Author(s)

Weihua Liao

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There are two methods for GIS similarity measurement problem, one is cross-coefficient for GIS attribute similarity measurement, and the other is spatial autocorrelation that is based on spatial location. These methods can not calculate subzone similarity problem based on universal background. The rough measurement based on membership function solved this problem well. In this paper, we used rough sets to measure the similarity of GIS subzone discrete data, and used neighborhood rough sets to calculate continuous data’s upper and lower approximation. We used neighborhood particle to calculate membership function of continuous attribute, then to solve continuous attribute’s subzone similarity measurement problem.

Subzone; Rough Sets; Neighborhood Rough Sets; Similarity Measurement

W. Liao, "The Rough Method for Spatial Data Subzone Similarity Measurement," Journal of Geographic Information System, Vol. 4 No. 1, 2012, pp. 37-45. doi: 10.4236/jgis.2012.41006.