Interface Design of GIS System Based on Visual Complexity

  • Siyi Wang
  • Chengqi XueEmail author
  • Jing Zhang
  • Junkai Shao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 972)


This paper will take the GIS system as a typical interface, and analyze the main design elements such as information structure, interface layout and element com-position. Based on the combination coding characteristics of cognitive complexity, a visual representation method is established. Through the preliminary mapping of visual complexity factors and physiological indicators, the mapping relation-ship between digital interface visual information and cognitive brain mechanism of information weapon system is proposed. Finally, the design strategy of GIS interface optimization complexity is proposed, which provides innovative ideas for the study of interface visual complexity.


Interface design Visual complexity GIS system 



This paper is supported by the National Natural Science Foundation of China (No. 71871056, 71471037).


  1. 1.
    Berlyne, D.E.: Complexity and incongruity variables as determinants of exploratory choice and evaluative ratings. Can. J. Psychol. 17, 274–290 (1963)CrossRefGoogle Scholar
  2. 2.
    Geissler, G.L., Watson, Z.R.T.: The influence of home page complexity on consumer attention, attitudes, and purchase intent. J. Advert. 35, 69–80 (2006)CrossRefGoogle Scholar
  3. 3.
    Machado, P., Romero, J., Nadal, M., Santos, A., Correia, J., Carballal, A.: Computerized measures of visual complexity. Acta Physiol. 160, 43–57 (2015)Google Scholar
  4. 4.
    Oliva, A., Mack, M.L., Shrestha, M.: Identifying the perceptual dimensions of visual complexity of scenes. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 3, no. 49, pp. 1041–1046 (2004)Google Scholar
  5. 5.
    Stickel, C., Ebner, M., Holzinger, A.: The XAOS metric – understanding visual complexity as measure of usability. In: Leitner, G., Hitz, M., Holzinger, A. (eds.) HCI in Work and Learning, Life and Leisure, USAB 2010. Lecture Notes in Computer Science, vol. 6389, pp. 278–290. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Huo, J.: Image complexity and visual working memory capacity. In: Chen, L., Kapoor, S., Bhatia, R. (eds.) Emerging Trends and Advanced Technologies for Computational Intelligence. Studies in Computational Intelligence, vol. 647, pp. 301–314. Springer, Cham (2016)CrossRefGoogle Scholar
  7. 7.
    Corchs, S.E., Ciocca, G., Bricolo, E., Gasparini, F.: Predicting complexity perception of real world images. PLoS ONE 11(6), 1–22 (2016)CrossRefGoogle Scholar
  8. 8.
    Chen, Y.Q., Duan, J., Zhu, Y., Qian, X.F., Xiao, B.: Research on the image complexity based on neural network. In: International Conference on Machine Learning and Cybernetics, pp. 295–300. IEEE, Guangzhou (2015)Google Scholar
  9. 9.
    Silva, M.P.D., Courboulay, V., Estraillier, P.: Image complexity measure based on visual attention. In: Proceedings-International Conference on Image Processing, pp. 3281–3284. IEEE, Belgium (2011)Google Scholar
  10. 10.
    Bonev, B., Chuang, L.L., Escolano, F.: How do image complexity, task demands and looking biases influence human gaze behavior? Pattern Recogn. Lett. 34(7), 723–730 (2013)CrossRefGoogle Scholar
  11. 11.
    Tseng, K.T., Tseng, Y.C.: The correlation between visual complexity and user trust in on-line shopping: implications for design. In: Kurosu, M. (eds.) Human-Computer Interaction. Applications and Services, HCI 2014. Lecture Notes in Computer Science, vol. 8512. Springer, Cham (2014)CrossRefGoogle Scholar
  12. 12.
    Wang, Q., Yang, S., Cao, Z., Liu, M., Ma, Q.: An eye-tracking study of website complexity from cognitive load perspective. Decis. Support Syst. 62(1246), 1–10 (2014)Google Scholar
  13. 13.
    Rigau, J., Feixas, M., Sbert, M.: An information-theoretic framework for image complexity. In: Neumann, L., Sbert, M., Gooch, B., Purgathofer, W. (eds.) Computational Aesthetics in Graphics, Visualization and Imaging, pp. 177–184. Wiley-Blackwell, Hoboken (2006)Google Scholar
  14. 14.
    Mario, I., Chacon, M., Alma, D., Corral, S.: Image complexity measure: a human criterion free approach. In: Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, pp. 241–246. IEEE (2005)Google Scholar
  15. 15.
    Peters, R.A.I.: Image complexity metrics for automatic target recognizers. In: Proceedings of the Automatic Target Recognizer System and Technology Conference, pp. 1–17. Citeseer (1990)Google Scholar
  16. 16.
    Rusu, A., Govindaraju, V.: The influence of image complexity on handwriting recognition. In: Proceedings of the Tenth International Workshop on Frontiers in Handwriting Recognition (IWFHR 2006), La Baule, France (2006)Google Scholar
  17. 17.
    Li, M., Bai, M.: A mixed edge based text detection method by applying image complexity analysis. In: Proceedings of the 10th World Congress on Intelligent Control and Automation, pp. 4809–4814. IEEE Press, Beijing (2012)Google Scholar
  18. 18.
    Liu, Q., Sung, A.H., Ribeiro, B., Wei, M., Chen, Z., Xu, J.: Image complexity and feature mining for steganalysis of least significant bit matching steganography. Inf. Sci. 178(1), 21–36 (2008)CrossRefGoogle Scholar
  19. 19.
    Carvajal-Gamez, B.E., Gallegos-Funes, F.J., Rosales-Silva, A.J.: Color local complexity estimation based steganographic (CLCES) method. Expert Syst. Appl. 40(4), 1132–1142 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Siyi Wang
    • 1
  • Chengqi Xue
    • 1
    Email author
  • Jing Zhang
    • 1
  • Junkai Shao
    • 1
  1. 1.School of Mechanical EngineeringSoutheast UniversityNanjingChina

Personalised recommendations