Artificial Neural Network
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A* Algorithm Fastest-Path Computation
Absolute Positional Accuracy Positional Accuracy Improvement (PAI)
Abstract Features Feature Extraction, Abstract
Abstract Representation of Geographic Data
Definition Model generalization is used to derive a more simple and more easy to handle digital representation of geometric features [1]. It is being applied mainly by National Mapping Agencies to derive different levels of representations with less details of their topographic data sets, usually called Digital Landscape Models (DLM’s). Model generalization is also called geodatabase abstraction, as it relates to generating a more simple digital representation of geometric objects in a database, leading to a considerable data reduction. The simplification refers to both the thematic diversity and the geometric complexity of the objects. Among the well known map generalization operations the following subset is used for model generalization: selection, (re-)classification, aggregation, and area collapse. Sometimes, also the reduction in the number of points to represent a geometric feature is applied in the model generalization process, although this is mostly considered a problem of cartographic generalization. This is achieved by line generalization operations.
Feature Catalogue
Historical Background
Abstraction Hierarchies and Level of Detail
Abstraction of GeoDatabases M ONIKA S ESTER Institute of Cartography and Geoinformatics, Leibniz University of Hannover, Hannover, Germany
Synonyms Model generalization; Conceptual generalization of databases; Cartographic generalization; Geographic data reduction; Multiple resolution database
Generalization is a process that has been applied by human cartographers to generate small scale maps from detailed ones. The process is composed of a number of elementary operations that have to be applied in accordance with each other in order to achieve optimal results. The difficulty is the correct interplay and sequencing of the operations, which depends on the target scale, the type of objects involved as well as constraints these objects are embedded in (e. g., topological constraints, geometric and semantic context, . . . ). Generalization is always subjective and requires the expertise of a human cartographer [2]. In the digital era, attempts to automate generalization have lead to the differentiation between model generalization and cartographic generalization, where the operations of model generalization are considered to be easier to automate than those of cartographic generalization. After model generalization has been applied, the thematic and geometric granularity of the data set corresponds
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Abstraction of GeoDatabases
appropriately to the target scale. However, there might be some geometric conflicts remaining that are caused by applying signatures to the features as well as by imposing minimum distances between adjacent objects. These conflicts have to be solved by cartographic generalization procedures, among which typification and displacement are th
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