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Map Generalization. Introduction Concepts conventional cartography geographic information systems Developments conceptual models algorithms knowledge representation. Image Processing Division. Introduction. Data presentation display communication Data integration

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Map generalization l.jpg
Map Generalization

  • Introduction

  • Concepts

    • conventional cartography

    • geographic information systems

  • Developments

    • conceptual models

    • algorithms

    • knowledge representation

  • Image Processing Division


    Introduction l.jpg
    Introduction

    • Data presentation

      • display

      • communication

  • Data integration

    • scale and spatial resolution

    • data quality

  • Derivation of spatial databases

    • spatial modeling

  • Image Processing Division


    Concepts l.jpg
    Concepts

    • The role of a map is to present a factual statement about geographic reality (Robinson, 1960).

    • A map is a data model that intervenes between reality and database (Goodchild, 1992).

    Image Processing Division


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    Concepts

    • Map generalization is the simplification of observable spatial variation to allow its representation on a map (Goodchild, 1991).

    • Map generalization is an information-oriented process intended to universalize the content of a spatial database for what is of interest (Müller, 1991).

    Image Processing Division


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    Concepts

    • Map generalization:

      • reduces complexity

      • retains spatial and attribute accuracy

      • accounts for map purpose and scale

      • provides more ‘information’ or more efficient communication

    Image Processing Division


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    Feature coalescence

    (McMaster and Shea, 1992)

    Image Processing Division


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    Feature selection

    (Monmonier, 1991)

    Image Processing Division


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    Complexity reduction

    (McMaster and Shea, 1992)

    Image Processing Division


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    Attribute accuracy

    (McMaster and Shea, 1992)

    Image Processing Division


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    Map purpose

    (McMaster and Shea, 1992)

    Image Processing Division


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    Developments

    • 1960 to 1975: algorithm development, with emphasis on line simplification.

    • Late 1970s to 1980s: assessment of algorithm efficiency.

    • 1990s: conceptual models; formalization of cartographic knowledge.

    Image Processing Division


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    Developments

    • Seminal attempts at automation

      • Julien Perkal: concept of approximate length of order , where  is a real number.

      • Waldo Tobler: computer rules for numerical generalization.

      • Friedrich Töpfer: amount of information that can be shown per unit area decreases according to geometric progression.

    Image Processing Division


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    Conceptual models

    • Brassel and Weibel

      • structure recognition

        • measures of relative importance

    • process recognition

      • definition of generalization process

  • process modeling

    • compilation of rules

  • process execution

    • generalization of original database

  • data display

  • Image Processing Division


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    Conceptual models

    • McMaster and Shea

      • why?

        • Complexity reduction, maintenance of spatial and attribute accuracy, map purpose and intended audience, retention of clarity

    • when?

      • Geometric conditions, spatial and holistic measures, transformation control

  • how?

    • Spatial and attribute transformation

  • Image Processing Division


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    Algorithmic approach

    • Overemphasis on line simplification

    • Lack of a theory to explain which algorithm is the most appropriate for which object

    • Obscure view of what is exploitable

    • Necessity to derive methods from semantic and topology rather than from form and size

    Image Processing Division


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    Algorithmic approach

    • Douglas and Peucker (1973)

      • redundancy in the number of points of digital lines

  • Cromley (1992)

    • modification of the Douglas-Peucker algorithm

    • hierarchical structure to store ranked points

  • Li and Openshaw (1992)

    • concept of the smallest visible object

    • hybrid vector/raster implementation

  • Image Processing Division


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    Algorithmic approach

    • Visual comparisons - perception

      Attneave’s cat (1954)

    • Geometric measures

      • change in the number of coordinates

      • change in angularity

      • vector displacement

      • areal displacement

    Image Processing Division


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    Knowledge representation

    • Knowledge acquisition

      • conventional KE techniques - communication?

      • analysis of text documents

      • comparison of map series

      • machine learning and neural networks

      • amplified intelligence

    Image Processing Division


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    Knowledge representation

    • If expert systems are to be based upon a consensual knowledge of experts, the map generalization realm will not be suited to expert systems technology (Rieger and Coulson, 1993).

    • Cooperative knowledge systems should result from joint research in AI, cognitive science, work psychology, and social sciences (Keller, 1995).

    Image Processing Division


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    Research agenda

    • Objectives of generalization in the digital context

    • Test scenarios to push the usefulness of existing tools to their limits

    • Cartographic x model-oriented generalizations

    • Explicitness of spatial relations for points, lines, and polygons

    • Research cooperation between mapping agencies and academia

    Image Processing Division