Abstract
Cultivated land is the basis of human survival and security. Taking cultivated land of Luoyang as the research object, based on GIS technology, using landscape pattern index method, kernel density analysis and spatial autocorrelation analysis, this paper analyzes the characteristics and change trend of cultivated land spatial pattern of Luoyang from 2010 to 2018. The research results show that: (1) The spatial fragmentation of cultivated land are enhanced. In addition, cultivated land dominates the whole landscape of Luoyang city. (2) During the study period, most of the areas outside the municipal district in the northeast and some areas in Luanchuan County in the southwest have high kernel density, while the rest areas surrounding Luanchuan County are relatively sparse. The areas with high and very high kernel density of cultivated land showed the characteristics of expansion first and then contraction, while the areas with low kernel density expanded from 2014 to 2018. (3) The amount of cultivated land in Luoyang has a high spatial autocorrelation. In the local spatial autocorrelation, the “high-high” typology is concentrated and distributed in the border zone of the north and northeast of Luoyang city. “Low-low” typology is concentrated in Luanchuan county and Songxian County as well as the district of Luoyang City, which is heavily influenced by topography and urbanization. The expansion during the study period occurred in the jurisdiction area of Luoyang city, and most of it was transferred from “high-low” type area.
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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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Wei, H., Mengyu, W., Xueye, C. (2021). Analysis on Spatial Pattern Evolution of Cultivated Land in Urban Area Based on Spatial Autocorrelation Analysis – A Case Study of Luoyang City. In: Sun, X., Zhang, X., Xia, 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_36
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DOI: https://doi.org/10.1007/978-3-030-78609-0_36
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