Research of Spatial Pattern for Cultivated Land Quality in Henan Province Based on Spatial Autocorrelation

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Artificial Intelligence and Security (ICAIS 2021)

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Abstract

The quality of cultivated land determines the productivity and level of regional development, and it also directly affects the country’s food security and ecological security. In order to improve the quality of cultivated land and the efficiency of cultivated land zoning protection, this reserch starts from the perspective of spatial pattern, uses the spatial correlation analysis method to study the county scale space gathered characteristics and difference of cultivated land quality in Henan province and put forward more effective zoning protection measures accordingly. The results show that the spatial distribution of cultivated land quality in Henan province has an obvious aggregation law. Positive correlation type (high - high and low - low) take the form of “group”, more strong clustering; the negative correlation type has no obvious concentrated region and shows a discrete state. Then according to the results of local spatial autocorrelation of cultivated land quality, the cultivated land protection is divided into four types in this paper. The conservation measures proposed in this research has comprehensively considered natural quality, economic conditions, utilization level and the effects of spatial attributes of cultivated land, and provide a more scientific reference for refined cultivated land management in Henan Province.

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Funding

The research was Supported by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Grant No. (KF-2019–04-038). This research was also funded by Key scientific and technological projects in Henan Province, Grant No. (212102210105).

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Hua, W., Yuxin, Z., Mengyu, W., Yifan, W., Xueye, C. (2021). Research of Spatial Pattern for Cultivated Land Quality in Henan Province Based on Spatial Autocorrelation. In: Sun, X., Zhang, X., **a, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_37

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  • DOI: https://doi.org/10.1007/978-3-030-78609-0_37

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