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dc.contributor.authorHolt, A
dc.contributor.authorMacDonell, SG
dc.contributor.authorBenwell, GL
dc.date.accessioned2012-03-10T09:31:26Z
dc.date.available2012-03-10T09:31:26Z
dc.date.copyright1998
dc.date.issued2012-03-10
dc.identifier.citationIn Proceedings of the Third International Conference on Geocomputation (GeoComp'98), Bristol, UK
dc.identifier.urihttp://hdl.handle.net/10292/3473
dc.description.abstractThis research continues with current innovative geocomputational research trends that aim to provide enhanced spatial analysis tools. The coupling of case-based reasoning (CBR) with GIS provides the focus of this paper. This coupling allows the retrieval, reuse, revision and retention of previous similar spatial cases. CBR is therefore used to develop more complex spatial data modelling methods (by using the CBR modules for improved spatial data manipulation) and provide enhanced exploratory geographical analysis tools (to find and assess certain patterns and relationships that may exist in spatial databases). This paper details the manner in which spatial similarity is assessed, for the purpose of re-using previous spatial cases. The authors consider similarity assessment a useful concept for retrieving and analysing spatial information as it may help researchers describe and explore a certain phenomena, its immediate environment and its relationships to other phenomena. This paper will address the following questions: What makes phenomena similar? What is the definition of similarity? What principles govern similarity? and How can similarity be measured? Generally, phenomena are similar when they share common attributes and circumstances. The degree of similarity depends on the type and number of commonalties they share. Within this research, similarity is examined from a spatial perspective. Spatial similarity is broadly defined by the authors as the spatial matching and ranking according to a specific context and scale. More specifically, similarity is governed by context (function, use, reason, goal, users frame-of mind), scale (coarse or fine level), repository (the application, local domain, site and data specifics), techniques (the available technology for searching, retrieving and recognising data) and measure and ranking systems. The degree of match is the score between a source and a target. In spatial matching a source and a target could be a pixel, region or coverage. The principles that govern spatial similarity are not just the attributes but also the relationships between two phenomena. This is one reason why CBR coupled with a GIS is fortuitous. A GIS is used symbiotically to extract spatial variables that can be used by CBR to determine similar spatial relations between phenomena. These spatial relations are used to assess the similarity between two phenomena (for example proximity and neighborhood analysis). Developing the concept of spatial similarity could assist with analysing spatial databases by developing techniques to match similar areas. This would help maximise the information that could be extracted from spatial databases. From an exploratory perspective, spatial similarity serves as an organising principle by which spatial phenomena are classified, relationships identified and generalisations made from previous bona fide experiences or knowledge. This paper will investigate the spatial similarity concept.
dc.publisherGeoComputation
dc.relation.urihttp://www.geocomputation.org/1998/17/gc_17.htm
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dc.titleAssessing the degree of spatial isomorphism for exploratory spatial analysis
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess


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