• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Spatial Characteristics and Driving Forces of Cultivated Land Changes by Coupling Spatial Autocorrelation Model and Spatial-temporal Big Data


Abstract

With the rapid development of information technology, it is now possible to analyze the spatial patterns of cultivated land and its evolution by combining GIS, geostatistical analysis models and spatiotemporal big data for the dynamic monitoring and management of cultivated land resources. The spatial pattern of cultivated land and its evolutionary patterns in Luoyang City, China from 2009 to 2019 were analyzed using spatial autocorrelation and spatial autoregressive models on the basis of GIS technology. It was found that: (1) the area of cultivated land in Luoyang decreased then increased between 2009 and 2019, with an overall increase of 0.43% in 2019 compared to 2009, with cultivated land being dominant in the overall landscape of Luoyang; (2) cultivated land holdings in Luoyang are highly spatially autocorrelated, with the ‘high-high’-type area being concentrated in the border area directly north and northeast of Luoyang, while the ‘low-low’-type area is concentrated in the south and in the municipal area of Luoyang, and being heavily influenced by topography and urbanization. The expansion determined during the study period mainly took place in the Luoyang City, with most of it being transferred from the ‘high-low’-type area; (3) elevation, slope and industrial output values from analysis of the bivariate spatial autocorrelation and spatial autoregressive models of the drivers all had significant effects on the amount of cultivated land holdings, with elevation having a positive effect, and slope and industrial output having a negative effect.


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Cite this article

[IEEE Style]
W. Hua, Z. Yuxin, W. Mengyu, N. Jiqiang, C. Xueye and Z. Yang, "Spatial Characteristics and Driving Forces of Cultivated Land Changes by Coupling Spatial Autocorrelation Model and Spatial-temporal Big Data," KSII Transactions on Internet and Information Systems, vol. 15, no. 2, pp. 767-785, 2021. DOI: 10.3837/tiis.2021.02.021.

[ACM Style]
Wang Hua, Zhu Yuxin, Wang Mengyu, Niu Jiqiang, Chen Xueye, and Zhang Yang. 2021. Spatial Characteristics and Driving Forces of Cultivated Land Changes by Coupling Spatial Autocorrelation Model and Spatial-temporal Big Data. KSII Transactions on Internet and Information Systems, 15, 2, (2021), 767-785. DOI: 10.3837/tiis.2021.02.021.