Intelligent initial map scale generation based on rough-set rules

  • Chaode YanEmail author
  • Likun Yang
  • Georg Gartner
  • Qiang Zhu
  • Xiao Liu
Original Paper


A proper initial map scale can help improve map legibility. However, the existing initial scale designs for electronic maps cannot make active adjustments according to the differences in the surrounding geographic information distributions, during map panning or navigation. This causes many redundant zooming operations, which reduce the reading efficiency. To solve this problem, we propose a method based on the rough set, which chooses an initial map scale according to the spatial distribution of the road network. First, the spatial distribution of the road network is evaluated using the neighborhood relation model, with Delaunay triangulations. Next, the data of the road network’s spatial distributions and the corresponding map scale data from user operations are collected at different locations. Then, the relationship rules are extracted based on rough set. Finally, an intelligent initial map scale service is developed according to the rules, and its feasibility and effectiveness are tested using an experimental system. The test results show that the intelligent initial map method can adjust the map scale adaptively and dynamically according to distribution of the road network. Consequently, the map legibility is improved significantly because of the reduction in the number of zooming operations.


Map Initial scale Rough set Spatial distribution Road network 


Author contributions

CY, LY, and GG conceived the idea presented in this paper. CY and LY designed the experiments and performed the modeling. QZ developed the experimental system. LY and XL performed the tests. CY and LY wrote the paper.

Funding information

This work is supported by the National Natural Science Foundation of China (No. 41671455, No. 40971238) and the key research projects of the Henan Provincial Education Department (15A420007, 16A420005).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  • Chaode Yan
    • 1
    Email author
  • Likun Yang
    • 2
    • 1
  • Georg Gartner
    • 3
  • Qiang Zhu
    • 1
  • Xiao Liu
    • 1
  1. 1.School of Water Conservancy and EnvironmentZhengzhou UniversityZhengzhouChina
  2. 2.Department of Surveying and MappingZhengzhou Trade and Industry SchoolsZhengzhouChina
  3. 3.Research Group Cartography, Department of Geodesy and GeoinformationTechnical University ViennaViennaAustria

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