Infometric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation.
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| Title: | Infometric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation. |
|---|---|
| Authors: | Jarvis, Claire H.1 c.jarvis@le.ac.uk, Stuart, Neil2, Cooper, William2 |
| Source: | International Journal of Geographical Information Science. Sep2003, Vol. 17 Issue 6, p495. 22p. |
| Subjects: | Spatial analysis (Statistics), Artificial intelligence, Interpolation |
| Abstract: | The wider uptake of GIS tools by many application areas outside GIScience means that many newer users of GIS will have high-level knowledge of the wider task, and low-level knowledge of specific system commands as given in reference manuals. However, these newer users may not have the intermediate knowledge that experts in GI science have gained from working with GI systems over several years. Such intermediate knowledge includes an understanding of the assumptions implied by the use of certain functions, and an appreciation of how to combine functions appropriately to create a workflow that suits both the data and overall goals of the geographical analysis task. Focusing on the common but non-trivial task of interpolating spatial data, this paper considers how to help users gain the necessary knowledge to complete their task and minimise the possibility of methodological error. We observe that both infometric (or cognitive) knowledge and statistical knowledge are usually required to find a solution that jointly and efficiently meets the requirements of a particular user and data set. Using the class of interpolation methods as an example, we outline an approach that combines knowledge from multiple sources and argue the case for designing a prototype 'intelligent' module that can sit between a user and a given GIS. The knowledge needed to assist with the task of interpolation is constructed as a network of rules, structured as a binary decision tree, that assist the user in selecting an appropriate method according to task-related knowledge (or 'purpose') and the characteristics of the data sets. The decision tree triggers exploratory diagnostics that are run on the data sets when a rule requires to be evaluated. Following evaluation of the rules, the user is advised which interpolation method might be and should not be considered for the data set. Any parameters required to interpolate the particular data set (e.g. a distance decay parameter for Inverse Distance Weighting) are also supplied through subsequent optimisation and model selection routines. The rationale of the decision process may be examined, so the 'intelligent interpolator' also acts as a learning tool. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 10779381 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Infometric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jarvis%2C+Claire+H%2E%22">Jarvis, Claire H.</searchLink><relatesTo>1</relatesTo><i> c.jarvis@le.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Stuart%2C+Neil%22">Stuart, Neil</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Cooper%2C+William%22">Cooper, William</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geographical+Information+Science%22">International Journal of Geographical Information Science</searchLink>. Sep2003, Vol. 17 Issue 6, p495. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Spatial+analysis+%28Statistics%29%22">Spatial analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Interpolation%22">Interpolation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The wider uptake of GIS tools by many application areas outside GIScience means that many newer users of GIS will have high-level knowledge of the wider task, and low-level knowledge of specific system commands as given in reference manuals. However, these newer users may not have the intermediate knowledge that experts in GI science have gained from working with GI systems over several years. Such intermediate knowledge includes an understanding of the assumptions implied by the use of certain functions, and an appreciation of how to combine functions appropriately to create a workflow that suits both the data and overall goals of the geographical analysis task. Focusing on the common but non-trivial task of interpolating spatial data, this paper considers how to help users gain the necessary knowledge to complete their task and minimise the possibility of methodological error. We observe that both infometric (or cognitive) knowledge and statistical knowledge are usually required to find a solution that jointly and efficiently meets the requirements of a particular user and data set. Using the class of interpolation methods as an example, we outline an approach that combines knowledge from multiple sources and argue the case for designing a prototype 'intelligent' module that can sit between a user and a given GIS. The knowledge needed to assist with the task of interpolation is constructed as a network of rules, structured as a binary decision tree, that assist the user in selecting an appropriate method according to task-related knowledge (or 'purpose') and the characteristics of the data sets. The decision tree triggers exploratory diagnostics that are run on the data sets when a rule requires to be evaluated. Following evaluation of the rules, the user is advised which interpolation method might be and should not be considered for the data set. Any parameters required to interpolate the particular data set (e.g. a distance decay parameter for Inverse Distance Weighting) are also supplied through subsequent optimisation and model selection routines. The rationale of the decision process may be examined, so the 'intelligent interpolator' also acts as a learning tool. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/1365881031000114099 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 495 Subjects: – SubjectFull: Spatial analysis (Statistics) Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Interpolation Type: general Titles: – TitleFull: Infometric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jarvis, Claire H. – PersonEntity: Name: NameFull: Stuart, Neil – PersonEntity: Name: NameFull: Cooper, William IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2003 Type: published Y: 2003 Identifiers: – Type: issn-print Value: 13658816 Numbering: – Type: volume Value: 17 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Geographical Information Science Type: main |
| ResultId | 1 |
