# A Formation Mechanism of Spatial Distribution Pattern of Industrial Clusters under Flow Space
## 3 Materials and Methods
### Results of the Causal Mechanism of Industrial Aggregation  
In this paper, the interaction relationship between city-scale industrial agglomeration intensity and influence factors is analyzed taking into account the interaction network of those influence factors. Figure 8a shows the results. The models of Hefei, Nanjing, Suzhou, Shanghai, and Jinhua have the highest goodness of fit, and the models of Hangzhou, Lishui, Shaoxing, Ningbo, Quzhou, Suqian, and Huai'an have the lowest goodness of fit; however, the lowest goodness of fit is also greater than 0.7, indicating that the regression relationship between the current industrial agglomeration and the impact factor can explain the variation of the dependent variable to some extent, that means the measure is relatively accurate, and the regression \(p\)-values are all close to 0, indicating that the multivariate linear equation is significant and credible in physical space. In this paper, we analyze in detail three factors that have a greater degree of influence on industrial agglomeration. These factors are the economic and technological level, technological innovation ability, and human resource level. 1. Figure 8c shows that the economic development level has a greater influence on the industrial agglomeration intensity of Jinhua, Shanghai, Suzhou, Wuxi, Bozhou, Huaian, and Lianyungang cities. When such cities want to develop local industries vigorously or introduce other industries, increasing local economic investment is a relatively direct way. However, the economic development level has a relatively weak influence for Fuyang, Hangzhou, Huangshan, and Zhenjiang. This means that the intensity of industrial agglomeration in these cities is relatively insensitive to the level of economic development, and it is inferred that the current level of economic input is relatively saturated, and continued input will result in market economy redundancy and a waste of resources. Therefore, these cities should find a breakthrough to strengthen industrial agglomeration from other perspectives;  
Figure 7: Results and analysis of the inter-city technology flow trend network: (**a**) Trend intensity of integrated technology mobility; (**b**) Trend of technology outflow; (**c**) Trend of technology inflow. 2. Figure 8b shows that the level of technological development has a greater impact on the industrial gathering intensity of Shanghai, Suzhou, Quzhou, Lishui, Wenzhou, and Nanjing, indicating that there is a technological deficiency in industries in these cities, and the appropriate introduction of high technology can influence industrial gathering. Therefore, we can summarize that technological innovation development is an important influencing factor in the development of industrial gathering in these cities;
3. Figure 8d shows that the cities with great influence of human resources on industrial agglomeration intensity are mainly Shanghai, Suzhou, Hefei, Yancheng, Jinhua, Lu'an and Anqing. Therefore, there has been a huge demand for a labor force in these cities, and a possible reason is the lack of local labor resources. These cities can attract and  
Figure 8: Results of the causal mechanism of industrial aggregation: (**a**) Fitting effect; (**b**) Sensitivity to the level of technological innovation; (**c**) Sensitivity to the level of economic development; (**d**) Sensitivity to the level of human resource. concentrate a large number of human resources through government decision-making and the migration of labor resources is realized by means of inter-city communication. The effect of 'borrowing' can help realize the vigorous development of the labor force industry. However, the level of human resources has relatively less influence on industrial gathering in Nanjing, Hangzhou, Suzhou, and Fuyang. In this study, the sensitivity of cities to three types of influencing factors is summarized.