An Intelligent Cartographic Generalization Algorithm Selecting Mode Used in Multi-scale Spatial Data Updating Process

  • Junkui XuEmail author
  • Dong Li
  • Longfei Cui
  • Xing Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 849)


In multi-scale spatial data updating process, cartographic features vary dramatically with the scales evolution. So, it is the critical step to select suitable cartographic generalization algorithm which can perfectly fulfill the scale-transformation task. This problem is also a main obstacle in the way of automatic spatial data updating. Through deeply studying the flows of multi-scale spatial data updating process, an intelligent cartographic generalization algorithm selecting mode is proposed. Firstly cartographic generalization algorithm base, knowledge base and case base is built in this mode. Secondly, based on the step of resolving the cartographic generalization process into segments, a self-adaption cartographic generalization algorithm selecting architecture is constructed. Thirdly, an intelligent cartographic generalization algorithm selecting and using flow is established and put into effect. Overall, this mode provides a new idea to solve the automatic problem of multi-scale spatial data updating.


Spatial data Updating Cartographic generalization Intelligent selecting Multi-scale map 


  1. 1.
    Deng, H., Wu, F., Qian, H., et al.: A model of point cluster selection based on genetic algorithms. J. Image Graph. 8, 970–974 (2003)Google Scholar
  2. 2.
    Qian, Q., Wu, F., Deng, H.: A point cluster selection algorithm based on CIRCLE character transformation techniques. Sci. Surv. Mapp. 30(3), 83–85 (2005)Google Scholar
  3. 3.
    Cai, Y., Guo, Q.: Points group generalization based on konhonen net. Geomatics Inf. Sci. Wuhan Univ. 32(7), 626–629 (2007)Google Scholar
  4. 4.
    Ai, T., Liu, Y.: A method of point cluster simplification with spatial distribution properties preserved. Acta Geodaetica Cartogr. Sin. 31(2), 175–181 (2002)MathSciNetGoogle Scholar
  5. 5.
    Yan, H., Wang, J.: A generic algorithm for point cluster generalization based on Voronoi diagrams. J. Image Graph. 10(5), 633–636 (2005)MathSciNetGoogle Scholar
  6. 6.
    Qian, H.: Study on Automated Cartographic Generalization and Intelligentized Generalization Process Control. Zhengzhou Institute of Surveying and Mapping, Zhengzhou (2006)Google Scholar
  7. 7.
    Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th edn. Pearson Education Inc. (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Junkui Xu
    • 1
    • 2
    Email author
  • Dong Li
    • 1
  • Longfei Cui
    • 1
    • 2
  • Xing Zhang
    • 1
    • 2
  1. 1.Luoyang Electronic Equipment Test Center of ChinaLuoyangChina
  2. 2.Zhengzhou Institute of Surveying and MappingZhengzhouChina

Personalised recommendations