In order to measure the degree of industrial agglomeration as well as the spatial location of industrial agglomeration, this paper proposes the central gravity index method by integrating two kinds of industrial spatial agglomeration measurement methods based on output value and distance. The purpose of this research is to design a central gravity index algorithm for distributed collaborative industrial space integration. In the present study, the data of the industrial data of North ‐ West, Tianjin, Hebei, Yangtze River Delta and Zhu Jiang Delta were surveyed, and the data were verified and verified using the industrial accumulation measurement and the central gravity index algorithm. The results show that SVM has the strongest learning ability, and has good performance in precision (84%), recall (84%) and f1measure (84%); the learning ability of KNN is second only to SVM, and precision (82%), recall (82%) and f1measure (82%) have good performance. It is concluded that the central gravity index algorithm of spatial agglomeration in this study improves the existing industrial spatial agglomeration measurement methods, and makes up for the defects that the output value measurement method ignores the spatial distribution, while the distance based method does not use enough output weight and cannot determine the geographical location; through the geographical location of virtual center enterprises, it provides a new method for tracking the geographical path of industrial transfer,  and contribute to the research of regional integration.

Laura Hoekstra. Gravity Index Algorithm of Industrial Spatial Agglomeration Center Based on Distributed Collaboration. Distributed Processing System (2021), Vol. 2, Issue 1: 24-41. https://doi.org/10.38007/DPS.2021.020103.

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