An analysis of spatial changes in the manufacturing industry in china's three major urban clusters from 2015 to 2019...

China's manufacturing industry has been ranked as the most valuable in the world from 2010 to 2021. However, high-resolution manufacturing datasets are lacking, and this has precluded study of the survival rates and spatial changes in the manufacturing industry in China. Here, we analyzed spatial patterns of the manufacturing industry using point-of-interest (POI) data and a machine learning classification algorithm based on the Naive Bayes classifier. Using 2,780,266 POI data points in 2015 and 3,426,501 POI data points in 2019 covering the three major urban clusters in China, we classified the manufacturing industry data into seven categories: textile and garment (TC), mechatronics and equipment (ME), wood furniture (WF), agricultural and sideline product food processing (AF), metallurgical chemical industry and resource rough processing (MC), pharmaceutical manufacturing (PM), and papermaking culture (PP). The evolution of the manufacturing industry at the scale of 451 districts and counties in the urban clusters and the factors driving new entrants in the manufacturing industry were studied. The main conclusions were as follows. (1) Between 2015 and 2019, manufacturing activities in the three major urban clusters were highly concentrated in provincial capitals, municipalities under direct control of the central government, and their neighboring districts and counties with favorable economic conditions; incremental growth was concentrated in the core cities. (2) The survival rate of enterprises in the Beijing-Tianjin-Hebei urban cluster was relatively high, whereas that in the Pearl River Delta urban cluster was low. Enterprises in the PM industry had a relatively high survival rate, whereas those in the ME industry had a relatively low survival rate. (3) Analysis of the factors driving new entrants in the manufacturing industry indicates that the industrial foundation is the core factor affecting the entry of new manufacturing enterprises. Land transfer policies and high population density promote the development of the manufacturing industry, and regions with high per capita GDP and more research institutions tend to inhibit the development of the manufacturing industry. Further regressions showed that the effects of the proportion of the secondary industry in GDP, the number of development zones, and the number of research institutions on the different urban clusters varied. This paper provides strategic guidance for the future development of China's manufacturing industry, which will help the government and planning departments optimize the layout of the manufacturing industry, promote the development of the regional economy, and enhance the sustainability of the manufacturing industry.