Visualizing clustering characteristics of multidimensional arable land quality indexes at the county level in mainland China

Sijing Ye, Changqing Song, Peichao Gao, Chenyu Liu and Changxiu Cheng
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Peichao Gao: State Key Laboratory of Earth Surface Processes and Resource Ecology, 47836Beijing Normal University, China
Chenyu Liu: Faculty of Geographical Science, 47836Beijing Normal University, China
Changxiu Cheng: State Key Laboratory of Earth Surface Processes and Resource Ecology, 47836Beijing Normal University, China;

Environment and Planning A, 2022, vol. 54, issue 2, 222-225

Abstract: The evaluation of the arable land ecosystem services capacity and arable land use intensity is important for recognizing regional key factors that impact the change of arable land attributes. A chronic lack of cooperation persists between these two fields of study, which makes providing sufficient information to support developing arable land use management and control policies difficult. In this study, the clustering characteristics of four arable land quality indexes have been assessed using the K-means algorithm to indicate the regional coordination between arable land resource protection and arable land use. The clustering results have been visualized using circular cartogram. This study can contribute to the identification of key regional challenges in China's arable land use and help to build the framework of other countries’ arable land protection policies.

Keywords: LUCC; arable land quality; circular cartogram; geovisualization; k-means (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:envira:v:54:y:2022:i:2:p:222-225

DOI: 10.1177/0308518X211062232

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