Does GridGIS require more intelligence than GIS?

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

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

3. “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)

4. “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)

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

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

7. 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?

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

9. ‘Doing’ GridGIS - Need for Intelligence (2) • Variation in levels of interaction 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 servicesIn the future GridGIS may equally be used, by an expert or otherwise, in a more explorative adaptive mode

10. How?

11. Non-Grid Pilot Prototype Goal:Design of an ‘intelligent’ module that sits between task and GISFocus task:Spatial interpolationDomain:Creation of griddedmeteorological surfaces for use in environmental models

12. 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.

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

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

15. 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.

16. analyse Collatoral data

18. 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.

19. 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)

20. 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?

21. 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?