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Spatial data quality capture through inductive learning

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Spatial Cognition and Computation

Abstract

The relatively weak uptake of spatial error handling capabilities bycommercial GIS companies and users can in part be attributed to therelatively low availability and high costs of spatial data qualityinformation. Based on the well established artificial intelligencetechnique of induction, this paper charts the development of anautomated quality capture tool. By learning from example, the tool makesvery efficient use of scarce spatial data quality information, sohelping to minimise the cost and maximise availability of data quality.The example application of the tool to a telecommunications legacy datacapture project indicates the practicality and potential value of theapproach.

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References

  • Abadi, M. and Cardelli, L. (1996). A Theory of Objects. New York: Springer-Verlag.

    Google Scholar 

  • Agumya, A. and Hunter, G. (1997). Determining Fitness for Use of Geographic Information, ITC Journal (2): 109–113.

    Google Scholar 

  • Aspinall, R. (1992). An Inductive Modelling Procedure Based on Bayes Theorem for Analysis of pattern in Spatial Data, International Journal of Geographical Information Systems 6(2): 105–121.

    Google Scholar 

  • Bastin, L., Wood, J. and Fisher, P. (1999). Visualisation of Fuzzy Spatial Information in Spatial Decision Making. In K. Lowell and A. Jaton (eds.), Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources, Chapter 18 (pp. 147–150). Michigan: Ann Arbor.

    Google Scholar 

  • Bennet, D. and Armstrong, M. (1996). An Inductive Based Approach to Terrain Feature Extraction, Cartography and Geographic Information Systems 23(1): 3–19.

    Google Scholar 

  • Campbell, G., Carker, L. and Egesborg, P. (1994). A GIS-BasedMultipurpose Digital Cadastre for Canada Lands. In FIG Congress XX.

  • CEN/TC287 (1996). Draft European Standard: Geographic Information - Quality. Technical Report prEN 287008, European Committee for Standardisation.

  • Codd, E. (1970). A Relational Model of Data for Large Stored Data Banks. Communications of the ACM 13(6): 377–387.

    Google Scholar 

  • Duckham, M. (2001). Object Calculus and the Object-Oriented Analysis and Design of an Error-Sensitive GIS, GeoInformatica 5(3): 261–289.

    Google Scholar 

  • Duckham, M. and Drummond, J. (1999). Implementing and Object-Oriented Approach to Data Quality. In B. Gittings (ed.), Integrating Information Infrastructures with GI Technology (pp. 53–64). London: Taylor and Francis.

    Google Scholar 

  • Egenhofer, M. and Frank, A. (1989). Object-Oriented Modeling: Inheritance and Propagation. In Proceedings Auto-Carto 9 (pp. 588–598).

    Google Scholar 

  • Geographic Data BC (1996). Spatial Archive and Interchange Format Release 3.2 Formal Definition. http://www.env.gov.bc.ca/gdbc/saif32/. Last modified 14 September 1999, last accessed 21 December 1999.

  • Goodchild, M. (1999). Measurement-Based GIS. In W. Shi, M. Goodchild and P. Fisher (eds.), Proceedings of the International Symposium on Spatial Data Quality (pp. 1–9).

  • Hankin, C. (1994). Lambda Calculi: A Guide for Computer Scientists. Oxford: Clarendon Press.

    Google Scholar 

  • Heuvelink, G. (1998). Error Propagation in Environmental Modelling with GIS, Research monographs in GIS. London: Taylor and Francis.

    Google Scholar 

  • Hunter, G. (1999). Reporting Spatial Data Quality: From Concepts to Reality. In W. Shi, M. Goodchild and P. Fisher (eds.), Proceedings of the International Symposium on Spatial Data Quality (pp. 343–353).

  • Kösters, K., Pagel, B.-U. and Six, H.-W. (1997). GIS-Application Development with GEOOOA, International Journal of Geographical Information Systems 11(4): 307–335.

    Google Scholar 

  • Lanter, D. (1991). Design of a Lineage-Based Meta-Data Base for GIS, Cartography and Geographic Information Systems 18(4): 255–261.

    Google Scholar 

  • Lanter, D. and Veregin, H. (1992). A Research Paradigm for Propagating Error in Layer-Based GIS, Photogrammetric Engineering and Remote Sensing 58(6): 825–833.

    Google Scholar 

  • Mikhail, E. (1978). Observations and Least Squares. New York: IEP Dun Donnely.

    Google Scholar 

  • Openshaw, S., Charlton, M. and Carver, S. (1991). Error Propagation: A Monte Carlo Simulation. In I. Masser and M. Blakemore (eds.), Handling Geographical Information (pp. 78–101). New York: Longman.

    Google Scholar 

  • Qiu, J. and Hunter, G. (1999). Managing Data Quality Information. In W. Shi, M. Goodchild and P. Fisher (eds.), Proceedings of the International Symposium on Spatial Data Quality (pp. 384–395).

  • Quinlan, J. (1979). Discovering Rules by Induction from Large Collections of Examples. In D. Michie (ed.), Expert Systems in the Micro-Electronic Age (pp. 168–201). Edinburgh University Press.

  • Quinlan, J. (1983). Learning Efficient Classification Procedures and Their Application to Chess end Games. In R. Michalski, J. Carbonell and T. Mitchell (eds.), Machine Learning: An Artificial Intelligence Approach, Chapter 15 (pp. 463–482). California: Morgan Kauffmann.

    Google Scholar 

  • Ramlal, B. and Drummond, J. (1992). A GIS Uncertainty Subsystem. In Archives ISPRS Congress XVII, Vol. 29.B3 (pp. 356–362).

    Google Scholar 

  • Russell, S. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. New Jersey: Prentice Hall.

    Google Scholar 

  • Shannon, C. (1948). A Mathematical Theory of Communication, The Bell System Technical Journal 27: 379–423, 623-656.

    Google Scholar 

  • Smith, B. (1996). NLIS in 1996: The Pilot Project Expands. Mapping Awareness 10(2): 22–24.

    Google Scholar 

  • Susmaga, R. (1997). Analyzing Discretizations of Continuous Attributes Given a Monotonic Discrimination Function. Intelligent data analysis 1(3).

  • Tobler, W. (1970). A Computer Movie: Simulation of Population Change in the Detriot Region, Economic Geography 46: 234–240.

    Google Scholar 

  • Unwin, D. (1995). Geographical Information Systems and the Problem of Error and Uncertainty. Progress in Human Geography 19(4): 549–558.

    Google Scholar 

  • US Geological Survey (1999). SDTS standard. http://mcmcweb.er.usgs.gov/sdts/. Last modified 16 June 1999, last accessed 21 December 1999.

  • van der Wel, F., Hootsmans, R. and Ormeling, F. (1994). Visualization of Data Quality. In A. MacEachren and D. Taylor (eds.), Visualization in Modern Cartography (pp. 313–331).

  • van Elzakker, C., Ramlal, B. and Drummond, J. (1992). The Visualisation of GIS Generated Information Quality. In Archives ISPRS Congress XVII, Vol. 29.B4 (pp. 608–615).

    Google Scholar 

  • Veregin, H. (1989). Error Modeling for the Map Overlay Operation. In M. Goodchild and S. Gopal (eds.), Accuracy of Spatial Databases (pp. 3–18). London: Taylor and Francis.

    Google Scholar 

  • Walker, P. and Moore, D. (1988). SIMPLE: An Inductive Modelling and Mapping Tool for Spatially-Oriented Data. International Journal of Geographical Information Systems 2(4): 347–363.

    Google Scholar 

  • Wesseling, C. and Heuvelink, G. (1993). Manipulating Qualitative Attribute Accuracy in Vector GIS. In: Proceedings Fourth European Conference on Geographical Information Systems, Vol. 1 (pp. 675–684).

    Google Scholar 

  • Worboys, M., Hearnshaw, H. and Maguire, D. (1990). Object-Oriented Data Modelling for Spatial Databases. International Journal of Geographical Information Systems 4(4): 369–383.

    Google Scholar 

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Duckham, M., Drummond, J. & Forrest, D. Spatial data quality capture through inductive learning. Spatial Cognition and Computation 2, 261–282 (2000). https://doi.org/10.1023/A:1015527221658

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