Data Mining of Regional Economic Analysis Based on Mobile Sensor Network Technology

Abstract

With the continuous development of regional economy, the difference of regional economy has also aroused the attention of all walks of life. Due to the limitations of the traditional research methods, the research results are relatively simple and unable to conduct a more comprehensive analysis. The traditional methods include the following: (1) analyze the evolution of regional logistics based on the location Gini coefficient and location quotient of GIS, and reflect the situation of industrial agglomeration from the annual change curve of the location Gini coefficient; (2) use SPSS12.0 software to perform multivariate or event factors, and analyze and calculate the factor score to sum up several principal component factors; and (3) the production function analysis method is used to measure the economies of scale and agglomeration. As an extension, the relationship between the estimated total output and the agglomeration index of the factor input to measure the uniform state of the industrial distribution department is an effective measurement method for the agglomeration economy. In order to promote the sustainable development of regional economy, this paper analyzes the regional economy comprehensively based on the emerging mobile sensor network technology and data mining technology. Firstly, this paper analyzes the location technology of mobile sensor networks based on sequential Monte Carlo, selects the C -means clustering method which is suitable for economic large-sample clustering analysis, and constructs a complete data mining model. Then, the model is used to analyze the economic, social, natural, and educational science and technology indicators of a certain region from 2015 to 2019. The results show that the first principal component weight of economic indicators is the highest proportion of fiscal revenue, which is 0.986. This shows that the role of fiscal revenue in economic indicators is greater. The main index of urban consumption is 72.0, which is the highest. This shows that the population growth rate and the average consumption of urban households in social indicators play a greater role. The first principal component of natural index has the highest weight of pollution emission, which is 0.47, while the second principal component has the highest weight of total energy consumption, which is 0.48. This shows that the pollution emissions and total energy consumption in the natural indicators play a greater role. In the educational science and technology index, the first principal component weight is the highest, which is 0.61. This shows that the education funds play an important role in educational science and technology indicators. Therefore, the data mining model based on mobile sensor network technology can comprehensively and accurately analyze various indicators of regional economy.