A Descriptive and Illustrated Guide for Type Placement on Small Scale Maps

Wood, Clifford H.

doi:10.1179/000870400787320851

Cited by 6 publications

(7 citation statements)

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“…They arguably differ in their aesthetic appeal, which is not only influenced by their geometry, but also by the type and style of the background image. The few existing guidelines as described in Section 3 are valuable resources when assessing different options of external labelings, but they do not cover all types of labelings and are primarily based on practical experience [Woo00, Ric17]. There is a lack of formal empirical studies and readability results to confirm or adapt these guidelines.…”

Section: Discussionmentioning

confidence: 99%

External Labeling Techniques: A Taxonomy and Survey

Bekos¹,

Niedermann

Nöllenburg

2019

Computer Graphics Forum

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External labeling is frequently used for annotating features in graphical displays and visualizations, such as technical illustrations, anatomical drawings, or maps, with textual information. Such a labeling connects features within an illustration by thin leader lines with their labels, which are placed in the empty space surrounding the image. Over the last twenty years, a large body of literature in diverse areas of computer science has been published that investigates many different aspects, models, and algorithms for automatically placing external labels for a given set of features. This state‐of‐the‐art report introduces a first unified taxonomy for categorizing the different results in the literature and then presents a comprehensive survey of the state of the art, a sketch of the most relevant algorithmic techniques for external labeling algorithms, as well as a list of open research challenges in this multidisciplinary research field.

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Section: Discussionmentioning

confidence: 99%

External Labeling Techniques: A Taxonomy and Survey

Bekos¹,

Niedermann

Nöllenburg

2019

Computer Graphics Forum

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“…They are thus essential to assisting the user in interpreting the content of the map correctly. Imhof (1975, p.128) also stated that "[...] good form and placing of type makes a good map" (Ahn & Freeman, 1984;Wood, 2000).…”

Section: Towards Efficient Cartographic Displaysmentioning

confidence: 99%

Investigating the Effectiveness of an Efficient Label Placement Method Using Eye Movement Data

Ooms

Maeyer

Fack

et al. 2012

The Cartographic Journal

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“…Morrison (1980) estimated that manual lettering can take up to 50% of total map production time. Over the last four decades many attempts have been made to automate the process of map labelling; see the bibliography of papers on this topic maintained by Wolff and Strijk (2009). The main reason is to reduce the cost of manual label placement on the map.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Multi-criteria Model for High Cartographic Quality Point-Feature Label Placement

Rylov

Reimer

2014

Cartographica

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The lettering process, including assigning names to point features, is an essential part of map production. While there have been numerous and varied research efforts to automate point-feature label placement (PFLP), none of them seems to have taken into account the many well-established cartographic precepts for point-feature annotation used by human cartographers. As a result, current fully automated solutions are limited in their expressive power. The PFLP problem is still vital, therefore, and solving it is a compelling challenge. This article presents a comprehensive multi-criterion model that complies with almost all well-defined cartographic placement principles and requirements for PFLP, allowing for a significant increase in toponym density without affecting legibility. The proposed model, expressed as a qualityevaluation function, can be used by any mathematical optimization algorithm to resolve the automated label-placement problem. Through an application of the proposed model tested on volunteered geographic (VGI) data and the creation of sample parameter settings, the article illustrates that a high level of cartographic quality for PFLP can be achieved through the integrated approach, comparable to the lettering produced by an expert cartographer. RÉSUMÉLe processus de lettrage, y compris l'attribution de noms à des caractéristiques ponctuelles, fait partie essentielle de la production de cartes. De nombreux efforts de recherche variés ont visé à automatiser la mise en place d'étiquettes de caractéristiques ponctuelles (MPECP), mais aucun de ces efforts ne semble avoir tenu compte des nombreux préceptes cartographiques bien établis portant sur l'annotation des caractéristiques ponctuelles utilisées par les cartographes humains. Il s'ensuit que les solutions entièrement automatisées courantes ont un pouvoir expressif limité. Le problème de la MPECP demeure donc vital et sa solution constitue un défi difficile à relever. Cet article présente un modèle multicritériel détaillé qui se conforme à presque tous les principes bien définis de mise en place cartographique et aux exigences de la MPECP, ce qui permet d'augmenter considérablement la densité toponymique sans que cela ait d'effet sur la lisibilité. Le modèle proposé, exprimé sous forme d'une fonction qualité-évaluation, peut être utilisé par n'importe quel algorithme d'optimisation mathématique pour régler le problème de la mise en place automatisée des étiquettes. En appliquant le modèle proposé mis à l'essai sur des données géographiques fournies volontairement et en créant des contextes paramétriques témoins, l'article démontre qu'il est possible d'obtenir une grande qualité cartographique pour la MPECP grâce à l'approche intégrée, comparable au lettrage produit par un cartographe expert.Mots clés : cartographie automatisée, mise en place automatisée des étiquettes, MPECP, optimisation combinatoire, cartographie SIG

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A Descriptive and Illustrated Guide for Type Placement on Small Scale Maps

Cited by 6 publications

References 0 publications

External Labeling Techniques: A Taxonomy and Survey

External Labeling Techniques: A Taxonomy and Survey

Investigating the Effectiveness of an Efficient Label Placement Method Using Eye Movement Data

A Comprehensive Multi-criteria Model for High Cartographic Quality Point-Feature Label Placement

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