Since 1999, the expansion of the West Nile virus (WNV) epizooty has led public health authorities to build and operate surveillance systems in North America. These systems are very useful to collect data, but cannot be used to forecast the probable spread of the virus in coming years. Such forecasts, if proven reliable, would permit preventive measures to be put into place at the appropriate level of expected risk and at the appropriate time. It is within this context that the Multi-Agent GeoSimulation approach has been selected to develop a system that simulates the interactions of populations of mosquitoes and birds over space and time in relation to the spread and transmission of WNV. This simulation takes place in a virtual mapping environment representing a large administrative territory (e.g. province, state) and carried out under various climate scenarios in order to simulate the effects of vector control measures such as larviciding at scales of 1/20 000 or smaller.

A landform is a physical feature of the terrain with its own recognisable shape. Its definition is often qualitative and inherently vague. Hence, landforms are difficult to formalise in a logical model that can be implemented. We propose for that purpose a framework where these qualitative and vague definitions are transformed successively during different phases to yield an implementable data structure. Our main consideration is that landforms are characterised by salient elements as perceived by users. Hence, a common prototype based on an object-oriented approach is defined that shall apply to all landforms. This framework shall facilitate the definition of conceptual models for other landforms and relies on the use of ontology design patterns to express common elements and structures. The model is illustrated on examples from the literature, showing that existing works undertaken separately can be developed under a common framework.

Largement répandue dans les régions tempérées de l’hémisphère nord, la borréliose de Lyme est la maladie à transmission vectorielle la plus fréquente sur l’ensemble de son aire de distribution. Depuis sa mise en évidence chez l’Homme en 1977, elle est considérée comme une maladie émergente, car son incidence a rapidement augmenté au cours des dernières décennies. En revenant préalablement sur la notion d’émergence, nous avons discuté du caractère émergent de cette affection. Il nous paraît alors difficile de vouloir généraliser l’émergence tant il existe une variété de situations et de contextes (géographique, biologique, socio-économique). Actuellement, la borréliose de Lyme semble poser une nouvelle problématique de santé publique dans les espaces fortement urbanisés, et ce, en raison d’une forte concentration humaine et de la capacité polarisante des grands massifs forestiers périurbains. Cette crainte d’une augmentation du risque, qui est en cours d’évaluation, est exprimée de façon importante en région Île-de-France, et en particulier autour de la forêt de Sénart. Une étape de l’anticipation du risque de borréliose de Lyme est alors conduite par la nécessité de prendre en considération les expositions et les facteurs de risque. Ainsi la mise en évidence de contacts entre les itinéraires de déplacement des usagers en forêt et les espaces et environnements dits à « risque » constitue de fait une voie privilégiée. L’intérêt étant de spatialiser ce contact et de réfléchir à comment minimiser le risque sur le plan paysager.

Map generalization is a complex task that sometimes requires human intervention. In order to support such a process on the fly, we propose a generalization approach based on self-generalizing objects (SGOs) that encapsulate geometric patterns (forms common to several cartographic features), generalization algorithms, and spatial integrity constraints. During a database enrichment process, an SGO is created and associated with a cartographic feature or a group of features. Each SGO created is then transformed into a software agent (SGO agent) in a multi-agent on-the-fly map-generalization system. SGO agents are equipped with behaviours that enable them to coordinate the generalization process. This article presents the concept of the SGO and two prototypes developed to support this approach: a prototype for the creation of SGOs and another for the on-the-fly map generalization (which uses the created SGOs).