By Dirk Burghardt, Cécile Duchêne, William Mackaness
Research within the box of computerized generalisation has confronted new demanding situations in recent times because of technological advancements in web-based processing, new visualisation paradigms and entry to huge volumes of multi-source facts generated through sensors and people. In those contexts, map generalisation must underpin ‘on-demand mapping’, a kind of mapping that responds to person person specifications within the thematic choice and visualisation of geographic info. it really is this new impetus that drives the examine of the ICA fee on Generalisation and a number of illustration (for instance via its annual workshops, biannual tutorials and courses in overseas journals). This booklet has a coherent constitution, each one bankruptcy targeting middle recommendations and projects within the map generalisation in the direction of on-demand mapping. every one bankruptcy offers a state of the art overview, including case experiences that illustrate the appliance of pertinent generalisation methodologies. The publication addresses matters from info collecting to multi scaled outputs. therefore there are chapters dedicated to defining person requisites in dealing with requirements, and within the software and overview of map generalisation algorithms. It explores the appliance of generalisation methodologies within the context of growing to be volumes of information and the expanding approval for consumer generated content.
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Additional resources for Abstracting Geographic Information in a Data Rich World: Methodologies and Applications of Map Generalisation
The map derivation is followed by an iterative evaluation process. The map is reviewed by the data engineer (who can select other tools or modify their parameters to make the output closer to the specifications), by the cartographer (who can change or tighten the specifications) and by the customer (who can express new requirements). The process terminates when the customer is satisfied. On-demand mapping is an automatic version of the process represented in Fig. 2. The cartographer and data engineer are replaced by an expert system interacting directly with end-users.
Following the EuroSDR testbed of generalisation software (Sect. 5), Burghardt et al. (2007) produced a fine-grained classification of cartographic constraints (Fig. 3). Constraints are first categorised according to the generalisation goal: improving the map legibility or preserving relevant visual characteristics. 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.
2007; Christophe 2009). Map specifications must describe the information content of the target map. g. roads and buildings), the scale (for paper maps) or scale range (for digital maps), and the 22 S. Balley et al. overall level of detail. g. for a map of car accidents: only important roads, plus roads involving a high number of accidents). This specification element provides clues as to the choice of source data, the feature selection and the generalisation process. g. g. cycling facilities follow roads).
Abstracting Geographic Information in a Data Rich World: Methodologies and Applications of Map Generalisation by Dirk Burghardt, Cécile Duchêne, William Mackaness