Progress in computational methods for representing geographical concepts

Egenhofer, Max J.; Glasgow, Janice I.; Günther, Oliver; Herring, John R.; Peuquet, Donna J.

doi:10.1080/136588199241012

Cited by 50 publications

(17 citation statements)

References 84 publications

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“…RSMapMining integrates our developed methods of marine object extraction [33], marine process definition [36], association pattern mining [19], mining strategies [16], and visualization [20]. It also incorporates several other popular components; e.g., object, event, and process definition [32,34,35], and an association pattern mining algorithm [21,23,37]. The preliminary results from a case of study of the northwestern Pacific Ocean are encouraging, and demonstrate that RSMapMining is useful and convenient for obtaining marine association patterns at various levels from the global scale to a detailed view.…”

Section: Discussionmentioning

confidence: 99%

“…RSMapMining develops a spatial cutting tool to obtain the grid pixels of any sea area, and stores them in raster format.  Marine object: An object represents a common attribute or behavior with a precise and "crisp" spatial location and extent [32]. Object-based approaches use homogeneous regions from image segmentation.…”

Section: Extraction Of Marine Informationmentioning

confidence: 99%

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A Remote-Sensing-Driven System for Mining Marine Spatiotemporal Association Patterns

Xue

Qian

et al. 2015

Remote Sensing

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Abstract:Remote sensing is widely used to analyze marine environments. While many effective and advanced methods have been developed, they are generally used independently of each other, despite the potential advantages of combining different modules into an integrated system. We develop here an image-driven remote-sensing mining system, RSMapMining (Remote Sensing driven Marine spatiotemporal Association Pattern Mining system), which consists of three modules. The image preprocessing module integrates image processing techniques and marine extraction methods to build a mining database. The pattern mining module integrates popular algorithms to implement the mining process according to the mining strategies. The third module, knowledge visualization, designs a series of interactive interfaces to visualize the marine data at a variety of scales, from global to grid pixel. The effectiveness of the integrated system is tested in a case study of the northwestern Pacific Ocean. The main contribution of this study is the development of a mining system to deal with marine remote sensing images by integrating popular techniques and methods ranging from information extraction, through visualization, to knowledge discovery.

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Section: Discussionmentioning

confidence: 99%

Section: Extraction Of Marine Informationmentioning

confidence: 99%

A Remote-Sensing-Driven System for Mining Marine Spatiotemporal Association Patterns

Xue

Qian

et al. 2015

Remote Sensing

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“…This change of approach has had an important impact on the development of Geographic Information Science from an exotic tool to be used by map producers and geographers into an integrated and specialized part of Information Science. However, for a long time there has been an increasing demand for further integration of the object-based and field-based models (Bian 2000, Cova & Goodchild 2002, Egenhofer et al 1999, Peuquet 1988, Winter 1998. These early works have focused on describing the co-existence and interactions of the two models.…”

Section: Introductionmentioning

confidence: 99%

On the integration of object‐based models and field‐based models in GIS

Kjenstad

2006

International Journal of Geographical Information Science

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KJENSTAD, K., 2006, On the integration of object-based models and field-based models in GIS. This is an electronic version of an article published in the International Journal of Geographical Information Science, Vol. 20, No. 5, May 2006, 491-509, available online at: http://www.informaworld.com/smpp/content~content=a747921911 Copyright of International Journal of Geographical InformationScience is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use."On the integration of object-based and field-based models in GIS"

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“…One of the great challenges for the near future is knowledge discovery in ever growing spatial sets [4]. Nevertheless, most work in the KDD community up to now has been almost exclusively focused on pattern discovery in relational and transaction databases.…”

Section: Introductionmentioning

confidence: 99%

Discovering Associations between Spatial Objects: An ILP Application

Malerba

Lisi

2001

Inductive Logic Programming

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Abstract. In recent times, there is a growing interest in both the extension of data mining methods and techniques to spatial databases and the application of inductive logic programming (ILP) to knowledge discovery in databases (KDD). In this paper, an ILP application to association rule mining in spatial databases is presented. The discovery method has been implemented into the ILP system SPADA, which benefits from the available prior knowledge on the spatial domain, systematically explores the hierarchical structure of taskrelevant geographic layers and deals with numerical aspatial properties of spatial objects. It operates on a deductive relational database set up by selecting and transforming data stored in the underlying spatial database. Preliminary experimental results have been obtained by running SPADA on geo-referenced census data of Manchester Stockport, UK.

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Progress in computational methods for representing geographical concepts

Cited by 50 publications

References 84 publications

A Remote-Sensing-Driven System for Mining Marine Spatiotemporal Association Patterns

A Remote-Sensing-Driven System for Mining Marine Spatiotemporal Association Patterns

On the integration of object‐based models and field‐based models in GIS

Discovering Associations between Spatial Objects: An ILP Application

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