Does GridGIS require more intelligence than GIS? Claire Jarvis Department of Geography GEOGRAPHY

Solving environmental problems with the aid of GIS e.g. attributes of biological reserve required to evaluate conservation status Task-related knowledge plus expert reasoning Low-level knowledge plus simple reasoning Command syntax – from user manuals, on-line help systems

“With the vast power of a user friendly GIS increasingly in the hands of the non specialist, the danger that the wrong kind of spatial statistics will become the accepted practice is great” (Anselin 1989)

“It is unwise to throws one’s data into the first available interpolation technique without carefully considering how the results will be affected by the assumptions inherent in the method” (Burrough 1986)

“the time taken to explore, understand, and describe the data set should be amply regarded” (Isaacs & Srivastava 1989)

(‘Intermediate knowledge’ concept after Bhavnani and John, 2000) Body of knowledge required to solve/research environmental problems using GIS e.g. attributes of biological reserve required to evaluate conservation status Task-related knowledge plus expert reasoning Low-level knowledge plus simple reasoning Command syntax – from user manuals, on-line help systems Intermediate knowledge plus (human) expert reasoning e.g. statistical assumptions of individual GIScience techniques

GIS technologies arguably need more intelligence to support their users, in a context where GIS is now much more accessible to ‘naïve’ users Does this position change when moving across to GridGIS?

GridGIS may open up regular or occasional GIS usage to a wider audience, both scientific and public ‘ Doing’ GridGIS - Need for Intelligence (1) Lack of coherent body of documentation Grid GIS will not come with a coherent ‘user manual’ of commands and terminologies Variation in audience

GridGIS may be encountered through an ‘expert’ user interacting with a well designed portal to develop a pre-specified workflow of known data and processing services In the future GridGIS may equally be used, by an expert or otherwise, in a more explorative adaptive mode Variation in levels of interaction ‘ Doing’ GridGIS - Need for Intelligence (2)

How?

Goal: Design of an ‘intelligent’ module that sits between task and GIS Focus task: Spatial interpolation Domain: Creation of gridded meteorological surfaces for use in environmental models Non-Grid Pilot Prototype

Approach Construct a network of rules that assist the user to select an appropriate interpolation method according to: – the task-related knowledge (or “purpose”) of the user; –encoded intermediate knowledge gained from experts in interpolation. Trigger statistical diagnostics to run on the data sets when a rule requires them to be evaluated.

Elements of knowledge Purpose Domain Function characteristics Parameters and assumptions statistical cognitive knowledge

Proportion of case-based knowledge initially low, will increase over time Derived from the theoretical literature. These trigger appropriate statistical diagnostic checks. Extracted from the user, with supporting visualisation where appropriate These rules will be weighted lower than theoretical rules, hence lower proportion overall. Derived from the theoretical literature. These suggest broadly suitable functions for certain types of data.. Contributions to the knowledge base Case-based knowledge gathering within the module Use planned for the interpolated surface Task related knowledge extracted from the user Rules regarding general characteristics of interpolation methods Rules regarding assumptions and parameters for specific interpolation methods Applications in the example domain by literature

Implementation of a prototype intelligent module Stand-alone module; Software environment: Java & Jess; Knowledge acquisition: iterative approach; Knowledge structure: decision tree; Interface design: multi-modal.

analyse Collatoral data

Outputs Interpolation methods that might be and should not be considered for the data set; Any parameters required to interpolate the particular data set (e.g. distance decay parameter for Inverse Distance Weighting); The rationale of the decision process, so the 'intelligent interpolator' also acts as a learning tool.

Conclusions from the pilot Previous work incorporating intelligence into GIS had been computer-intensive or knowledge intensive -- prototype module offers a more balanced approach Successful verification and validation by users, but in a small trial only Needed wider testing to establish truly generic ability. ‘The ultimate aim is to develop an intelligent partnership between user and machine, a relationship which currently lacks balance.’ (Openshaw and Alvanides, 1999)

Incorporating ‘intelligence’ within (Grid)GIS – Questions (1) Should methods be selected mostly according to purpose and domain, or the characteristics of the data? How can purpose be encapsulated within an adaptive Grid processing system? Should intermediate knowledge be associated with GIS functions, or encoded as meta-data? How should we approach metadata regarding GIS services?

Incorporating ‘intelligence’ within (Grid)GIS – Questions (2) How far should a user be aware the decision making process, or should this be hidden? How do we build usable ‘case’ examples into a re-usable body of knowledge? How do we balance rules and case study information, to take the best from inductive and deductive approaches? How can we capture intelligence related to more complex processing tasks; the pilot applied to a small range of services that are likely in an applied context to form only part of a workflow?