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Data mining of geospatial data: combining visual and automatic methods
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
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.
Series
Trita-SOM , ISSN 1653-6126 ; 06:01
Keyword [en]
geographic information science, geoinformatics, geovisualisation, spatial data mining, visual data mining, usability evaluation
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-3892ISBN: 91-7178-297-4 (print)OAI: oai:DiVA.org:kth-3892DiVA: diva2:9900
Public defence
2006-04-07, F3, Lindstedtsvägen 26, KTH, Stockholm, 10:00
Opponent
Supervisors
Note
QC 20110118Available from: 2006-03-21 Created: 2006-03-21 Last updated: 2011-01-18Bibliographically approved
List of papers
1. Knowledge Extraction by Visual Data Mining of Metadata in Site Planning
Open this publication in new window or tab >>Knowledge Extraction by Visual Data Mining of Metadata in Site Planning
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(English)Manuscript (Other academic)
Abstract [en]

The paper describes a tool designed within the first stage of the European project INVISIP in order to explore geographical metadata in the site planning process. A visual data mining approach is applied to a database of geographical metadata to help the user find an optimal subset of the existing geographical datasets for his particular planning task. It allows the user to perform both confirmative and explorative analysis. The approach is implemented in the Visual Data Mining tool, which integrates different types of visualisations with various interaction functionalities. It includes the interactive communication with the user and the brushing and linking process between different visualisations. The paper also presents an example of an application on a test metadatabase which was created for this purpose.

National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-5504 (URN)
Note
QC 20110114Available from: 2006-03-21 Created: 2006-03-21 Last updated: 2011-01-14Bibliographically approved
2. Visual and Automatic Data Mining for Exploration of Geographical Metadata
Open this publication in new window or tab >>Visual and Automatic Data Mining for Exploration of Geographical Metadata
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(English)Manuscript (Other academic)
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-5505 (URN)
Note
QC 20110117Available from: 2006-03-21 Created: 2006-03-21 Last updated: 2011-01-17Bibliographically approved
3. A Visualisation of a Hierarchical Structure in Geographical Metadata
Open this publication in new window or tab >>A Visualisation of a Hierarchical Structure in Geographical Metadata
2004 (English)In: Proceedings of the 7 th AGILE Conference on Geographical Information Science, 2004Conference paper, Published paper (Refereed)
Abstract [en]

The paper presents a visualisation of a hierarchical structure discovered in a repository of geographical metadata by clustering. The hierarchical structure is visualised as a radial tree. The clusters at different levels of detail are represented by the vertices and the subset relationship between the clusters by the edges of the tree. The similarity between elements in different clusters is shown using a colour scheme for the vertices and the edges. The visualisation is integrated in a visual data mining tool for the exploration of the geographical metadata.

Keyword
visualisation, clustering, tree drawing, geographical metadata
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-5506 (URN)
Conference
7th AGILE Conference on Geographic Information Science 29 April – 1 May 2004, Heraklion, Crete
Note
QC 20110117 Uppdaterad från manuskript till konferensbidrag(20110117).Available from: 2006-03-21 Created: 2006-03-21 Last updated: 2011-01-17Bibliographically approved
4. Knowledge discovery in environmental sciences: visual and automatic data mining for radon problem in groundwater
Open this publication in new window or tab >>Knowledge discovery in environmental sciences: visual and automatic data mining for radon problem in groundwater
2007 (English)In: Transactions on GIS, ISSN 1361-1682, E-ISSN 1467-9671, Vol. 11, no 2, 255-281 p.Article in journal (Refereed) Published
Abstract [en]

Efficiently exploring a large dataset with the aim of forming a hypothesis is one of the main challenges in environmental research. The exploration of georeferenced environmental data is usually performed by established statistical methods. This paper presents an alternative approach. The aim of this study was to see if a visual data mining system and an integrated visual-automatic data mining system could be used for data exploration for a particular environmental problem: the occurrence of radon in groundwater. In order to demonstrate this, two data mining systems were built, one consisting of visualisations and the other including an automatic data mining method – a Self-Organising Map (SOM). The systems were designed for exploration of a large multidimensional dataset representing wells in Stockholm County.

Keyword
data mining, data set, environmental research, groundwater, radon, visualization
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-5507 (URN)10.1111/j.1467-9671.2007.01044.x (DOI)2-s2.0-33947611002 (Scopus ID)
Note

QC 20110117

Available from: 2006-03-21 Created: 2006-03-21 Last updated: 2017-11-21Bibliographically approved
5. Exploring geographical data with spatio-visual data mining
Open this publication in new window or tab >>Exploring geographical data with spatio-visual data mining
2006 (English)In: Spatial Data Handling - Status Quo and Progress: Proceedings of the 12th International Symposium on Spatial Data Handling / [ed] Andreas Riedl, Wolfgang Kainz, Gregory Elmes, Springer Verlag , 2006, 149-166 p.Chapter in book (Other academic)
Abstract [en]

Efficiently exploring a large spatial dataset with the aim of forming a hypothesisis one of the main challenges for information science. This studypresents a method for exploring spatial data with a combination of spatialand visual data mining. Spatial relationships are modeled during a datapre-processing step, consisting of the density analysis and vertical viewapproach, after which an exploration with visual data mining follows. The method has been tried on emergency response data about fire and rescueincidents in Helsinki.

Place, publisher, year, edition, pages
Springer Verlag, 2006
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-5508 (URN)10.1007/3-540-35589-8_10 (DOI)2-s2.0-33947588233 (Scopus ID)978-3-540-35588-5 (ISBN)
Note
QC 20110117Available from: 2006-03-21 Created: 2006-03-21 Last updated: 2011-01-17Bibliographically approved
6. Combining formal and exploratory methods for evaluation of an exploratory geovisualization application in a low-cost usability experiment
Open this publication in new window or tab >>Combining formal and exploratory methods for evaluation of an exploratory geovisualization application in a low-cost usability experiment
2007 (English)In: Cartography and Geographic Information Science, ISSN 1523-0406, Vol. 34, no 1, 29-45 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a joined formal and exploratory usability evaluation of a geovisualization tool for emergency response data. The first objective of the evaluation, which is addressed by a formal usability test, is to evaluate the performance of the test participants on pre-defined exploration tasks. The second objective is to attempt to better understand how the participants explore the spatial dataset. This objective is addressed in the exploratory usability part of the experiment, where participants are given free hand to perform exploration in any way they want. The study introduces a low-cost methodology for performing usability evaluation, which uses simple and inexpensive methods for data collection and analysis, yet proves to be adequate to identify potentials and problems of the tool.

National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-5509 (URN)10.1559/152304007780279069 (DOI)
Note
QC 20110117 Tidigare titel: A low-cost usability evaluation of a visual data mining system for geospatial data. Uppdaterad från submitted till published(20110117) Available from: 2006-03-21 Created: 2006-03-21 Last updated: 2011-01-17Bibliographically approved

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