Background: Climate change has a significant impact on population health. Population vulnerabilities depend on several determinants of different types, including biological, psychological, environmental, social and economic ones. Surveillance of climate-related health vulnerabilities must take into account these different factors, their interdependence, as well as their inherent spatial and temporal aspects on several scales, for informed analyses. Currently used technology includes commercial off-the-shelf Geographic Information Systems (GIS) and Database Management Systems with spatial extensions. It has been widely recognized that such OLTP (On-Line Transaction Processing) systems were not designed to support complex, multi-temporal and multiscale analysis as required above. On-Line Analytical Processing (OLAP) is central to the field known as BI (Business Intelligence), a key field for such decision-support systems. In the last few years, we have seen a few projects that combine OLAP and GIS to improve spatio-temporal analysis and geographic knowledge discovery. This has given rise to SOLAP (Spatial OLAP) and a new research area. This paper presents how SOLAP and climate-related health vulnerability data were investigated and combined to facilitate surveillance.
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 mapgeneralization 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). RésuméLa généralisation cartographique est un processus complexe qui demande parfois l'intervention humaine. Afin de supporter un tel processus à la volée, nous proposons une approche de généralisation qui se base sur les SGO (objets autogénéralisants ou self-generalizing objects) qui encapsulent à la fois des patrons géométriques (qui sont des formes communes à plusieurs objets cartographiques), des algorithmes de généralisation et des contraintes d'intégrité spatiales. Lors d'un processus d'enrichissement de la base de données, un SGO est créé et associé à chaque objet ou groupe d'objets de la carte. Les SGO créés sont ensuite automatiquement transformés en agents logiciels (agents SGO) dans un système multi-agent de généralisation à la volée. Les agents SGO sont dotés de comportements qui leurs permettent de coordonner le processus de généralisation. Dans cet article, nous présenterons le concept des SGO et les prototypes (un prototype pour la création et l'enrichissement des SGO et un autre pour la généralisation à la volée, utilisant les SGO créés) développés pour supporter cette approche.Mots clés : objets auto-généralisants (SGO), la généralisation cartographique à la volée, patron géométrique, enrichissement des bases de données cartographica (volume 43, issue 3), pp. 155-173
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