Land Use Thematic Maps Recommendation Based on Pan-Map Visualization Dimension Theory

Yebin Chen, Zhicheng Shi, Yaxing Li, Dezhi Han, Minmin Li () and Zhigang Zhao
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Yebin Chen: Research Institute of Smart City, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Zhicheng Shi: School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, China
Yaxing Li: Research Institute of Smart City, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Dezhi Han: Research Institute of Smart City, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Minmin Li: Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518123, China
Zhigang Zhao: Research Institute of Smart City, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China

Land, 2024, vol. 13, issue 9, 1-16

Abstract: In the era of information and communication technology (ICT), the advancement of science and technology has led to a trend of diversification in map representation. However, the lack of professional knowledge means that there is still a challenge in determining the appropriate type of thematic map for land use expression. To address this issue, this paper proposes a knowledge recommendation method for land use thematic maps based on the theory of visualization dimensions. Firstly, we establish a knowledge ontology of land use thematic maps centered on spatial data, data characteristics, visualization dimensions, thematic map forms, and application scenarios. A land use thematic map knowledge graph is constructed through knowledge extraction and storage operations. Secondly, knowledge embedding is performed on the knowledge graph to enable the knowledge-based expression of map visualization elements. Finally, based on the knowledge elements of land use thematic expression, a similarity calculation model is established to calculate the similarity between input data and the spatial data characteristics, visualization dimensions, and application scenarios within the knowledge graph, deriving a comprehensive similarity result to achieve precise recommendation for land use thematic map forms. The results show that the method can provide a more accurate visualization reference for the selection of land use themes, meeting the diversified needs of land use thematic expression to a certain extent.

Keywords: land use thematic map; knowledge graph; knowledge representation; knowledge recommendation; similarity calculation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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