Incorporating ideas of structure and meaning in interactive multi scale mapping environments

ABSTRACT

Web based, slippy, scalable maps are common place. Interacting with such digital maps at varying levels of detail is key to interpretation, and exploration of different geographies. The process of abstraction remains key to the immediate and successful interpretation of their many structures and geographical associations found at any given scale. Meaning is derived from such recognisable structures and map generalisation plays a critical role in communicating an entity's most characteristic and salient qualities. But what are these structures? How (and why) do they change over scale? Why are such questions relevant to automated mapping? In this paper we reflect on the value of perceptual studies and reconsider the context in which map generalisation now takes place. We review developments in pattern recognition techniques and the role played by machine learning techniques in identifying high level structures in abstracted maps. The benefits of their application include derivation of ontological descriptions of landscape, identification and preservation of salient landmarks across scales. We argue that a 'structuralist based approach' provides a more meaningful basis for measuring success and achieving more meaningful outputs. Ultimately the ambition is greater levels of automation in map generalisation, particularly in the context of web based solutions.

ABSTRAITE

Les cartes web multi-échelles et interactives sont maintenant le support cartographique le plus utilisé au monde. Ces cartes numériques permettent d'interagir avec des cartes à plusieurs échelles et donc d'interpréter et explorer des phénomènes géographiques à plusieurs échelles. Le processus d'abstraction reste la clé de l'interprétation immédiate et réussie des nombreuses structures géographiques et relations spatiales que l'on trouve à différentes échelles. La compréhension, le sens que l'on donne à la carte provient de notre capacité à décoder ces structures, et la généralisation cartographique joue un rôle fondamental dans la communication des propriétés les plus saillantes d'une entité géographique. Mais que sont exactement ces structures ? Comment, et pourquoi changent-elles à travers les échelles ? Et pourquoi ces questions sont importantes quand on s'intéresse à la conception de cartes multi-échelles ? Cet article propose un état de l'art sur des études perceptuelles en cartographie qui permettent de repenser le rôle de la généralisation cartographique. L'article discute les récents développements des techniques de reconnaissances de formes et structures, et le rôle joué par l'apprentissage machine pour détecter des structures abstraites dans des cartes topographiques. L'intérêt de ces techniques de reconnaissance inclut notamment la dérivation de descriptions ontologiques d'un paysage et l'identification de points de repères saillants dans des cartes multi-échelles. Nous pensons qu'une approche ‘structuraliste' de la généralisation permet de mieux mesurer la qualité d’une carte multi-échelle, et cela pourrait permette également, dans le futur, une meilleure automatisation des processus de généralisation cartographique.

KEYWORDS:

• Map generalisation
• web based mapping
• structuralism
• pattern recognition
• machine learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by H2020 European Research Council [grant number 101003012].

Notes on contributors

Guillaume Touya

Guillaume Touya is a senior researcher, at IGN France (the French mapping agency) and Univ Gustave Eiffel. He holds a PhD and habilitation in GI science from Paris-Est University. His research interests focus on automated cartography, map generalization and volunteered geographic information. He is particularly interested in research approaches to multi-scale cartography that mix automated cartography, spatial cognition and human-computer interaction issues. He is the principal investigator of the recent LostInZoom project, funded by the Europe Research Council (ERC). He is the chair of the ICA (International Cartographic Association) commission on map generalization and multiple representation.

Quentin Potié

Quentin Potié is a PhD student at IGN France (the French mapping agency) and Univ Gustave Eiffel. He is interested in cartography, spatial cognition, and machine learning. He holds a Master in GI Science from AgroParisTech.

William A. Mackaness

William A. Mackaness is a Senior Lecturer in the School of Geo Sciences at the University of Edinburgh. His core expertise lies in map generalisation and automated cartography, in particular multiscale map generalisation and geovisualisation.