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Abstracting Geographic Information in a Data Rich World: by Dirk Burghardt, Cécile Duchêne, William Mackaness

By Dirk Burghardt, Cécile Duchêne, William Mackaness

Research within the box of computerized generalisation has confronted new demanding situations lately because of technological advancements in web-based processing, new visualisation paradigms and entry to large volumes of multi-source info generated via sensors and people. In those contexts, map generalisation must underpin ‘on-demand mapping’, a sort of mapping that responds to person consumer specifications within the thematic choice and visualisation of geographic info. it's this new impetus that drives the learn of the ICA fee on Generalisation and a number of illustration (for instance via its annual workshops, biannual tutorials and guides in foreign journals). This publication has a coherent constitution, every one bankruptcy concentrating on center recommendations and initiatives within the map generalisation in the direction of on-demand mapping. every one bankruptcy provides a state of the art overview, including case reports that illustrate the applying of pertinent generalisation methodologies. The booklet addresses matters from info collecting to multi scaled outputs. hence there are chapters dedicated to defining consumer specifications in dealing with standards, and within the software and overview of map generalisation algorithms. It explores the applying of generalisation methodologies within the context of transforming into volumes of information and the expanding approval for person generated content.

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Extra info for Abstracting Geographic Information in a Data Rich World: Methodologies and Applications of Map Generalisation

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G. ), by the number of objects involved, and by the geographic concept affected. Cartographic constraints (as presented in Fig. 3) can express some of the specification elements listed in Sect. 2: • The information content can be partly specified. Minimal dimensions constraints can help delimit the set of represented entities. g. ‘‘if the use of the building equals ‘habitation’, then the building should not be represented’’). However, the entities that should appear cannot be specified. This is due to the fact that, in generalisation, the information content of the target map is implicitly determined by the information content of the original map.

G. the fact that cycle routes follow roads). Depending on the map use and theme, three integration options are possible. The first option consists of overlaying and geometrically aligning user’s objects on the referential background. g. public phones). This choice can be expressed through a positional constraint (Fig. 10). The second option consists of discarding user’s object geometries and projecting their properties on the referential objects as new thematic attributes. g. statistical data or itineraries), or if it represents physical entities that already are in the referential with less thematic attributes but better geometric accuracy.

26 S. Balley et al. User variables User need Activity/Task to achieve User profile Age Gender Familiarity with maps Visual disabilities Nationality Context off use Position Time Season Device Typical use scale Situation influence Map specification elements Information content Overall level of detail Represented concepts Represented entities Geometric modelling Thematic modelling Data schema Cartographic symbols Colour Size User preferences Likes/dislikes Fig. 4 User variables influencing map specification elements However, the definition of what constitutes an ‘‘expert’’ and its influence over the ability to interpret maps efficiently is a controversial issue (Ooms et al.

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