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

Author

Listed:
  • 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
    Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China)

  • Zhigang Zhao

    (Research Institute of Smart City, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China)

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.

Suggested Citation

  • Yebin Chen & Zhicheng Shi & Yaxing Li & Dezhi Han & Minmin Li & Zhigang Zhao, 2024. "Land Use Thematic Maps Recommendation Based on Pan-Map Visualization Dimension Theory," Land, MDPI, vol. 13(9), pages 1-16, August.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:9:p:1389-:d:1466573
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    References listed on IDEAS

    as
    1. Qiuhao Huang & Yongxue Liu & Manchun Li & Kun Mao & Feixue Li & Zhenjie Chen & Chong Chen & Wei Hu, 2012. "Thematic maps for county-level land use planning in Contemporary China," Journal of Maps, Taylor & Francis Journals, vol. 8(2), pages 185-188.
    2. Xuesong Kong & Yaolin Liu & Xingjian Liu & Yiyun Chen & Dianfeng Liu, 2014. "Thematic maps for land consolidation planning in Hubei Province, China," Journal of Maps, Taylor & Francis Journals, vol. 10(1), pages 26-34, January.
    3. Jie Zhu & Mengyao Zhu & Jiaming Na & Ziqi Lang & Yi Lu & Jing Yang, 2023. "Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes," Land, MDPI, vol. 12(10), pages 1-22, October.
    Full references (including those not matched with items on IDEAS)

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