Investigating the spatial distribution characteristics and influencing factors of various industry types is critical for promoting the high-quality transformation and development of China's industry. This study combined the Getis-Ord Gi* statistic method, the random forest-based importance assessment method, and the geographically weighted regression method to determine the spatial distribution characteristics of four industry types and their influencing factors. The results revealed that the raw material industry was primarily concentrated in the surrounding districts and counties of Linyi and Qingdao. The food and light textile industry was mainly concentrated in the surrounding districts and counties of Qingdao, and a few were concentrated in some counties of Linyi. The processing and manufacturing industry was also concentrated in the surrounding districts and counties of Qingdao, and a few were concentrated in the belt regions connecting Jinan, Zibo, and Weifang. The high-tech industry was mainly concentrated in the surrounding districts and counties of Jinan and Qingdao. The key spatial influencing factors of the four industry types were different. The number of employees in the secondary industry and road density were most important in determining the spatial distribution of the raw material industry. The financial environment and number of research institutions were most important to the spatial distribution of the food and light textile industry. The gross domestic product and number of medical facilities were most important to the spatial distribution of the processing and manufacturing industry. Urbanization rate, number of research institutions, and gross domestic product were most important to the spatial distribution of the high-tech industry. Geographically weighted regression analysis revealed that the impact intensity of these key factors on the industry exhibits significant spatial heterogeneity. Taken together, these results are useful for formulating the development strategy for each industrial type in different regions.
Copyright: © 2023 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.