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2017-07-11 - Colloque/Article dans les actes avec comité de lecture - Anglais - 28 page(s)

Brison Valérie , Pirlot Marc , "Spatial decision models for comparing maps" in International Conference on Multiple Criteria Decision Making, Ottawa, Canada, 2017

  • Codes CREF : Modèles mathématiques d'aide à la décision (DI1151)
  • Unités de recherche UMONS : Mathématique et Recherche opérationnelle (F151)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut des Sciences et du Management des Risques (Risques)
  • Centres UMONS : Centre de Recherche en Technologie de l’Information (CRTI)

Abstract(s) :

(Anglais) Many decision problems occur in a geographic or environmental context. In this work, we address the issue of comparing maps. Imagine we have two maps representing the suitability of a region for a given use, but not at the same time. During the time period, the state of the territory has evolved. Our objective is to help a decision maker to determine whether the global state of the territory has improved or deteriorated during the time period. For this purpose, we have developed three models to help a decision maker expressing his/her preferences over such maps, and consequently help him/her evaluating, for example, the results of a policy applied to the territory under study. The first model we propose assumes that the only thing that matters is the proportion of the surface area assigned to each category of the maps assessment scale. The second model allows to consider some geographic aspects. For example, the fact that good zones are close to or far away from a watercourse or a village can have an importance. The third model allows to take contiguity into account. Indeed, the fact that good zones are grouped together or scattered over the map may matter. We established the precise conditions (axioms) under which these models can represent the decision maker's preference. We designed elicitation methods based on the models' axiomatic characterization. Our models can also be useful to compare several land-use scenarios as will be illustrated on the results of the ESNET (Ecosystem Services NETworks) project. This project, which we have collaborated on, aimed at assessing the effects of different environmental policies on the ecosystems of the Isère department (France). We also use this project to show that other interesting aggregation problems occur in geographic contexts. For example, starting from a map each pixel of which is evaluated on some scale, how can we aggregate these evaluations to assign a single one to each commune? Or, starting with several maps representing the evaluation of a territory w.r.t. several criteria, how can we aggregate these evaluations to produce a single map representing the overall state of the territory for some purpose?