地球信息科学学报 ›› 2012, Vol. 14 ›› Issue (4): 426-431.doi: 10.3724/SP.J.1047.2012.00426

• 地球信息科学理论与方法 • 上一篇    下一篇

GIS的不同次区域之间相似性度量

廖伟华   

  1. 广西大学数学与信息科学学院, 南宁 530004
  • 收稿日期:2011-07-11 修回日期:2012-07-21 出版日期:2012-08-25 发布日期:2012-08-22
  • 作者简介:廖伟华(1975-),汉族,男,广西大学讲师,主要从事GIS不确定性研究。E-mail:gisliaowh@163.com
  • 基金资助:

    广西自然科学基金项目(2010GXNSFA013109);广西大学科研基金资助项目(XJZ110584)。

The Similarity Measurement for Different Subzones of GIS

LIAO Weihua   

  1. School of Mathematics and Information, Guangxi University, Nanning 530004, China
  • Received:2011-07-11 Revised:2012-07-21 Online:2012-08-25 Published:2012-08-22

摘要:

GIS属性相似度量有相似系数及以空间位置的空间自相关。这2种方法都不能计算全域为背景的次区域相似性问题,而以隶属函数的粗糙度量很好地解决了这个问题。本文利用粗糙集计算了GIS的离散型数据的次区域相似度量。利用邻域粗糙集计算了连续属性值的上下近似逼近,并利用邻域信息粒子计算连续属性值的粗糙隶属问题,从而计算连续属性值次区域相似度量问题。本文方法能度量次区域而不是全域或单个元素之间的空间相关与自相关问题,考虑了全区域背景下的GIS相似问题。并针对GIS连续属性值,利用距离函数和邻域粗糙集来划分连续属性的上下近似,以及分类问题,提出一种基于邻域信息粒子的粗糙隶属函数。最后利用粗糙相似度量公式度量GIS次区域的相似问题。

关键词: 相似度量, 邻域粗糙, 次区域, 粗糙集

Abstract:

There are two methods for GIS similarity measurement problems, one is cross-coefficient for GIS attribute similarity measurement, and the other is spatial autocorrelation that is based on spatial location. Both of these two methods can not measure subzone similarity of GIS subzone based on universal background. The rough measurement based on membership function solved this problem well. In this paper we used rough sets to calculate the GIS subzone discrete data similarity measurement, and used neighborhood rough sets to calculate continuous data's upper and lower approximation. We used neighborhood particle to calculate continuous attribute's rough membership function, then to calculate continuous attribute's subzone similarity measurement problem. This paper used rough membership to measure similarity problem for different subzones. Because Moran's I can only measure universe or each unit's spatial autocorrelation, it can not measure subzone, so our method in this paper can compute GIS subzone similarity based on universe. And for continuous value, we used distance function and neighborhood rough sets to divide continuous value's upper and lower approximation and classification problem, then we put forward a rough membership function based on neighborhood information granulation. Then, we used rough similarity measurement formula to measure GIS subzone similarity problem. This method can provide a new direction for GIS point group or others' object group similarity measurement. At last, using an example that includes discrete and continuous value, we can find that spatial autocorrelation can not measure discrete value, cross-coefficient can not measure discrete value too. If the subzone in map is not equal length for continuous value, cross-coefficient can not measure similarity, but the rough measurement based on membership function solved this problem well.

Key words: neighborhood rough sets, subzone, rough sets, similarity measurement