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Journal ArticleDOI

Error Modelling for Integrated GIS

TL;DR: An analytical model is developed to address common errors that can be associated with the generation of GIS layers, such as manual digitizing (positional) errors and automated classification (thematic) errors.
Abstract: A persistent problem for GIS researchers is the quantitative assessment of the overall error, or uncertainty, of a GIS analysis that requires the combination of heterogeneous data sets. Rather than strive for a global solution, this chapter outlines a stepwise approach to a combined error analysis for the integration of diverse data sets in GIS. The presented method was developed for the integration of information derived from remotely sensed data into a GIS database. Even for such a simple analysis, which is routinely performed in environmental GIS applications, an analytical error-handling strategy is still missing, and researchers have made intensive use of simulation techniques. The purpose of this study is to develop an analytical model to address common errors that can be associated with the generation of GIS layers, such as manual digitizing (positional) errors and automated classification (thematic) errors. As a first step, the error model is restricted to the case of point digitizing and maximum ...
Citations
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Journal ArticleDOI
TL;DR: In this article, the authors address the modelling of uncertainty in an integrated geographic information system (GIS), specifically focused on the fusion of activities between GIS and remote sensing, and derive a framework for the propagation of uncertainty through an integrated GIS.
Abstract: This paper addresses the modelling of uncertainty in an integrated geographic information system (GIS), specifically focused on the fusion of activities between GIS and remote sensing. As data is abstracted from its ‘raw’ form to the higher representations used by GIS, it passes through a number of different conceptual data models via a series of transformations. Each model and each transformation process contributes to the overall uncertainty present within the data. The issues that this paper addresses are threefold. Firstly, a description of various models of geographic space is given in terms of the inherent uncertainty characteristics that apply; this is then worked into a simple formalism. Secondly, the various transformation processes that are used to form geographic classes or objects from image data are described, and their effects on the uncertainty properties of data are stated. Thirdly, using the formalism to describe the transformation processes, a framework for the propagation of uncertainty through an integrated GIS is derived. By way of a summary, a table describing sources of accumulated uncertainty across four underlying models of geographic space is derived.

92 citations


Cites background from "Error Modelling for Integrated GIS"

  • ...Ž .rough 1993 , Ehlers and Shi 1997 and Leung and Ž ....

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01 Jan 2005
TL;DR: The QSS Framework for Modelling Qualitative Change: Prospects and Problems (Antony Galton) as mentioned in this paper is a QSS-based framework for modelling qualitative change in GIS.
Abstract: List of Contributors. Preface. 1. Re-presenting Geographical Information Systems (Peter Fisher and David J. Unwin). PART I: NOT JUST OBJECTS. 2. Not Just Objects: Reconstructing Objects (Ola Ahlqvist, Peter Bibby, Matt Duckham, Peter Fisher, Francis Harvey and Nadine Schuurman). 3. Social Dimensions of Object Definition in GIS (Nadine Schuurman). 4. The Linguistic Trading Zones of Semantic Interoperability (Francis Harvey). 5. GIS, Worldmaking and Natural Language (Peter Bibby). 6. Land Use and Land Cover: Contradiction or Complement (Peter Fisher, Alexis Comber and Richard Wadsworth). 7. Transformation of Geographic Information using Crisp, Fuzzy and Rough Semantics (Ola Ahlqvist). 8. Uncertainty and Geographic Information: Computational and Critical Convergence (Matt Duckham and Joanne Sharp). PART II: NOT JUST SPACE. 9. Not Just Space: An Introduction (Michael Batty, Antony Galton and Marcos Llobera). 10. The QSS Framework for Modelling Qualitative Change: Prospects and Problems (Antony Galton). 11. Network Geography: Relations, Interactions, Scaling and Spatial Processes in GIS (Michael Batty). 12. The Nature of Everyday Experience: Examples from the Study of Visual Space (Marcos Llobera). PART III: TIME AS WELL. 13. Time As Well: An Introduction (Jonathan Raper, Harvey J. Miller, Subhrajit Guhathakurta, Robert Muetzelfeldt and Tao Cheng). 14. Spatio-Temporal Ontology for Digital Geographies (Jonathan Raper). 15. Modeling and Visualizing Linear and Cyclic Changes (Tao Cheng). 16. What about People in Geographic Information Science? (Harvey J. Miller). 17. Dynamic Spatial Modelling in the Simile Visual Modelling Environment (Robert Muetzelfeldt and Matt Duckham). 18. Telling Stories with Models: Reflecting on Land Use and Ecological Trends in the San Pedro Watershed (Subhrajit Guhathakurta). PART IV: NOT 'THERE' YET? 19. Conclusion: Towards a Research Agenda (David J. Unwin and Peter Fisher). Index.

49 citations

01 Jan 2005
TL;DR: The increasingly widespread use of Geographical Information Systems (GIS) has meant that a version of "geography" has been exported to manyother disciplines and walks of life where this technology has been found to be useful.
Abstract: The increasingly widespread use of Geographical Information Systems (GISystems,widely known as GIS) has meant that a version of ‘geography’ has been exported to manyother disciplines and walks of life where this technology has been found to be useful. Asan undergraduate, one of us read Applied Geography by Dudley Stamp (Stamp, 1960). Itis full of ideas and examples of the applications of geography in the real world, but it hastaken the better part of half a century for much of Stamp’s vision to become reality; therest of the world – including many cognate sciences – have discovered the power ofsomething they call ‘geography’. It is wise to treat the word with caution, however, sincethere are at least three ways in which ‘geography’ is used. First, and at its simplest,geography is the places and spaces on our planet. Second, in the analysis of geographicinformation, ‘geography’ is often used as a short hand for the spaces and distances usedto explain or model some phenomenon. Third, there is the Geography of our schools,colleges and research institutes; the academic study of the previous two readings.Typically, it is the usefulness of the second usage, in turn almost entirely a consequenceof the phenomenon of spatial autocorrelation, which explains the evident popularity ofGISystems.In his essay in Political Geography Quarterly, Peter Taylor (1990) referred to thisprocess as the ‘imperialism of the new geography’. In the vanguard of this imperialismhas been the technology of GISystems, which Taylor paints as the villain of the piece.

22 citations

Journal ArticleDOI
TL;DR: It is argued that classification schemes that are based on parameterized definitions could be assessed for problematic categories during their construction using this approach, and thus, enabling the identification of a thematic vagueness component to supplement the more traditional statistical measures derived from the error matrix.
Abstract: We present a rationale and method for representing the vagueness in taxonomic class definitions in cases where classes are described by a set of characteristics, such as those sometimes used as the basis for land-cover category discrimination. We further describe methods to estimate the semantic similarity between any two classes by calculating semantic similitude metrics based on such parameterized class definitions. Our working hypothesis is that a large similitude would predict categories that will be more prone to confusion and hence image or map misclassification. We use two different existing data sets to demonstrate and evaluate the method, and the results support our original hypothesis. Consequently, we argue that classification schemes that are based on parameterized definitions could be assessed for problematic categories during their construction using our approach, and thus, enabling the identification of a thematic vagueness component to supplement the more traditional statistical measures derived from the error matrix.

16 citations

References
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Book
01 Jan 2008
TL;DR: In this paper, the authors present an introduction to quantitative evaluation of satellite and aircraft derived from remotely retrieved data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations.
Abstract: From the Publisher: The book provides the non-specialist with an introduction to quantitative evaluation of satellite and aircraft derived from remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations. Problems conclude each chapter.

3,532 citations

Journal Article
TL;DR: In this article, the authors provide a broad overview of spatial data error sources, and identify priority research topics which will reduce impediments and enhance the quality of integrated remote sensing and GIS data.
Abstract: Error associated with the remote sensing and GIS data acquisition, processing, analysis, conversion, and final product presentation can have a significant impact on the confidence of decisions made using the data. The goal of this paper is to provide a broad overview of spatial data error sources, and to identify priority research topics which will reduce impediments and enhance the quality of integrated remote sensing and GIS data. Potential sources of error will be identified at each data integration process step. Impacts of error propagation on decision making and implementation processes will be assessed, and priority error quantification research topics will be recommended.

320 citations

Journal ArticleDOI
TL;DR: An approach to determine uncertainties and their propagation in dynamic change detection based on classified remotely-sensed images and a visualization technique, using 3-D and colour, was developed to present uncertainties.
Abstract: This paper provides an approach to determine uncertainties and their propagation in dynamic change detection based on classified remotely-sensed images. First, the uncertainties of a classified image using maximum likelihood (ML) classification are determined. The probability vectors which are generated during the maximum likelihood classification are used as uncertainty indicators. Secondly, the uncertainty propagation of classified multi-date images is described using mathematical language for problem description. Based on this mathematical formulation, two techniques were used to calculate the uncertainty propagation. One is based on the product rule in probability theory and the other is based on a certainty factor model with probabilistic interpretation. Thirdly, a visualization technique, using 3-D and colour, was developed to present uncertainties.

34 citations