Spatial data methods and vague regions: A rough set approach

Beaubouef, Theresa; Petry, Frederick E.; Ladner, Roy

doi:10.1016/j.asoc.2004.11.003

Cited by 39 publications

(13 citation statements)

References 30 publications

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“…Since RST is useful in analyzing data with insufficient and incomplete knowledge it has been applied in many traditional domains including finance, medicine, telecommunications, vibration analysis, control theory, signal analysis, pattern recognition, and image analysis (Yasdi, 1996;Polkowski and Skowron, 1998;Polkowski et al, 2000;Skowron, 2001;Leung and Li, 2003). The literature has also revealed that RST has been applied to problems of spatial analysis (Bittner, 2001;Bittner and Stell, 2001); spatial classification and uncertainty analysis (Ahlqvist et al, 2000(Ahlqvist et al, , 2003, geo-knowledge discovery (Wang et al, 2001;Beaubouef et al, 2007), remote sensing image classification (Dong et al, 2007), and in the extraction of decision rules in GIS and remote sensing (Berger, 2004;Leung et al, 2007;Bai et al, 2009). Moreover, Cao et al (2009) initially attempted to extract the spatial relationship indicator rules based on RST.…”

Section: Brief Remarks On Rough Set Theory and Its Applicationsmentioning

confidence: 95%

Application of rough set-based analysis to extract spatial relationship indicator rules: An example of land use in Pearl River Delta

Cao

et al. 2011

J. Geogr. Sci.

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Spatial relations, reflecting the complex association between geographical phenomena and environments, are very important in the solution of geographical issues. Different spatial relations can be expressed by indicators which are useful for the analysis of geographical issues. Urbanization, an important geographical issue, is considered in this paper. The spatial relationship indicators concerning urbanization are expressed with a decision table. Thereafter, the spatial relationship indicator rules are extracted based on the application of rough set theory. The extraction process of spatial relationship indicator rules is illustrated with data from the urban and rural areas of Shenzhen and Hong Kong, located in the Pearl River Delta. Land use vector data of 1995 and 2000 are used. The extracted spatial relationship indicator rules of 1995 are used to identify the urban and rural areas in Zhongshan, Zhuhai and Macao. The identification accuracy is approximately 96.3%. Similar procedures are used to extract the spatial relationship indicator rules of 2000 for the urban and rural areas in Zhongshan, Zhuhai and Macao. An identification accuracy of about 83.6% is obtained.

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Section: Brief Remarks On Rough Set Theory and Its Applicationsmentioning

confidence: 95%

Application of rough set-based analysis to extract spatial relationship indicator rules: An example of land use in Pearl River Delta

Cao

et al. 2011

J. Geogr. Sci.

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“…According to [5], rough sets, 9-intersection modeling, RCC theory and egg-yolk approaches are useful for managing the types of uncertainty related to topology. These include concepts, such as nearness, contiguity, connection, orientation, inclusion and overlap of spatial entities.…”

Section: Topological Uncertainty In Spatial Datamentioning

confidence: 99%

Uncertainty in spatial data mining

2007

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Spatial data mining, i.e., mining knowledge from large amounts of spatial data, is a demanding field since huge amounts of spatial data have been collected in various applications. The collected data far exceeds people's ability to analyze it. Thus, new and efficient methods are needed to discover knowledge from large spatial databases. Most of the spatial data mining methods do not take into account the uncertainty of spatial information. In our work we use objects with broad boundaries, the concept that absorbs all the uncertainty by which spatial data is commonly affected and allows computations in the presence of uncertainty without rough simplifications of the reality. And we propose an uncertainty model that enables efficient analysis of such data. The study case of suitable flounder fishery search indicates the benefit of uncertainty research in spatial data mining.

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“…The consistency of topological relations between CBBRs in multi-resolution spatial databases can be evaluated from two points of view: (1) if the relation matrix at a small scale is a subset of the one at a large scale for the same pair of CBBRs, then they are consistent; (2) if some relations in the relation matrix at a small scale are included in the one at a large scale, while others are not, then it is necessary to evaluate whether the latter set of relations can be derived from the matrix at the large scale. If the answer is yes, then they are consistent; otherwise, not.…”

Section: Hybrid Approach Of Merging and Dropping Operatorsmentioning

confidence: 99%

“…As the crisp objects have multiple representations in an integrated database, the BBRs also have different representations at different scales. Recent work about uncertain regions was to model vague regions with fuzzy sets and rough sets [2,9], and to formalize fuzzy topological relations [3,11,12,30]. Complex regions with broad boundaries (CBBRs), which are composed of a set of regions, have more complex structures than crisp objects.…”

Section: Introductionmentioning

confidence: 99%