Smart Spatial Analysis
Smart Spatial Analysis
Introduction
These pages document developments in the ESRC funded project entitled "A smart spatial pattern explorer for the geographical analysis of GIS data", the principal investigator is Prof. Stan Openshaw, with Dr Ian Turton as researcher.Background
The galloping computerisation of nearly all of society's administrative and management functions is creating an immensely data rich situation. Much of these data are geographically referenced; for instance by postal address or postcode. Unfortunately, there are as yet few geographical analysis methods able to explore large databases for evidence of patterns if the analyst has no good ideas of where and when to look for the patterns and what characteristics they may have. As a result many important datasets in both the public and private sector are not being analysed to the fullest extent, if indeed at all. For example, data about crime, disease and marketing behaviour are collected and at best poorly analysed using often inadequate methods.Aims
This site aims to develop a novel new approach to the exploratory analysis of geographical data that is widely applicable. The idea is to use techniques developed in an area of artificial intelligence known as artificial life to create artificial pattern hunting creatures able to move around the complex many dimensional space of a complex multivariate databases in a search for potential patterns where further investigation may be worthwhile. The aim is to develop an automated, smart geographical analysis tool that will make suggestions as to where to look for patterns, when to look, and what to look for. This is not an easy task but the problems can be resolved by the use of computationally intensive methods based upon a supercomputer. The dynamics of these pattern hunting creatures will be visualised using computer animation so that the end-user watching these computer movies will be able to discover whether the patterns being uncovered are real or figments of the imagination. It is believed that perfecting this automated method of analysis will have a most significant impact on the usefulness of many large geographically referenced databases in many different application areas.Results
This project came on-line in October 1997, as various systems are developed and tested they will be listed below.-
GAM/K
The original GAM/1 geographical analysis machine was developed by Openshawet al (1987, 1988). GAM/K is a development by Openshaw and Craft (1991) this version has now been recoded and a WWW interface added. Users are invited to test the on-line version of GAM/K. -
GEM
The Geographical Exploration Machine (GEM) has now been converted to work on the WWW in a similar way to GAM above. Again users are invited to try out the on-line version of GEM. -
Test Data
The CCG has developed two sets of synthetic data to allow people to test cluster detection methods. A brief introduction and the data sets are provided.
References
Openshaw, S., Charlton, M., Wymer, C., and Craft, A., (1987) 'A mark I geographical analysis machine for the automated analysis of point data sets', International Journal of Geographical Information Systems, 1, p335-358.Openshaw, S., Charlton, M., Craft, A. and Birch, J. (1988) 'An investigation of leukaemia clusters by the use of a geographical analysis machine', The Lancet, Feb 6th, 272-273.
Openshaw, S., and Craft, A., (1991) 'Using geographical analysis machines to search for evidence of cluster and clustering in childhood leukaemia and non-Hodgkin Lymphomas in Britain. In G. Draper (ed) 'The Geographical Epidemiology of Childhood Leukaemia and non-Hodgkin Lymphomas in Great Britain 1966-83' Studies in Medical and Population Subjects No 53, OPCS, London, HMSO.
Ian Turton < [email protected] > Last modified: Mon May 18 13:30:14 BST 1998