Figure 3 - uploaded by Yvan Bédard
Content may be subject to copyright.
Three types of spatial dimensions from left to right: non-geometric, geometric (2 polygonal levels, 1 punctual level) and mixed (1 polygonal level and 1 punctual level).
Source publication
Geographic Knowledge Discovery (GKD) requires systems that support interactive exploration of data without being slowed down by the intricacies of a SQL-type query language and cryptic data structures. GKD requires to compare maps of different phenomena or epochs, to dig into these maps to obtain detailed information, to roll-up data for more globa...
Context in source publication
Citations
... Hence, the integration of geographic information in data warehouse has become increasingly an active research field. As introduced by [2], Spatial OLAP (SOLAP) is a visual platform built especially to support rapid and easy spatialtemporal analysis and exploration of data following a multidimensional approach comprised of aggregation levels available in cartographic displays as well as in tabular and diagram displays. Many research work have addressed several aspects of Spatial Data Warehouse (SDW): From design to implementation [3] [4]. ...
... In the literature, several DW design-method based approaches have been proposed [7], [8], [9], [10], [11], [12]. These research works, among others can be classified into three categories: (1) Supply-oriented methods [12], [7], [13], (2) Requirements-oriented methods [14], [10], and (3) Hybrid methods [15], [8], [9], [16], [17]. ...
Road accidents is a complex and dangerous phenomenon, which requires a deep analysis of a large amount of information available in several databases of the concerned national public and private institutions. To help on fighting against this phenomenon, we propose a design approach of a spatial data warehouse dedicated to OLAP and SOLAP road accident analysis. The design method is driven by the metadata that allows expressing the needs by taking into account the available data. Our approach can be classified as a supply-driven method; its nine steps cover the lifecycle of the design, from the sources metadata extraction to the physical model of the spatial data warehouse. To validate our approach, our developed spatial data warehouse is backed up with a decisional tool allowing OLAP and SOLAP analysis. We present some results of our experiments at an administrative district level scale.
... Such approaches are not novel, but we do not yet know how best to design coordinated geovisual environments in the context of geospatial big data. Recent work to define new types of data structures around spatial dimensions (Bédard, Proulx, Rivest, & Badard, 2006) and trajectories (Leonardi et al., 2013) could help support rapid interaction with new cartographic interfaces to big data. ...
Geospatial big data present a new set of challenges and opportunities for cartographic researchers in technical, methodological and artistic realms. New computational and technical paradigms for cartography are accompanying the rise of geospatial big data. Additionally, the art and science of cartography needs to focus its contemporary efforts on work that connects to outside disciplines and is grounded in problems that are important to humankind and its sustainability. Following the development of position papers and a collaborative workshop to craft consensus around key topics, this article presents a new cartographic research agenda focused on making maps that matter using geospatial big data. This agenda provides both longterm challenges that require significant attention and short-term opportunities that we believe could be addressed in more concentrated studies.
... The most of database applied in these approaches are based on the concept of On-Line Transactional Processing (OLTP). Despite the big influence of GIS with transactional database in the management of spatial data, this concept cannot offer the relevant information quickly and efficiently to decision-makers (Bédard et al., 2006;Bédard et al., 2001). Indeed, GIS cannot effectively be designed to support numerical and multidimensional data exploration. ...
... Many approaches and tools deal with the combination of OLAP and GIS. The most important work in this field is Spatial OLAP (SOLAP) provided by Geomatics Center of Laval University in Canada, the essential features of which are quick and easy spatio-temporal analysis, exploration of multidimensional data and also provides a set of visualization techniques, such as diagrams, tables and maps Bédard et al., 2003;Bédard et al., 2006). Another research axis proposed by Voss for combining the CubeView and Polaris to facilitate the observation of spatial patterns and temporal trends in huge volumes of data (Voss et al., 2004). ...
The location selection for Landfill of Industrial Wastes (LIW) is a very significant task in waste management studies, which has significant impacts on sustainable development of the region. Furthermore, the selection of the appropriate and efficient sites for LIWs is an important multi-criteria decision making problem. The present document suggests an OLAP/GIS-Fuzzy AHP-TOPSIS based methodology for evaluation and selection of best sites for LIWs. In this respect, the candidate locations are specified based on the combination of On-Line Analytical Processing and Geographic Information System (OLAP/GIS). The Fuzzy Analytical Hierarchy Process (Fuzzy-AHP), a multi-criteria decision-making method is applied to analyze the structure of the problem and obtain the weights of the qualitative and quantitative criteria, by incorporating the uncertainty values in decision-making. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is taken into account to assess and rank the alternative locations. Finally, a hypothetical application of the proposed approach is illustrated by a case study of location selection for LIWs in Morocco. The results show that the proposed methodology can successfully achieve the aim of this work.
... Some SOLAP tools have been developed to integrate OLAP and GIS functionalities Sampaio et al., 2006;Matias & Moura-Pires, 2007;Silva et al., 2008;Escribano et al., 2007). In Bédard et al. (2006), the authors present an OLAP-GIS integrated tool, which provides several types of cartographic, tabular and graphic displays that are accessible by different coordinated windows. The visual components are synchronized to form a uniquely flexible and interactive user interface. ...
Spatial OLAP (SOLAP) integrates spatial data into OLAP systems, and SOLAP models defne spatial dimensions while measuring spatio-multidimensional operators. In this paper, the author presents the concepts of geographic and complex measures that allow integrating geographic and complex information as subjects of analysis in spatial data warehouses. The concept of geographic measure extends the concept of spatial measure to the semantic component of geographic information. The concept of complex measure allows introducing complex data as subjects of multidimensional analysis. To reduce the gap in fexibility between spatial and multidimensional analysis, this paper proposes a symmetrical representation of measures and dimensions. Additionally, the author presents a Web-based SOLAP prototype, GeWOlap, that enriches existing SOLAP tools by effectively and easily supporting symmetrical geographic/complex measures and dimensions for modeling and visualization. To validate this approach, the simulated environmental data concerning the pollution of the Venice lagoon is used.
... Newell, 1990). More recently, SOLAP has been enriched with hypermedia and annotation (red-lining) functionalities (Bé dard et al., 2006). ...
In this paper, we describe how spatial on-line analytical processing (SOLAP), a specific category of business intelligence technology especially adapted to geospatial data, can help to improve the technological side of public participation GIS applications. Based on two simulated cases of realistic scenarios of a public audience, this paper aims at demonstrating the relevance of this SOLAP technology to support and improve the interactive access and analysis of multi-scale, multi-epoch geospatial information (and indirectly public involvement) for an environmental management PPGIS application.
... Ceci est une importante limitation car une analyse spatiale effective implique la comparaison du phénomène avec les éléments situés autours (e.g. routes, industries, villes, etc.) (Bédard et al., 2006). Donc, la capacité d'importer des informations spatiales à partir d'autres sources de données est fondamentale. ...
RÉSUMÉ. Les outils OLAP Spatial (SOLAP) existants n'intègrent pas encore complètement toutes les fonctionnalités de visualisation, d'interaction et d'analyse des SIG. Dans cet article, nous présentons l'implémentation d'un système SOLAP Web flexible, GoOlap, qui combine à la fois des fonctionnalités OLAP et des fonctionnalités évoluées de géovisualisation. Ce système intégre le geobrowser Google Earth avec le système OLAP Mondrian/JPivot. Google Earth enrichie les capacités d'analyse spatio-multidimensionnelles à travers la visualisation 3D, la « contextualisation » de l'application SOLAP et la personnalisation des affichages cartographiques. ABSTRACT. Spatial OLAP (SOLAP) systems do not completely integrate visualization, interaction and analysis functionalities of Geographic Information Systems. In this paper, we propose a flexible Web SOLAP tool, GoOlap, which combines OLAP and Geovisualization functionalities through the integration of the geobrowser Google Earth and the OLAP tool Mondrian/JPivot. Google Earth enriches spatio-multidimensional analysis capabilities with 3D visualizations, SOLAP applications contextualization and cartographic displays customization.
Spatial OLAP (SOLAP) integrates spatial data into OLAP systems, and SOLAP models define spatial dimensions while measuring spatio-multidimensional operators. In this paper, the author presents the concepts of geographic and complex measures that allow integrating geographic and complex information as subjects of analysis in spatial data warehouses. The concept of geographic measure extends the concept of spatial measure to the semantic component of geographic information. The concept of complex measure allows introducing complex data as subjects of multidimensional analysis. To reduce the gap in flexibility between spatial and multidimensional analysis, this paper proposes a symmetrical representation of measures and dimensions. Additionally, the author presents a Web-based SOLAP prototype, GeWOlap, that enriches existing SOLAP tools by effectively and easily supporting symmetrical geographic/complex measures and dimensions for modeling and visualization. To validate this approach, the simulated environmental data concerning the pollution of the Venice lagoon is used.
Spatial OLAP (SOLAP) systems are decision-support systems for the analysis of huge volumes of spatial data. Usually, SOLAP clients provide decision-makers with a set of graphical, tabular and cartographic displays to visualize warehoused spatial data. Geovisualization methods coupled with existing SOLAP systems are limited to interactive (multi) maps. However, a new kind of geovisualization method recently appears to provide summaries of geographic phenomena: the chorem-based methods. A chorem is theoretically defined as a schematized spatial representation, which eliminates any unnecessary details to the map comprehension. Therefore, in this paper we investigate the opportunity to integrate SOLAP and chorem systems in a unique decision-support system. We propose the ChoremOLAP system that enriches SOLAP maps with chorems. We apply our proposal to agricultural data analysis, since both chorems and SOLAP have been rarely used in this application domain. Using open data provided by the FAO, we show how ChoremOLAP is well adapted in the agricultural context.
The design of Spatial OLAP (SOLAP) applications consists of (i) Spatial Data Warehouse (SDW) model design and (ii) SOLAP visualization definition because a specific set of understandable and readable cartographic visualizations corresponds to a particular type of SOLAP query. Unfortunately few works investigate geovisualization issues in SOLAP systems and propose new methodologies to visualize spatio-temporal data, and no works investigate tools for readable SOLAP cartographic displays. Moreover, some works propose ad-hoc methodologies for DWs and SDWs exclusively based on data and user analysis requirements. Therefore, we present in this paper (i) a new geovisualization methodology for SOLAP queries that yields readable maps and (ii) a new prototyping design methodology for SOLAP applications that accounts for geovisualization requirements.
Modern data analysis deeply relies on computational visualization tools, specially when spatial data is involved. Important efforts in governmental and private agencies are looking for patterns and insights buried in dispersive, massive amounts of data (conventional, spatiotemporal, etc.). In Visual Analytics users must be empowered to analyze data from different perspectives, integrating, transforming, aggregating and deriving new representations of conventional as well as spatial data. However, a challenge for visual analysis tools is how to articulate such wide variety of data models and formats, specially when multiple representations of geographic elements are involved. A usual approach is to convert data to a database--e.g., a multirepresentation database--which centralizes and homogenizes them. This approach has restrictions when facing the dynamic and distributed model of the Web. In this paper we propose an on the fly and on demand multi-representation data integration and homogenization approach, named Lens, as an alternative that fits better with the Web. It combines a metamodel driven approach to transform data to a unifying multidimensional and multirepresentation model, with a middleware-based architecture for seamless and on-the-fly data access, tailored to Visual Analytics.
