Multi-Criteria Geographic Analysis for Automated Cartographic Generalization

ABSTRACT

Cartographic generalization is a process similar to text summarization that transforms a map when scale is reduced. Cartographic generalization simplifies the map content while preserving as much as possible the initial characteristics and spatial relations of the map. The automation of this process requires a deep understanding of the context of each map feature, which involves different criteria such as the shape of the feature, the semantic nature of the feature, or the spatial patterns of its neighbouring features. This is why multiple criteria decision techniques can be relevant during the process. This paper proposes two use cases of cartographic generalization sub-tasks where multiple criteria decision techniques improve past techniques: the classification of urban building blocks and the ordering of the buildings to keep in the map as a priority. In both use cases, the proposed methods are experimented on large real cartographic datasets, and evaluated in comparison to alternative techniques.

KEYWORDS:

• Multiple criteria analysis
• cartography
• cartographic generalization
• ELECTRE
• PROMETHEE
• spatial analysis

Disclosure statement

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

Additional information

Notes on contributors

G. Touya

Guillaume Touya is vice-head of the LASTIG lab, at IGN France (the French mapping agency) and Université Gustave Eiffel. He is a senior researcher in the GEOVIS research team of LASTIG. 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 and human-computer interaction issues. He is the Chair of the ICA Commission on Map Generalization and Multiple Representation.