Classification of surface roughness using spatial autocorrelation in the economical wind resistant design of buildings

  • Eun-Su Seo
  • Se-Hyu ChoiEmail author
Part of the following topical collections:
  1. Academia and Industry collaboration on the Spatial Information
  2. Academia and Industry collaboration on the Spatial Information


The crowding of housing facilities and industrial facilities due to the development of cities is greatly influencing geographic feature changes within urban areas. The construction of various social infrastructure leads to high-rise buildings and the crowding of nearby buildings, while there also arises a mixture with already existing low-rise buildings such as detached houses or industrial complex. This results in the confusion of designer in calculating the velocity pressure exposure coefficient which is an important factor in the wind resistant design of buildings. Therefore, this study utilized construction data of 1:5000 digital maps to analyze the velocity pressure exposure coefficient. Analysis was carried out by utilizing the Moran-I and Getis-Ord’s Gi* between nearby buildings where newly designed buildings are situated, and by suggesting a method that classifies surface roughness according to this standard the actual information of geographic feature was applied in the calculation process of velocity pressure exposure coefficient. Depending on the environment, it was possible to confirm that the results of the illumination of the indicators differed differently depending on the surroundings of the building. Also by suggesting a measure that calculates the surface roughness by using GIS, the existing problem of which the designer estimates surface roughness according to his own subjective judgement is solved. This is expected to help to achieve a more reasonable and economical wind resistant design of buildings.


Surface roughness Velocity pressure exposure coefficient Spatial autocorrelation Getis-Ord’s Gi* Design wind velocity 



This research was supported by a grant [MOIS-DP-2015-05] through the Disaster and Safety Management Institute funded by Ministry of the Interior and Safety of Korean government.


  1. 1.
    Kang, I. H. (2009). High-rise house. KHousing, 4(2), 91–114.Google Scholar
  2. 2.
    Kim, H. G., & Choi, J. W. (2002). Calculation of wind profile exponent in Pohang area. Journal of the Wind Engineering Institute of Korea, 6(1), 47–52.Google Scholar
  3. 3.
    Architectural Institute of Korea. (2009). Korean building code-structural. Kimoondang.Google Scholar
  4. 4.
    Choi, S. H., & Sung, M. H. (2011). Estimation of velocity pressure exposure coefficient using GIS. Journal of Korea Spatial Information Society, 19(1), 13–19.Google Scholar
  5. 5.
    Anselin, L. (1995). Local indicators of spatial association–LISA. Geographical Analysis, 27(2), 93–115.CrossRefGoogle Scholar
  6. 6.
    Kim, G. G. (2003). Detecting spatial autocorrelation and using spatial regression. International Journal of Policy Evaluation and Management, 13(1), 273–306.Google Scholar
  7. 7.
    Lee, J., & Wong, D. W. S. (2001). Statistical analysis with ArcView GIS. Hoboken: Wiley.Google Scholar
  8. 8.
    Zhang, Z., & Griffitth, D. A. (1997). Developint user-friendly spatial statistical analysis modules for GIS: An example using ArcView. Computation, Envrionment, and Urban System, 21(1), 5–29.CrossRefGoogle Scholar
  9. 9.
    Lee, S. I., Cho, D. H., Son, H. K., & Chae, M. O. (2010). A GIS-based method for delineating spatial clusters: A modified AMOEBA technique. Journal of the Korean Geographical Society, 45(4), 502–520.Google Scholar
  10. 10.
    Won, J.-Y., Shin, J.-D., & Lee, J.-S. (2017). Correlation analysis between the occurrence of safety accidents and land cover ratio: focused on 119 emergency activity data for Ulsan Metropolitan City in South Korea. Spatial Information Research, 25(4), 535–546.CrossRefGoogle Scholar
  11. 11.
    Mohamad, M. Q., Beniamino, M., Mohsen, Y. F., & Moslem, Z. (2017). Urbanization patterns in Iran visualized through spatial auto-correlation analysis. Spatial Information Research, 25(5), 627–633.CrossRefGoogle Scholar
  12. 12.
    Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.CrossRefGoogle Scholar
  13. 13.
    Getis, A., & Ord, J. K. (1996). Local spatial statistics: An overview in spatial analysis: Modling in a GIS environment (pp. 261–277). Cambridge: Geoinformation International.Google Scholar

Copyright information

© Korean Spatial Information Society 2019

Authors and Affiliations

  1. 1.School of Architecture, Civil, Environmental and Energy EngineeringKyungpook National UniversityDaeguSouth Korea
  2. 2.School of Architecture and Civil EngineeringKyungpook National UniversityDaeguSouth Korea

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