In order to explore the current income gap, a method based on the PSO algorithm in the edge computing environment is proposed. PSO calculates and simulates bird flock foraging activities, the Frank Heppner biological group model, and the three rules in bird activities. After studying the activities of these natural creatures, abstract problems are quantified and similar models established. The Gini coefficient is calculated by using grouped data, and the grouping basis is also innovative. The quantile grouping method is adopted, which can effectively solve the difference between the concentration index and the Gini coefficient, and the Gini coefficients of each year can be added up to finally get the Gini coefficient of the stock income. Experimental results show that the Gini coefficient of traffic income in 2017 and 2018 had dropped significantly, but the variation of the Gini coefficient of stock income (Delta CG) was still greater than 0. Obviously, the adjustment speed of the Gini coefficient of stock income was lagging behind, as was the Gini coefficient of traffic income. We found that after 1986, the facilitation effect was greater than the dilution effect, and the facilitation effect continued to push up the stock income gap, which indicated more income flow to the high-income group, with the income flow gap showing an upward trend and the upward trend becoming more and more obvious. It has been proved that the PSO algorithm can effectively identify the income gap in the edge computing environment, and the corresponding policy suggestions are given.
With the rapid development of China’s economy, urbanization is gradually accelerating, but the income gap between urban and rural areas is growing, which may constrain economic development. To test the impact of urbanization on the urban–rural income gap, this paper uses panel data of 31 provinces (cities/autonomous regions) in China from 2007 to 2018, and combines ArcGIS technology to construct Spatial Dubin Model. This paper finds that the increase in urbanization level in China can significantly reduce the urban–rural income gap. The mediating effects model further shows that the increased level of urbanization in China promotes the flow of factors, which helps the flow of capital and advanced technology into the countryside and increases productivity. It also promotes the transfer of a large amount of surplus labor to the tertiary sector, adjusting the industrial structure and increasing the income of peasants moving to the city. In addition, the development of urbanization in China can lead to the construction of public education, improving the conditions and quality of education and teaching, and increasing the possibilities for farmers to earn high incomes. As a result, the urban–rural income gap in China has been narrowed. The findings of this paper are useful for understanding the underlying mechanisms in the level of urbanization and the urban–rural income gap in China. It provides policy insights for accelerating China’s new urbanization process and promoting the coordinated development of China’s urban and rural regions.
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