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Visual and Automatic Data Mining for Exploration of Geographical Metadata
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
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(English)Manuscript (Other academic)
National Category
Other Computer and Information Science
URN: urn:nbn:se:kth:diva-5505OAI: diva2:9895
QC 20110117Available from: 2006-03-21 Created: 2006-03-21 Last updated: 2011-01-17Bibliographically approved
In thesis
1. Data mining of geospatial data: combining visual and automatic methods
Open this publication in new window or tab >>Data mining of geospatial data: combining visual and automatic methods
2006 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

Most of the largest databases currently available have a strong geospatial component and contain potentially useful information which might be of value. The discipline concerned with extracting this information and knowledge is data mining. Knowledge discovery is performed by applying automatic algorithms which recognise patterns in the data.

Classical data mining algorithms assume that data are independently generated and identically distributed. Geospatial data are multidimensional, spatially autocorrelated and heterogeneous. These properties make classical data mining algorithms inappropriate for geospatial data, as their basic assumptions cease to be valid. Extracting knowledge from geospatial data therefore requires special approaches. One way to do that is to use visual data mining, where the data is presented in visual form for a human to perform the pattern recognition. When visual mining is applied to geospatial data, it is part of the discipline called exploratory geovisualisation.

Both automatic and visual data mining have their respective advantages. Computers can treat large amounts of data much faster than humans, while humans are able to recognise objects and visually explore data much more effectively than computers. A combination of visual and automatic data mining draws together human cognitive skills and computer efficiency and permits faster and more efficient knowledge discovery.

This thesis investigates if a combination of visual and automatic data mining is useful for exploration of geospatial data. Three case studies illustrate three different combinations of methods. Hierarchical clustering is combined with visual data mining for exploration of geographical metadata in the first case study. The second case study presents an attempt to explore an environmental dataset by a combination of visual mining and a Self-Organising Map. Spatial pre-processing and visual data mining methods were used in the third case study for emergency response data.

Contemporary system design methods involve user participation at all stages. These methods originated in the field of Human-Computer Interaction, but have been adapted for the geovisualisation issues related to spatial problem solving. Attention to user-centred design was present in all three case studies, but the principles were fully followed only for the third case study, where a usability assessment was performed using a combination of a formal evaluation and exploratory usability.

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. x, 91 p.
Trita-SOM , ISSN 1653-6126 ; 06:01
geographic information science, geoinformatics, geovisualisation, spatial data mining, visual data mining, usability evaluation
National Category
Other Computer and Information Science
urn:nbn:se:kth:diva-3892 (URN)91-7178-297-4 (ISBN)
Public defence
2006-04-07, F3, Lindstedtsvägen 26, KTH, Stockholm, 10:00
QC 20110118Available from: 2006-03-21 Created: 2006-03-21 Last updated: 2011-01-18Bibliographically approved

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Demšar, UrškaHauska, Hans
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