Until now, road network generalization has mainly been applied to the task of generalizing from one fixed source scale to another fixed target scale. These actions result in large differences in content and representation, e.g., a sudden change of the representation of road segments from areas to lines, which may confuse users. Therefore, we aim at the continuous generalization of a road network for the whole range, from the large scale, where roads are represented as areas, to mid-and small scales, where roads are represented progressively more frequently as lines. As a consequence of this process, there is an intermediate scale range where at the same time some roads will be represented as areas, while others will be represented as lines. We propose a new data model together with a specific data structure where for all map objects, a range of valid map scales is stored. This model is based on the integrated and explicit representation of: (1) a planar area partition; and (2) a linear road network. This enables the generalization process to include the knowledge and understanding of a linear network. This paper further discusses the actual generalization options and algorithms for populating this data structure with high quality vario-scale cartographic content.

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The Space-Scale Cube (SSC) model stores the result of a generalization process, that supports smooth scale transitions for map objects. The third dimension is used to describe geometrically the smooth transitions between objects at different levels of detail. Often-used map generalization operators fit in this SSC model. The 3D SSC model to derive 2D maps can be used in a mobile web client, where these days powerful graphics hardware is available. This article shows the steps needed for producing and disseminating SSC data with smooth transitions over the web. Firstly, we explain how SSC data can be obtained and subsequently be rendered by making effective use of the GPU. Secondly, we show how we organize data in chunks and how this 'chunked' data can be used for efficient communication between client and server. In the third place, we describe which operations can be used on the client side for deriving maps. Fourthly, the SSC also allows for (a) mixed abstraction slicing surfaces useful for highlighting specific regions by showing more detail and (b) near-intersection blending, which helps to prevent abrupt transitions while the slicing surface is in motion. Finally, we show how animated pan and zoom functionalities may be realized. A set of prototypes allows us to disseminate the data with smooth transitions on the web and in practice judge the effect of continuous generalization and animating the map image. RÉSUMÉLe modèle du Cube Echelle-Espace (SSC) stocke le résultat dun processus de généralisation qui permet des transitions d'échelle fluides pour les objets cartographiques. Les opérateurs standards de généralisation cartographique s'intègrent dans le modèle SSC. Le modèle 3D SSC pour dériver des cartes2D peut tre utilisé dans un client web mobile. Cet article montre les étapes nécessaires pour produire et distribuer des données SSC avec des transitions fluides sur le web. En premier, nous expliquons comment les données SSC peuvent tre obtenues et restituées par une utilisation efficace du processeur graphique (GPU). Puis nous montrons comment organiser les données en morceaux et comment ces morceaux' peuvent tre utilisés pour une ARTICLE HISTORY

ABSTRACT:Processing massive datasets which are not fitting in the main memory of computer is challenging. This is especially true in the case of map generalization, where the relationships between (nearby) features in the map must be considered. In our case, an automated map generalization process runs offline to produce a dataset suitable for visualizing at arbitrary map scale (vario-scale) and efficiently enabling smooth zoom user interactions over the web. Our solution to be able to generalize such large vector datasets is based on the idea of subdividing the workload according to the Fieldtree organization: a multi-level structure of space. It subdivides space regularly into fields (grid cells), at every level with shifted origin. Only features completely fitting within a field are processed. Due to the Fieldtree organization, features on the boundary at a given level will be contained completely in one of the fields of the higher levels. Every field that resides at the same level in the Fieldtree can be processed in parallel, which is advantageous for processing on multicore computer systems. We have tested our method with datasets with upto 880 thousand objects on a machine with 16 cores, resulting in a decrease of runtime with a factor 27 compared to a single sequential process run. This more than linear speed-up indicates also an interesting algorithmic side-effect of our approach.