An Identification of Industrial Functional Zones Based on NLP: Evidence From Online Commercial Registration Data

Yuanting Ma, Yong Sun, Futian Weng and Yan Xu

SAGE Open, 2023, vol. 13, issue 1, 21582440231153854

Abstract: The identification of spatial layout and functional characteristics among industrial clusters is vital to support the development of regional industries. Based on the industrial registration data of more than 330,000 companies in “The First Industrial Clusters in China,†and natural language processing methods (NLP), a set of identification framework for urban industrial functional zones is constructed by introducing commercial registration data and electronic map location data to deal with complex industrial big data, realizing the recognition of industrial spatial layout and functional characteristics of Nanshan. The results show that the industrial coverage of Nanshan is as high as 79.07%, and the wholesale and retail enterprises are its main bodies. Among the nine industrial functional zones, emerging enterprises accounted for the majority and diversified agglomeration areas were more than specialized agglomerations in capital scale and employability. Thus, in future industrial planning, maintaining a diversified industrial agglomeration, while giving more policy favor to functional zones characterized by wholesale and retail, can better stimulate consumption and promote economic development in the region.

Keywords: industrial functional zones; urban governance; Place2vec; NLP; Nanshan (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/21582440231153854 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:13:y:2023:i:1:p:21582440231153854

DOI: 10.1177/21582440231153854

Access Statistics for this article

More articles in SAGE Open
Bibliographic data for series maintained by SAGE Publications ().