Jarvis CH, Stuart N, Cooper W "Infometric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation" INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 17 (6): 495-516 SEP 2003
Eugene Garfield
garfield at CODEX.CIS.UPENN.EDU
Thu Oct 30 14:54:47 EST 2003
Claire H. Jarvis : c.jarvis at le.ac.uk
TITLE Infometric and statistical diagnostics to provide
artificially-intelligent support for spatial analysis:
the example of interpolation
AUTHOR Jarvis CH, Stuart N, Cooper W
JOURNAL INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
17 (6): 495-516 SEP 2003
Document type: Article Language: English Cited References: 76
Times Cited: 0 Explanation
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.
KeyWords Plus:
GEOGRAPHICAL INFORMATION-SYSTEMS, NEURAL NETWORKS, KNOWLEDGE,
CLASSIFICATION, SELECTION, SPLINES, MODELS, TOOL, GIS
Addresses:
Jarvis CH, Univ Leicester, Dept Geog, Leicester LE1 7RH, Leics, England
Univ Leicester, Dept Geog, Leicester LE1 7RH, Leics, England
Univ Edinburgh, Dept Geog, Edinburgh EH8 9YL, Midlothian, Scotland
Publisher:
TAYLOR & FRANCIS LTD, 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON,
ENGLAND
IDS Number:
718ZR
ISSN:
1365-8816
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