Incorporating a new computational reasoning approach to spatial modelling

Part of the GeoComputation '96 Special Issue 96/25; follow the "related link" to download the entire collection as a single document.Decision support systems, statistics and expert systems were some of the mainstay techniques used for modelling environmental phenomena. Now modelling systems utilise artificial intelligence (AI) techniques for the extra computational analysis they provide. Whilst operating in a toolbox environment and by adopting AI techniques, the geographic information system (GIS) modellers have greater options available for solving problems. This paper outlines a new approach in applying artificial intelligence techniques to solve spatial problems. The approach combines case-based reasoning (CBR) with geographic information systems and allows both techniques to be applied to solve spatial problems. More specifically this paper examines techniques applied to the problem of soil classification. Spatial cases are defined and analysed using the case-based reasoning techniques of retrieve, reuse, revise and retain. Once the structure of cases are defined a case base is compiled. When the case base is of sufficient size, the problem of soil classification is tested using this new approach. The problem is solved by searching the case base for another spatial phenomena similar to that which exists. Then the knowledge from that searched case is used to formulate an answer to the problem. A comparison of the results obtained by this approach and a traditional method of soil classification is then undertaken. This paper also documents the saving data concept in translating from decision trees to CBR. The logistics of the problems that are characteristic of case-based reasoning systems are discussed, for example, how should the spatial domain of an environmental phenomena be best represented in a case base? What are the constraints of CBR, what data are lost, and what functions are gained? Finally, the following question is posed: “to what real world level can the environment be modelled using GIS and case-based reasoning techniques”?UnpublishedAamodt, A. and E. Plaza, 1994. Case-based Reasoning: Foundational Issues, Methodological Variations and System Approaches. Artificial Intelligence Communications, Vol.7 No.1. Aha, D. W. 1994. Case-Based Reasoning, AIRIES 94, Workshop. Biloxi, MS. Althoff, K. Wess, S. and M. Manago, 1994. Reasoning With Cases-Theory and Practice ECAI. Benwell, G. L. 1996. A Land Speed Record? Some Management Issues to Address, The Tewkesbury Lecture, and Invited Keynote Address, Proceedings of the Conference on Managing Geographic Information For Success, Conference Proc, Department of Geomatics, The University of Melbourne, pp70-75. Berger, J. 1994. Roentgen: Radiation Therapy and Case-based Reasoning. In the Proceedings of the Conference on Artificial Intelligence Applications, Proceedings. pp.171-177. Branting, K. L. and J. D. Hastings, 1994. An Empirical Evaluation of Model-based Case Matching and Adaption. American association for artificial intelligence, case-based reasoning workshop, Proceedings. Seattle, Washington. Burrough, P. A. and A. U. Frank, 1995. Concepts and Paradigms In Spatial Information: are Current Geographic Information Systems Truely Generic? Vol.9. No.2. pp.101-116. Burstein, F. and H. Smith, 1994. Case based Decision Making for Intelligent Decision Support. Department of Information Systems, University of Monash. Fischer, M. M. and P. Nijkamp, 1993. Design and Use of Geographic Information Systems and Spatial Models. In: Geographical information systems, spatial modelling, and policy evaluation, Eds. M. M. Fischer and N. P. Springer Verlag, Berlin. Frank, A. U. 1996. Qualitative Spatial Reasoning: Cardinal Directions as an Example International Journal of Geographical Information Systems, Vol.10. No.3. pp.269-290. Hernandez, D. 1993. Reasoning with Qualitative Representations: Exploiting the Structure of Space. In Piera Carrete and Singh pages 493-502. Hewitt, A. E. 1995. Soils of the Conroy Land System, Central Otago. Lincoln, Canterbury, New Zealand, Manaaki Whenua Press. Landcare Research Science Series. Higham, E.C., Holt A. & G.W. Kearsley 1996. Tourist Flow Reasoning: The Spatial Similarities of Tourist Movements. In the Proceedings of the 8th Annual Colloquim of the Spatial Information Research Centre, Otago University, Dunedin, New Zealand, pp 69-78. Holt, A. Benwell, G. L. & Kasabov, N. K. (In press) The Case-Based Reasoning (CBR) Approach, Hand Book for Geostatistics and Spatial Data Analysis: Elements of Interpretation and Examples, Editors Shibli, S. A. R. Michel, M. & D. Gregoire. Holt, A. and G. L. Benwell, 1996. Case-based Reasoning and Spatial Analysis. Journal of the Urban and Regional Information Systems Association, Vol.8. No.1. pp 27-36. Holt, A. 1996. Allowing the Environment to Model Itself. Environmental Perspectives, A Triannuual Newsletter published by the Environmental Policy and Management Reseach Centre. Issue 10. Holt, A. and G. L. Benwell, 1995a. Spatial Reckoning using Case-based Reasoning. 8th Australian Artificial Intelligence conference incorporating the Workshop on AI in the environment, Proceedings. Canberra, pp. 53-66. Holt, A. and G. L. Benwell, 1995b. Case-based Reasoning with Spatial Data. The 2nd New Zealand International two-stream conference on Artificial Neural Networks and Expert Systems, Proceedings. Eds. N.K. Kasabov and G. Coghill. Dunedin, New Zealand, IEEE Computer Society Press, pp. 385-388. Irvin, B. J. Ventura, S. J. & B.K. Slater, 1995. Landform Classification for Soil-landscape Studies. Annual ESRI User Conference. Jankowski, P. 1995. Integrating Geographical Information Systems and Multiple Criteria Decision-making Methods. Vol.9. No.3. pp.251-273. Jones, E. K. and A. Roydhouse, 1994. Spatial Representations of Meteorological Data for Intelligent Retrieval. The Sixth Annual Colloquium of the Spatial Research Centre, Proceedings. Eds. G.L. Benwell and N.C. Sutherland. Dunedin, New Zealand. pp.45-58. Kasabov, N. and R. L. Trifonov, 1993. Using hybrid Connectionist Systems for Spatial Information Processing. The Fifth Annual Colloquim of the Spatial Research Centre, Proceedings. Eds. Benwell G. L. and N. C. Sutherland. Dunedin, New Zealand.pp85-96. Kasabov, N. K. and D. Nikovski, 1992. Prognostic Expert Systems on a Hybrid Connectionist Environment. In: Artificial intelligence- methodology, systems, applications, Eds. B. Du Boulay and A. V. S. Gurev. Elsevier, Holland. Kolodner, J. 1993. Case Based Reasoning. San Mateo, Morgan Kaufmann Publishers. Laurini, R. and D. Thompson, 1992. Spatial Knowledge. In: Fundamentals of Spatial Information Systems, Academic Press, London. Leake D. B. 1995. Case-Base Reasoning: Issues, Methods and Technology, A Tutorial for the First International Conference on Case-Based Reasoning, Sesimbra, Portugal. Lekkas, G. P. N. M. Avouris, & L.G. Viras, 1994. Case-Based Reasoning in Environmental Monitoring Applications. Applied Artificial Intelligence An International Journal, Vol.8. No.3. pp.359-376. Leung, Y. and K. S. Leung, 1993. An Intelligent Expert System Shell for Knowledge-based Geographical Information Systems: 1. The Tools. International Journal of Geographical Information Systems, Vol.7. No.3. pp.189-199. Malek, M. and A. Labbi, 1995. Integration of Case based Reasoning and Neural Networks Approaches for Classification. Openshaw, S. 1995. Human Systems Modelling as a New Grand Challenge Area in Science (Commentary). Environment and Planning A, Vol.27. pp.159-164. Openshaw, S. 1993. Modelling Spatial Interaction Using a Neural Net. In: Geographical information systems, spatial modelling, and policy evaluation, Eds. M. M. Fischer and N. P. Springer Verlag, Berlin. Skidmore, A. 1995. Neural Networks and GIS. GIS User, No.11 (May-July, 1995), pp.53-55. Skidmore, A. K. Baang, J.S. and P. Luckananurug, 1992. Knowledge-based Methods in Remote Sensing and GIS. The 6th Australasian Remote Sensing Conference, Proceedings. Wellington, New Zealand, pp. 394-403. Skidmore, A. K. Ryan, P. J. Dawes, W. Short, D. and E. O'Loughlin, 1991. Use of An Expert System to Map Forest Soils from a Geographical Information System. International Journal of Geographical Information Systems, Vol.5. No.4. pp.431-445. Smith, T. R. and Yiang, J.E. 1991. Knowledge-based Approaches in GIS. In: Geographic Information Systems, Eds. D.J. Maguire, M. F. Goodchild and D. Rhind. Longman Scientific and Technical, Essex. Webster, C. 1990. Rule-based Spatial Search. Vol.4. No.3. pp.241-259. Williams, G. J. 1995. Templates for Spatial Reasoning in Responsive Geographic Information Systems. International Journal of Geographical Information Systems, Vol.9. No.2. pp.117-131

Incorporating a new computational reasoning approach to spatial modelling

Part of the GeoComputation '96 Special Issue 96/25; follow the "related link" to download the entire collection as a single document.Decision support systems, statistics and expert systems were some of the mainstay techniques used for modelling environmental phenomena. Now modelling systems utilise artificial intelligence (AI) techniques for the extra computational analysis they provide. Whilst operating in a toolbox environment and by adopting AI techniques, the geographic information system (GIS) modellers have greater options available for solving problems. This paper outlines a new approach in applying artificial intelligence techniques to solve spatial problems. The approach combines case-based reasoning (CBR) with geographic information systems and allows both techniques to be applied to solve spatial problems. More specifically this paper examines techniques applied to the problem of soil classification. Spatial cases are defined and analysed using the case-based reasoning techniques of retrieve, reuse, revise and retain. Once the structure of cases are defined a case base is compiled. When the case base is of sufficient size, the problem of soil classification is tested using this new approach. The problem is solved by searching the case base for another spatial phenomena similar to that which exists. Then the knowledge from that searched case is used to formulate an answer to the problem. A comparison of the results obtained by this approach and a traditional method of soil classification is then undertaken. This paper also documents the saving data concept in translating from decision trees to CBR. The logistics of the problems that are characteristic of case-based reasoning systems are discussed, for example, how should the spatial domain of an environmental phenomena be best represented in a case base? What are the constraints of CBR, what data are lost, and what functions are gained? Finally, the following question is posed: “to what real world level can the environment be modelled using GIS and case-based reasoning techniques”?UnpublishedAamodt, A. and E. Plaza, 1994. Case-based Reasoning: Foundational Issues, Methodological Variations and System Approaches. Artificial Intelligence Communications, Vol.7 No.1. Aha, D. W. 1994. Case-Based Reasoning, AIRIES 94, Workshop. Biloxi, MS. Althoff, K. Wess, S. and M. Manago, 1994. Reasoning With Cases-Theory and Practice ECAI. Benwell, G. L. 1996. A Land Speed Record? Some Management Issues to Address, The Tewkesbury Lecture, and Invited Keynote Address, Proceedings of the Conference on Managing Geographic Information For Success, Conference Proc, Department of Geomatics, The University of Melbourne, pp70-75. Berger, J. 1994. Roentgen: Radiation Therapy and Case-based Reasoning. In the Proceedings of the Conference on Artificial Intelligence Applications, Proceedings. pp.171-177. Branting, K. L. and J. D. Hastings, 1994. An Empirical Evaluation of Model-based Case Matching and Adaption. American association for artificial intelligence, case-based reasoning workshop, Proceedings. Seattle, Washington. Burrough, P. A. and A. U. Frank, 1995. Concepts and Paradigms In Spatial Information: are Current Geographic Information Systems Truely Generic? Vol.9. No.2. pp.101-116. Burstein, F. and H. Smith, 1994. Case based Decision Making for Intelligent Decision Support. Department of Information Systems, University of Monash. Fischer, M. M. and P. Nijkamp, 1993. Design and Use of Geographic Information Systems and Spatial Models. In: Geographical information systems, spatial modelling, and policy evaluation, Eds. M. M. Fischer and N. P. Springer Verlag, Berlin. Frank, A. U. 1996. Qualitative Spatial Reasoning: Cardinal Directions as an Example International Journal of Geographical Information Systems, Vol.10. No.3. pp.269-290. Hernandez, D. 1993. Reasoning with Qualitative Representations: Exploiting the Structure of Space. In Piera Carrete and Singh pages 493-502. Hewitt, A. E. 1995. Soils of the Conroy Land System, Central Otago. Lincoln, Canterbury, New Zealand, Manaaki Whenua Press. Landcare Research Science Series. Higham, E.C., Holt A. & G.W. Kearsley 1996. Tourist Flow Reasoning: The Spatial Similarities of Tourist Movements. In the Proceedings of the 8th Annual Colloquim of the Spatial Information Research Centre, Otago University, Dunedin, New Zealand, pp 69-78. Holt, A. Benwell, G. L. & Kasabov, N. K. (In press) The Case-Based Reasoning (CBR) Approach, Hand Book for Geostatistics and Spatial Data Analysis: Elements of Interpretation and Examples, Editors Shibli, S. A. R. Michel, M. & D. Gregoire. Holt, A. and G. L. Benwell, 1996. Case-based Reasoning and Spatial Analysis. Journal of the Urban and Regional Information Systems Association, Vol.8. No.1. pp 27-36. Holt, A. 1996. Allowing the Environment to Model Itself. Environmental Perspectives, A Triannuual Newsletter published by the Environmental Policy and Management Reseach Centre. Issue 10. Holt, A. and G. L. Benwell, 1995a. Spatial Reckoning using Case-based Reasoning. 8th Australian Artificial Intelligence conference incorporating the Workshop on AI in the environment, Proceedings. Canberra, pp. 53-66. Holt, A. and G. L. Benwell, 1995b. Case-based Reasoning with Spatial Data. The 2nd New Zealand International two-stream conference on Artificial Neural Networks and Expert Systems, Proceedings. Eds. N.K. Kasabov and G. Coghill. Dunedin, New Zealand, IEEE Computer Society Press, pp. 385-388. Irvin, B. J. Ventura, S. J. & B.K. Slater, 1995. Landform Classification for Soil-landscape Studies. Annual ESRI User Conference. Jankowski, P. 1995. Integrating Geographical Information Systems and Multiple Criteria Decision-making Methods. Vol.9. No.3. pp.251-273. Jones, E. K. and A. Roydhouse, 1994. Spatial Representations of Meteorological Data for Intelligent Retrieval. The Sixth Annual Colloquium of the Spatial Research Centre, Proceedings. Eds. G.L. Benwell and N.C. Sutherland. Dunedin, New Zealand. pp.45-58. Kasabov, N. and R. L. Trifonov, 1993. Using hybrid Connectionist Systems for Spatial Information Processing. The Fifth Annual Colloquim of the Spatial Research Centre, Proceedings. Eds. Benwell G. L. and N. C. Sutherland. Dunedin, New Zealand.pp85-96. Kasabov, N. K. and D. Nikovski, 1992. Prognostic Expert Systems on a Hybrid Connectionist Environment. In: Artificial intelligence- methodology, systems, applications, Eds. B. Du Boulay and A. V. S. Gurev. Elsevier, Holland. Kolodner, J. 1993. Case Based Reasoning. San Mateo, Morgan Kaufmann Publishers. Laurini, R. and D. Thompson, 1992. Spatial Knowledge. In: Fundamentals of Spatial Information Systems, Academic Press, London. Leake D. B. 1995. Case-Base Reasoning: Issues, Methods and Technology, A Tutorial for the First International Conference on Case-Based Reasoning, Sesimbra, Portugal. Lekkas, G. P. N. M. Avouris, & L.G. Viras, 1994. Case-Based Reasoning in Environmental Monitoring Applications. Applied Artificial Intelligence An International Journal, Vol.8. No.3. pp.359-376. Leung, Y. and K. S. Leung, 1993. An Intelligent Expert System Shell for Knowledge-based Geographical Information Systems: 1. The Tools. International Journal of Geographical Information Systems, Vol.7. No.3. pp.189-199. Malek, M. and A. Labbi, 1995. Integration of Case based Reasoning and Neural Networks Approaches for Classification. Openshaw, S. 1995. Human Systems Modelling as a New Grand Challenge Area in Science (Commentary). Environment and Planning A, Vol.27. pp.159-164. Openshaw, S. 1993. Modelling Spatial Interaction Using a Neural Net. In: Geographical information systems, spatial modelling, and policy evaluation, Eds. M. M. Fischer and N. P. Springer Verlag, Berlin. Skidmore, A. 1995. Neural Networks and GIS. GIS User, No.11 (May-July, 1995), pp.53-55. Skidmore, A. K. Baang, J.S. and P. Luckananurug, 1992. Knowledge-based Methods in Remote Sensing and GIS. The 6th Australasian Remote Sensing Conference, Proceedings. Wellington, New Zealand, pp. 394-403. Skidmore, A. K. Ryan, P. J. Dawes, W. Short, D. and E. O'Loughlin, 1991. Use of An Expert System to Map Forest Soils from a Geographical Information System. International Journal of Geographical Information Systems, Vol.5. No.4. pp.431-445. Smith, T. R. and Yiang, J.E. 1991. Knowledge-based Approaches in GIS. In: Geographic Information Systems, Eds. D.J. Maguire, M. F. Goodchild and D. Rhind. Longman Scientific and Technical, Essex. Webster, C. 1990. Rule-based Spatial Search. Vol.4. No.3. pp.241-259. Williams, G. J. 1995. Templates for Spatial Reasoning in Responsive Geographic Information Systems. International Journal of Geographical Information Systems, Vol.9. No.2. pp.117-131