Principal Component Analysis of Building Cluster Factors

  • Hua Ai
  • Qiang LiuEmail author
  • Zhen Wang
  • Zezhong Zheng
  • Yaosen Huang
  • Zhiqin Huang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 699)


Building properties on a map can be represented by multiple building characterization factors. In this paper, using principal component analysis method, we analyzed multiple factors characterizing buildings. Also, through dimensionality reduction transformation into a small amount of comprehensive factors, this paper proposed simplified expression of building properties, to better meet the need of map generalization for buildings.


Principal component analysis Map generalization Building cluster factor 



This work was partially supported by Science Research Program of Land and Resources Department of Sichuan Province (No. KJ201613 and No. KJ20159), and The Project Supported by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (No. KF-2016-02-007).


  1. 1.
    Qianhu, S.: Research on Clustering Method of Building Cluster Based on Multiple Constraints. Central South University, Changsha (2011)Google Scholar
  2. 2.
    Tinghua, A., Xiang, Z.: The aggregation of urban building clusters based on the skeleton partitioning of gap space. In: Fabrikant, S.I., Wachowicz, M. (eds.) The European Information Society. Lecture Notes in Geoinformation and Cartography, pp. 153–170. Springer, Heidelberg (2007)Google Scholar
  3. 3.
    Boyan, C., Qiang, L., Xiaowen, L.: Intelligent building grouping using a self-organizing map. J. Acta Geod. et Cartograph. Sin. 42(2), 290–294 (2013)Google Scholar
  4. 4.
    Liangjian, W., Wei, L.: Study on driving force of land use change in Wuzhou city. J. Econ. Geogr. 19(4), 74–79 (1999)Google Scholar
  5. 5.
    Jing, Y., Zhengong, T., Yuzhe, L.: The application of SPSS software to analysis and evaluation of the principal components of drinking water quality. J. Environ. Sci. Technol. 07, 171–174 (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Hua Ai
    • 1
  • Qiang Liu
    • 2
    Email author
  • Zhen Wang
    • 2
  • Zezhong Zheng
    • 2
  • Yaosen Huang
    • 2
  • Zhiqin Huang
    • 3
  1. 1.Neijiang Normal UniversityNeijiangPeople’s Republic of China
  2. 2.School of Resources and EnvironmentUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China
  3. 3.Department of Land and Resources of Sichuan ProvinceChengduPeople’s Republic of China

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