Share This Article:

Effectiveness of Fuzzy Overlay Function for Multi-Criteria Spatial Modeling—A Case Study on Preparation of Land Resources Map for Mawsynram Block of East Khasi Hills District of Meghalaya, India

Abstract Full-Text HTML XML Download Download as PDF (Size:4773KB) PP. 605-612
DOI: 10.4236/jgis.2014.66050    3,511 Downloads   3,978 Views   Citations

ABSTRACT

Multi-criteria spatial modeling is one of the important components of spatial decision support system (SDSS). Multi-criteria spatial modeling often requires a common scale of values for diverse and dissimilar inputs to create an integrated analysis. Weighted overlay function is most commonly used for site suitability analysis which identifies the most preferred locations for a specific phenomenon. However, weighted overlay function gives inconsistent and erroneous results for highly dissimilar inputs as it assumes that most favorable factors result in the higher values of raster, while identifying the best sites. This paper conveys the effectiveness of fuzzy overlay function for multi-criteria spatial modeling. It is based on the principle of fuzzy logic theory which defines membership using Gaussian function on each of the input rasters instead of giving individual rank to them like in weighted overlay function. A case study on preparation of land resources map for Mawsynram block of East Khasi Hills district of Meghalaya, India is presented here. It was observed that fuzzy overlay function has given more satisfactory output in terms of site suitability while comparing with the result of weighted overlay function.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Baidya, P. , Chutia, D. , Sudhakar, S. , Goswami, C. , Goswami, J. , Saikhom, V. , Singh, P. and Sarma, K. (2014) Effectiveness of Fuzzy Overlay Function for Multi-Criteria Spatial Modeling—A Case Study on Preparation of Land Resources Map for Mawsynram Block of East Khasi Hills District of Meghalaya, India. Journal of Geographic Information System, 6, 605-612. doi: 10.4236/jgis.2014.66050.

References

[1] Upton, G.J. and Fingelton, B. (1985) Spatial Data Analysis by Example Volume 1: Point Pattern and Quantitative Data. Biometrical Journal, 28, 664.
[2] Knox, P.L. (1980) Measure of Accessibility as Social Indicators: A Note. Social Indicators Research, 7, 367-377. http://dx.doi.org/10.1007/BF00305607
[3] Sénécal, G. (2002) Urban Spaces and Quality of Life: Moving beyond Normative Approaches. Policy Research Initiative, 5, 306-318.
[4] Goswami, J., Chutia, D. and Sudhakar, S. (2012) A Geospatial Approach to Climatic Zone Specific Effective Horticultural Planning in East Khasi Hills District of Meghalaya, India. Journal of Geographic Information System, 4, 267-272. http://dx.doi.org/10.4236/jgis.2012.43032
[5] Chutia, D. (2010) Application of Geospatial Technologies for Development of an Efficient Election Management System in Meghalaya. Journal of Geomatics, 5, 115.
[6] Oledzki, J.R. (2004) Geoinformatics: An Integrated Spatial Tool, Miscellanea Geographica Warszawa. Miscellanea Geographica—Regional Studies on Development, 11, 323-331.
http://www.wgsr.uw.edu.pl/pub/uploads/mcg04/35oledzki.pdf
[7] Neog, A.K. (2006) WTO and Agriculture Development in Backward Regions. In: Deb, B.J. and Ray, B.D., Eds., Changing Agriculture Scenario in North East India, Concept Publishing Company, New Delhi, 25-42.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.