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Map Generalization

Map Generalization

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Map Generalization

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  1. Map Generalization • Introduction • Concepts • conventional cartography • geographic information systems • Developments • conceptual models • algorithms • knowledge representation Image Processing Division

  2. Introduction • Data presentation • display • communication • Data integration • scale and spatial resolution • data quality • Derivation of spatial databases • spatial modeling Image Processing Division

  3. 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

  4. 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

  5. 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

  6. Feature coalescence (McMaster and Shea, 1992) Image Processing Division

  7. Feature selection (Monmonier, 1991) Image Processing Division

  8. Complexity reduction (McMaster and Shea, 1992) Image Processing Division

  9. Attribute accuracy (McMaster and Shea, 1992) Image Processing Division

  10. Map purpose (McMaster and Shea, 1992) Image Processing Division

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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