# A Formation Mechanism of Spatial Distribution Pattern of Industrial Clusters under Flow Space
###### Abstract  
This study focuses on analyzing the spatial distribution pattern and formation mechanisms of urban industrial clusters and aims to address the mismatch between industrial clusters and resource distribution. Firstly, the spatial distribution pattern of industrial clusters is analyzed using the kernel density estimation approach. Subsequently, a multi-layered model of interactive driving factors is constructed to analyze the functional types within the multi-layered network space. Lastly, a spatial weighted regression analysis model, considering the intensity of flow space, is developed to explore the intrinsic formation mechanisms of industrial agglomeration. The experimental results indicate the following: (1) There is a trend of industrial agglomeration in the Yangtze River Delta region, primarily concentrated in cities such as Shanghai, Nanjing, Hefei, Jinhua, and Taizhou. (2) The impact of spatial interaction factors on industrial agglomeration development is significant, and the analysis of interaction networks reflects the strength of interactive influencing factors to a certain extent. (3) The regression analysis model, which incorporates interactive information considering flow space intensity, better aligns with the study of the actual mechanisms behind industrial agglomeration in physical space.  
enterprise space aggregation pattern; spatial element interaction network; spatial interaction regression model; spatial formation mechanism +
Footnote †: journal: Applied  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-000000-00000-000000-000000-000000-0000  
The industrial agglomeration areas in cities have great demand for urban resources. The urban resources in some areas can hardly meet local industries' demand, making it difficult for industries to develop with high quality. At the same time, industries in some areas can only partially utilize urban resources, making it impossible for them to be used efficiently. Therefore, in order to achieve both high-quality industrial development and high-efficiency utilization of resources, government policymakers usually adopt the way of resource transportation, i.e., to influence the spatial distribution pattern of local factors in urban space. For example, labor-intensive gathering industrial areas have a great attraction to labor resources in local areas and surrounding areas, and influence the distribution pattern of the urban labor force, and the aggregation state of enterprises may also enable enterprises to achieve joint development, alleviate, or even solve social problems, such as labor industries developed on a large scale provide employment opportunities for the unemployed population in cities, converting urban population pressure into human resource advantages and solving unemployment problems.  
Regarding studying the influencing factors and underlying mechanisms of industrial agglomeration, the existing research data are limited and can be broadly classified into two groups: quantitative data [10], which represents the primary influencing factors, and non-quantitative data [11], also known as \"latent factors\". Researchers who use these two types of data mainly treat regions as independent individuals. However, some scholars have already demonstrated the existence of various connections between industrial agglomeration zones, and they have even proposed concepts such as \"borrowed scales\", \"agglomeration benefits\", and \"shadow of agglomeration\" [12; 13] to illustrate the substantial connections between regions. Therefore, analyzing the causal mechanism of industrial agglomeration from the perspective of \"flow space\" [14; 15; 16] is more suitable for the practical situation of industrial agglomeration, this way can help uncover more profound and valuable information.