TY - JOUR
T1 - Rough spatial interpretation
AU - Wang, Shuliang
AU - Yuan, Manning
AU - Chen, Guoqing
AU - Li, Deren
AU - Shi, Wenzhong
PY - 2004
Y1 - 2004
N2 - Rough set is a new approach to uncertainties in spatial analysis. In this paper, we complete three works under the umbrella of rough space. First, a set of simplified rough symbols is extended on the basis of existing rough symbols. It is in terms of rough interpretation and specialized indication. Second, rough spatial entity is proposed to study the real world as it is, without forcing uncertainties to change into a crisp set. Third, rough spatial topological relationships are studied by using rough matrix and their figures. The relationships are divided into three types, crisp entity and crisp entity (CC), rough entity and crisp entity (RC) and rough entity and rough entity (RR). A universal intersected equation is further developed. Finally, rough membership function is further extended with the gray scale in our case study. And the maximum and minimum maps of river thematic classification are generated via the rough membership function and rough relationships.
AB - Rough set is a new approach to uncertainties in spatial analysis. In this paper, we complete three works under the umbrella of rough space. First, a set of simplified rough symbols is extended on the basis of existing rough symbols. It is in terms of rough interpretation and specialized indication. Second, rough spatial entity is proposed to study the real world as it is, without forcing uncertainties to change into a crisp set. Third, rough spatial topological relationships are studied by using rough matrix and their figures. The relationships are divided into three types, crisp entity and crisp entity (CC), rough entity and crisp entity (RC) and rough entity and rough entity (RR). A universal intersected equation is further developed. Finally, rough membership function is further extended with the gray scale in our case study. And the maximum and minimum maps of river thematic classification are generated via the rough membership function and rough relationships.
UR - http://www.scopus.com/inward/record.url?scp=9444245312&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-25929-9_52
DO - 10.1007/978-3-540-25929-9_52
M3 - Conference article
AN - SCOPUS:9444245312
SN - 0302-9743
VL - 3066
SP - 435
EP - 444
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 4th International Conference, RSCTC 2004
Y2 - 1 June 2004 through 5 June 2004
ER -