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Data Analysis of Hybrid Principal Component for Rural Land Circulation Management based on Gray Relation Algorithmic Models

Volume 15, Number 7, July 2019, pp. 1976-1987
DOI: 10.23940/ijpe.19.07.p25.19761987

Zhongbo Wang and Zhilin Suo

College of Economics and Management, Northeast of Agricultural University, Harbin, 150001, China


(Submitted on March 13, 2019; Revised on April 8, 2019; Accepted on June 20, 2019)


Data analysis is a common and essential process of determining the main driving factors of hybrid information principal components for rural land circulation management. To solve the existing hybrid information regarding the problem of rural land circulation in China, the main driving factors need to be confirmed based on gray relation algorithmic models in the paper. Five types of gray relation algorithmic models are adopted for hybrid Information principal component analysis for rural land circulation, such as the Deng’s gray relation algorithmic model, gray absolute relation algorithmic model, T-type gray relation algorithmic model, improved gray relation algorithmic model, and gray slope relation algorithmic model. According to our collected data, the results of data analysis comparison illustrate that different gray relation algorithms may affect the order of the importance of each driving factor. The most critical driving factors are obtained as follows: the rate of non-agricultural income, the ratio of signed contracts and the ratio of peasants’ spontaneous taking part in rural land circulation, which are also the most three main driving factors on the Chinese rural land circulation management.


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