Consensus or Confusion: Cartographers' Knowledge of Generalization

Rieger, Monika Katerine; Coulson, Michael

doi:10.3138/m6h4-1006-6422-h744

Cited by 14 publications

(7 citation statements)

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“…Deep knowledge is more important and much more complex to automate and is used by cartographers when superficial knowledge does not suffice. Rieger and Coulson (1993) carried out a survey among a group of cartographers performing interactive generalisation and found that the classification of generalisation operators differs depending on the specific cartographer. Additionally, they discovered that a consensus on such a classification does not exist.…”

Section: Literature Reviewmentioning

confidence: 99%

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Challenges for Automated Generalisation at European Mapping Agencies: A Qualitative and Quantitative Analysis

Foerster¹,

Stoter²,

Kraak³

2010

The Cartographic Journal

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The automation of generalisation is an important issue at National Mapping Agencies (NMAs) to reduce data production costs and to improve data maintenance. This paper presents the challenges for automated generalisation at European NMAs integrating a qualitative and quantitative analysis. The qualitative analysis focuses on the current strategies for automated generalisation at NMAs. The quantitative analysis extends these findings and measures the status of automated generalisation functionality at NMAs using the required and missing generalisation operators as indicators. The results are interesting for the research community, the software vendors and NMAs to streamline their efforts to accomplish full automation of generalisation processes.

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Section: Literature Reviewmentioning

confidence: 99%

“…When two cartographers are given the same generalisation rules for the same area, they will come to different results (Rieger and Coulson, 1993). Exceptions are common in the generalisation process, and there may be more than one ideal generalisation solution.…”

Section: Necessary Research To Improve Current Generalisation Practicementioning

confidence: 99%

Challenges for Automated Generalisation at European Mapping Agencies: A Qualitative and Quantitative Analysis

Foerster¹,

Stoter²,

Kraak³

2010

The Cartographic Journal

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“…This knowledge can be acquired thanks to domain experts -but this raises the problem of collecting and formalising it (Rieger and Coulson 1993, Weibel et al 1995, Kilpelinen 2000) -a problem classically known as 'the bottleneck of knowledge acquisition' (Feigenbaum 1977). One approach for solving this dilemma is the application of Machine Learning Techniques.…”

Section: Related Workmentioning

confidence: 99%

Automatic revision of rules used to guide the generalisation process in systems based on a trial and error strategy

Taillandier

Duchêne

Drogoul

2011

International Journal of Geographical Information Science

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Automating the generalisation process, a major issue for national mapping agencies, is extremely complex. Several works have proposed to deal with this complexity using a trial and error strategy. The performance of systems based on such a strategy is directly dependent on the quality of the control knowledge (i.e. heuristics) used to guide the trials. Unfortunately, most of the time, the definition and updation of knowledge is a fastidious task. In this context, automatic knowledge revision can not only improve the performance of the generalisation, but also allow it to automatically adapt to various usages and evolve when new elements are introduced. In this article, an offline knowledge revision approach is proposed, based on a logging of the system and on the analysis of outcoming logs. This approach is dedicated to the revision of control knowledge expressed by production rules. We have implemented and tested this approach for the automated generalisation of groups of buildings within a generalisation model called AGENT, from initial data that reference a scale of approximately 1:15,000 compared with the target map's scale of 1:50,000. The results show that our approach improves the quality of the control knowledge and thus the performance of the system. Moreover, the approach proposed is generic and can be applied to other systems based on a trial and error strategy, dedicated to generalisation or not.

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“…Unfortunately, no consensus has yet been reached on an all-encompassing set of operators. Even worse, authors may use different definitions for the same term or use different terms for the same definition, as a recent study by Rieger and Coulson (1993) has shown.…”

Section: A Closer Look At Generalization Operatorsmentioning

confidence: 99%

Generalization of spatial data: Principles and selected algorithms

Weibel

1997

Algorithmic Foundations of Geographic Information Systems

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Consensus or Confusion: Cartographers' Knowledge of Generalization

Abstract: health sciences, history THE CANADIAN HISTORICAL REVIEW utpjournals.press/chr Offering a comprehensive analysis on the events that have shaped Canada, CHR publishes articles that examine Canadian history from both a multicultural and multidisciplinary perspective.

Cited by 14 publications

References 0 publications

Challenges for Automated Generalisation at European Mapping Agencies: A Qualitative and Quantitative Analysis

Challenges for Automated Generalisation at European Mapping Agencies: A Qualitative and Quantitative Analysis

Automatic revision of rules used to guide the generalisation process in systems based on a trial and error strategy

Generalization of spatial data: Principles and selected algorithms

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