Comparative Study of Inverse Power of IDW Interpolation Method in Inherent Error Analysis of Aspect Variable

  • Neeraj Bhargava
  • Ritu Bhargava
  • Prakash Singh Tanwar
  • Prafull Chandra Narooka
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)


This paper deals with inherent error analysis of aspect variable using IDW interpolation method and its various power values. In the first section, it shows aspect analysis method and algorithm. In the second section, it creates a DEM model from various measured and erroneous elevated points which becomes input to calculate aspect of interpolated DEMs separately then error is calculated by calculating difference of the aspect for true and erroneous aspect. It explores error analysis of aspect with its practical implementation in ArcGIS. In the last section, it explains the comparison of errors on aspect for various power of IDW method. Result shows that aspect error decreases with the increment in the inverse power of distance in IDW method.


3D GIS Aspect Error analysis IDW Interpolation 


  1. 1.
    A. A. Rahman, M. Pilouk, and S. Zlatanova, “The 3D GIS software development: global efforts from researchers and vendors,” Geoinformation Science Journal, vol. 1, no. 13, 2001.Google Scholar
  2. 2.
    E. R. Vivoni, V. Y. Ivanov, R. L. Bras, and D. Entekhabi, “Generation of triangulated irregular networks based on hydrological similarity,” Journal of hydrologic engineering, vol. 9, no. 4, pp. 288–302, 2004.Google Scholar
  3. 3.
    Neeraj Bhargava, Ritu Bhargava, and Prakash Singh Tanwar, “Triangulated Irregular Network Model from Mass Points,” International Journal of Advanced Computer Research, vol. 3 No. 2, no. 10, pp. 172–176, June 2013.Google Scholar
  4. 4.
    George Y. Lu and David W. Wong, “An adaptive inverse distance weighting spatial interpolation technique,” vol. 34, no. 9, pp. 1044–1055, September 2008.Google Scholar
  5. 5.
    Q. Zhou and X.Liu, “Error Analysis on Grid-Based Slope and Aspect Algorithms,” Photogrammetric Engineering & Remote Sensing, vol. 70, no. 8, pp. 957–962, 2004.Google Scholar
  6. 6.
    R. Hickey, “Slope angle and slope length solutions for GIS,” Cartography, vol. 29, no. 1, pp. 1–8, 2000.Google Scholar
  7. 7.
    E. M. Masaad and S. M. Moneim, “Suitable Design of Road Pattern for Kosti Town Based on TIN Analysis,” Khartoum University Engineering Journal, vol. 2, no. 1, 2012.Google Scholar
  8. 8.
    M. A. Azpurua and K. D.Ramos, “A comparison of spatial interpolation methods for estimation of average electromagnetic field magnitude,” Progress In Electromagnetics Research M, vol. 14, pp. 135–145, 2010.Google Scholar
  9. 9.
    X. Hanjianga, T. Limina, and S. Longa, “A Strategy To Build A Seamless Multi-Scale TIN-DEM Database,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVII., no. B4, 2008.Google Scholar
  10. 10.
    G. E. Tucker, S. T. Lancaster, N. M. Gasparini, R. L. Bras, and S. M. Rybarczyk, “An object-oriented framework for distributed hydrologic and geomorphic modeling using triangulated irregular networks,” Computers & Geosciences, vol. 27, no. 8, pp. 959–973, 2001.Google Scholar
  11. 11.
    R. Pajarola, M. Antonijuan, and R. Lario, “Quadtin: Quadtree based triangulated irregular networks,” Proceedings of the conference on Visualization’02, pp. 395–402, 2002.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Neeraj Bhargava
    • 1
  • Ritu Bhargava
    • 2
  • Prakash Singh Tanwar
    • 3
  • Prafull Chandra Narooka
    • 3
  1. 1.Department of Computer Science, School of Engineering & System SciencesM.D.S. University AjmerAjmerIndia
  2. 2.Department of Computer ScienceAryabhatt International CollegeAjmerIndia
  3. 3.Department of Computer ScienceMJRP UniversityJaipurIndia

Personalised recommendations