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Natural language processing for urban research: A systematic review.

Meng Cai1

  • 1School of Planning, Design and Construction, Michigan State University, East Lansing, Michigan, 48824, United States.

Heliyon
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Natural language processing (NLP) offers potential for urban studies by analyzing big data. However, NLP applications in urban research are nascent, facing challenges in data and technique maturity.

Keywords:
Natural language processingText miningUrban big dataUrban research

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Area of Science:

  • Urban Studies
  • Computational Social Science
  • Geoinformatics

Background:

  • Urban data sources are under-utilized.
  • Natural Language Processing (NLP) shows promise for data analysis.
  • Integrating NLP into urban studies is an emerging field.

Purpose of the Study:

  • To systematically review urban studies using NLP.
  • To identify current applications and challenges of NLP in urban research.
  • To provide an overview for advancing NLP adoption in urban studies.

Main Methods:

  • Systematic literature review of peer-reviewed urban studies.
  • Analysis of studies that adopted NLP techniques.
  • Categorization of NLP applications in urban research.

Main Results:

  • NLP applications in urban studies are in their early stages.
  • Key application areas include urban governance, public health, land use, mobility, and urban design.
  • NLP offers benefits such as enhanced data usability, expanded study scales, and reduced research costs.

Conclusions:

  • Urban research can leverage NLP for big data analysis.
  • Challenges include data quality, NLP technique maturity, and computational skills.
  • Further development is needed to fully realize NLP's potential in urban studies.