Cai Meng
School of Planning, Design and Construction, Michigan State University, East Lansing, Michigan, 48824, United States.
Heliyon. 2021 Mar 8;7(3):e06322. doi: 10.1016/j.heliyon.2021.e06322. eCollection 2021 Mar.
Natural language processing (NLP) has shown potential as a promising tool to exploit under-utilized urban data sources. This paper presents a systematic review of urban studies published in peer-reviewed journals and conference proceedings that adopted NLP. The review suggests that the application of NLP in studying cities is still in its infancy. Current applications fell into five areas: urban governance and management, public health, land use and functional zones, mobility, and urban design. NLP demonstrates the advantages of improving the usability of urban big data sources, expanding study scales, and reducing research costs. On the other hand, to take advantage of NLP, urban researchers face challenges of raising good research questions, overcoming data incompleteness, inaccessibility, and non-representativeness, immature NLP techniques, and computational skill requirements. This review is among the first efforts intended to provide an overview of existing applications and challenges for advancing urban research through the adoption of NLP.
自然语言处理（NLP）已展现出作为一种有前景的工具来挖掘未充分利用的城市数据源的潜力。本文对发表在同行评审期刊和会议论文集上采用NLP的城市研究进行了系统综述。该综述表明，NLP在城市研究中的应用仍处于起步阶段。当前的应用分为五个领域：城市治理与管理、公共卫生、土地利用与功能区、交通出行以及城市设计。NLP展示了提高城市大数据源可用性、扩大研究规模和降低研究成本的优势。另一方面，为了利用NLP，城市研究人员面临着提出好的研究问题、克服数据不完整、不可获取和缺乏代表性、NLP技术不成熟以及计算技能要求等挑战。本综述是旨在概述通过采用NLP推进城市研究的现有应用和挑战的首批努力之一。
