Abstract

The growth of the manufacturing industry is the engine of rapid economic growth in developing regions. Characterizing the geographical distribution of manufacturing firms is critically important for scientists and policymakers. However, data on the manufacturing industry used in previous studies either have a low spatial resolution (or fuzzy classification) or high-resolution information is lacking. Here, we propose a map point-of-interest classification method based on machine learning technology and build a dataset of the distribution of Chinese manufacturing firms called the Gridded Manufacturing Dataset. This dataset includes the number and type of manufacturing firms at a 0.01° latitude by 0.01° longitude scale. It includes all manufacturing firms (classified into seven categories) in China in 2015 (4.56 million) and 2019 (6.19 million). This dataset can be used to characterize temporal and spatial patterns in the distribution of manufacturing firms as well as reveal the mechanisms underlying the development of the manufacturing industry and changes in regional economic policies.

Measurement(s)

distribution of manufacturing firms

Technology Type(s)

natural language processing

Details

Title
China’s Gridded Manufacturing Dataset
Author
Fan, Chenjing 1 ; Huang, Xinran 2 ; Zhou, Lin 3 ; Gai, Zhenyu 2 ; Zhu, Chaoyang 4   VIAFID ORCID Logo  ; Zhang, Haole 5 

1  Research Center for Digital Innovation Design, Nanjing Forestry University, Nanjing, China (GRID:grid.410625.4) (ISNI:0000 0001 2293 4910); Nanjing Forestry University, College of Landscape Architecture, Nanjing, China (GRID:grid.410625.4) (ISNI:0000 0001 2293 4910) 
2  Nanjing Forestry University, College of Landscape Architecture, Nanjing, China (GRID:grid.410625.4) (ISNI:0000 0001 2293 4910) 
3  Renmin University of China, School of Public Administration and Policy, Beijing, China (GRID:grid.24539.39) (ISNI:0000 0004 0368 8103); Chinese Academy of Social Sciences, Institute of Industrial Economics, Beijing, China (GRID:grid.418560.e) (ISNI:0000 0004 0368 8015) 
4  Beijing University of Technology, Faculty of Architecture, Civil and Transportation Engineering, Beijing, China (GRID:grid.28703.3e) (ISNI:0000 0000 9040 3743) 
5  Shanghai Tongji Urban Planning & Design Institute CO. LTD, Shanghai, China (GRID:grid.495823.1) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41597-022-01848-8
ProQuest document ID
2745195144
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.