Cartographic generalization

This short paper gives a subjective view on cartographic generalization, its achievements in the past, and the challenges it faces in the future

Gradual Generalization of Nautical Chart Contours with a Cube B-Spline Snake Model

—B-spline snake methods have been used in cartographic generalization in the past decade, particularly in the generalization of navigational charts where this method yields good results with respect to the shoal-bias rules for generalization of chart contours. However, previous studies only show generalization results at particular generalization (or scale) levels, and the user can only see two conditions: before the generalization and after generalization, but nothing in between. This paper presents an improved method of using B-spline snakes for generalization in the context of nautical charts, where the generalization process is done gradually, and the user can see the complete process of the generalization

Deconstruction of fractals and its implications for cartographic education

The research reported here was designed for two reasons: firstly, to involve anyone with an interest in cartographic visualization to participate in eliciting cartographic knowledge and to provide them with the opportunity to contribute their practical knowledge and opinions; and secondly, to inform the design of algorithms for line generalization. In the past, there has been some resistance to such mining and codification of expert knowledge. However, many cartographers now welcome highly interactive computer graphics, computer mapping, and virtual reality systems as providing them with new opportunities for launching cartography into a new creative age. Despite nearly thirty years of research on line generalization algorithms, the available algorithms are somewhat simplistic. This research, undertaken under the auspices of the BCS Design Group, explored the behavioural tendencies of cartographers engaged in line filtering. The results show that a carefully contrived, even if obviously artificial, exercise on the deconstruction of lines into meaningless forms can prompt cartographers to observe, record, and discuss their own cognitive processing

Gradual Generalization of Nautical Chart Contours with a B-Spline Snake Method

B-spline snake methods have been used in cartographic generalization in the past decade, particularly in the generalization of nautical charts where these methods yield good results with respect to the shoal-bias rules for the generalization of chart contours. However, previous studies only show generalization results at particular generalization (or scale) levels, and show only two states of the algorithm: before and after generalization, but nothing in between. This thesis presents an improved method of using B-spline snakes and other auxiliary functions and workflows for generalization in the context of nautical charts which can generalize multiple nautical chart features from large scale to small scale without creating any invalid intermediate features that require special processing to resolve. This process allows users to generate charts at any intermediate scale without cartographic irregularities, and is capable of extension to include more specialized generalization operators

A computer science perspective on the bendsimplification algorithm

The primary aim of this study was to evaluate whether the use of bends provides a better basis than point elimination for research on line structuring. These investigations were undertaken using Arc/Info 7.1.1. Comparative experimental results suggest that the algorithm may not be as widely applicable as the much simpler geometric filters, such as the Douglas-Peucker or Visvalingam algorithms. The paper therefore provides a brief review of these three algorithms. A more detailed conceptual and empirical evaluation of the bendsimplification system follows, highlighting some problems with implementing the system in Arc/Info. The paper then questions the value of over-coupling model- and image-oriented generalization processes within the black-box bendsimplification system. It suggests the type of parameters which could enhance the utility and usability of the Bendsimplify option within the Arc/Info (and perhaps also within the ArcView) environment and provides some pointers for further research. With respect to the main aim of the research, the evidence suggests that bendsimplification is less useful for line segmentation than Visvalingam's algorithm. Further research is needed to assess the value of the iterative bend elimination operator within bendsimplification

Cartographic algorithms : problems of implementation and evaluation and the impact of digitising errors

CISRG discussion paper ;

Knowledge revision in systems based on an informed tree search strategy : application to cartographic generalisation

Many real world problems can be expressed as optimisation problems. Solving this kind of problems means to find, among all possible solutions, the one that maximises an evaluation function. One approach to solve this kind of problem is to use an informed search strategy. The principle of this kind of strategy is to use problem-specific knowledge beyond the definition of the problem itself to find solutions more efficiently than with an uninformed strategy. This kind of strategy demands to define problem-specific knowledge (heuristics). The efficiency and the effectiveness of systems based on it directly depend on the used knowledge quality. Unfortunately, acquiring and maintaining such knowledge can be fastidious. The objective of the work presented in this paper is to propose an automatic knowledge revision approach for systems based on an informed tree search strategy. Our approach consists in analysing the system execution logs and revising knowledge based on these logs by modelling the revision problem as a knowledge space exploration problem. We present an experiment we carried out in an application domain where informed search strategies are often used: cartographic generalisation.Comment: Knowledge Revision; Problem Solving; Informed Tree Search Strategy; Cartographic Generalisation., Paris : France (2008