ARTICLES

The Similarity Measurement for Different Subzones of GIS

Expand
  • School of Mathematics and Information, Guangxi University, Nanning 530004, China

Received date: 2011-07-11

  Revised date: 2012-07-21

  Online published: 2012-08-22

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.

Cite this article

LIAO Weihua . The Similarity Measurement for Different Subzones of GIS[J]. Journal of Geo-information Science, 2012 , 14(4) : 426 -431 . DOI: 10.3724/SP.J.1047.2012.00426

References

[1] 李同升,王霞. 陕西省非农人口分布的空间自相关特征分析[J]. 西北大学学报(自然科学版),2007,37(6):935-939.

[2] 宋琳,董春,胡晶,等.基于空间统计分析与GIS的人均GDP空间分布模式研究[J].测绘科学,2006,31(4):13-125.

[3] 王劲峰,李连发,葛咏,等. 地理信息空间分析的理论体系探讨[J].地理学报,2000,55(1):922-1003.

[4] 杨凤海,郭欣欣,高凤杰,等. 基于DEM聚焦分析的旬平均气温与地面高程的相关性定量研究[J].地理与地理信息科学,2009,25(6):37-40.

[5] 连健,李小娟,宫辉力. GIS支持下的空间分层抽样方法研究——以北京市人均农业总产值抽样调查为例[J]. 地理与地理信息科学,2008,24(6):30-34.

[6] 程涛,邓敏,李志林. 空间目标不确定性的表达方法及其在GIS中的应用分析[J].武汉大学学报·信息科学版,2007,32(5):389-393.

[7] 柴思跃,苏奋振,周成虎. 基于周期表的时空关联规则挖掘方法与实验[J].地球信息科学学报,2011,13(4):455-464.

[8] 廖伟华.变精度粗糙集下的GIS面目标拓扑关系扩展研究[J]. 地球信息科学学报,2010,12(6):806-810.

[9] 王耀革,王志伟,朱长青. DEM误差的空间自相关特征分析[J]. 武汉大学学报.信息科学版,2008,33(12):1259-1262.

[10] Anselin L. Local indicators of spatial association LISA[J]. Geographical Analysis, 1995,27:93-115.

[11] 张松林,张昆. 全局空间自相关 Moran指数和G系数对比研究[J].中山大学学报(自然科学版),2007,46(4):93-97.

[12] 刘富春. 模糊粗糙集的相似度量和相似性方向[J]. 计算机工程与应用,2005,35:63-66.

[13] 王洪凯,管延勇,史开泉. 粗集间的相似度量及其应用[J].计算机工程与应用,2004,31:39-40.

[14] 胡清华,于达仁,谢宗霞.基于邻域粒化和粗糙逼近的数值属性约简[J].软件学报,2008,19(3):640-649.

[15] 史战红,连玉平. 基于包含度的粗糙集间的相似性度量[J].数学教学研究,2008,2:53-54.

Outlines

/