big textual data. We asked whether planners could use NLP techniques to more efficiently extract useful and reliable information from planning documents. By analyzing 78 resilience plans from the 100 Resilient Cities Network, we found that results generated from topic modeling, which is an NLP technique, coincided to a large extent (80%) with those from the conventional content analysis approach. Topic modeling was generally effective and efficient in extracting the main information of plans, whereas the content analysis approach could find more in-depth details but at the expense of considerable time and effort. We further propose a transferrable model for cutting-edge planners to more efficiently read and study a large collection of plans using machine learning. Our methodology has limitations: Both topic modeling and content analysis can be subject to human bias and generate unreliable results; NLP text processing techniques may create inaccurate results due to their specific method limitations; and the transferable approach can be only applied to big textual data where there are enough sufficiently long documents. 

    Takeaway for practice

    NLP represents a valuable addition to the planner’s toolbox. Topic modeling coupled with other NLP techniques can help planners to effectively discover key topics in plans, identify planning priorities and plans of specific emphasis, and find relevant policies.

    Original languageEnglish
    Pages (from-to)107-119
    Number of pages13
    JournalJournal of the American Planning Association
    Volume89
    Issue number1
    Early online date20 Jun 2022
    DOIs
    Publication statusPublished - 2 Jan 2023

    Scopus Subject Areas

    • Geography, Planning and Development
    • Development
    • Urban Studies

    User-Defined Keywords

    • machine learning
    • natural language processing
    • plan evaluation
    • urban resilience

    Fingerprint

    Dive into the research topics of 'Using Natural Language Processing to Read Plans: A Study of 78 Resilience Plans From the 100 Resilient Cities Network'. Together they form a unique fingerprint.

    Cite this