Yvan Bédard

Towards spatial data quality information analysis tools for experts assessing the fitness for use of spatial data

Geospatial data users increasingly face the need to assess how datasets fit an intended use. However, information describing data quality is typically difficult to access and understand. Therefore, data quality is often neglected by users, leading to risks of misuse. Understanding data quality is a complex task that may involve thousands of partially related metadata. For complex cases where heterogeneous datasets have to be integrated, there is a need for tools supporting data quality analysis. This paper presents the design of such a tool that can manage heterogeneous data quality information and provide functions to support expert users in the assessment of the fitness for use of a given dataset. Combining concepts from GIS and Business Intelligence, this approach provides interactive, multi‐granularity and context‐sensitive spatial data quality indicators that help experts to build and justify their opinions. A prototype called the Multidimensional User Manual is presented to illustrate this approach.

Multidimensional Management of Geospatial Data Quality Information for its Dynamic Use Within GIS

Metadata should help users to assess the quality (fitness for use) of geospatial data, thus reducing the risk of data misuse. However, metadata presents limitations and remain largely unused. There still exists a need to provide information to users about data quality in a more meaningful way. This research aims to dynamically communicate quality information to the users in a rapid and intuitive way in order to reduce user meta-uncertainty related to geospatial data quality, and then reduce the risks of data misuses. Such a solution requires a data model able to support heterogeneous data quality information at different levels of analysis. Using a multidimensional database approach, this paper proposes a conceptual framework named the Quality Information Management Model (QIMM) relying on quality dimensions and measures. This allows a user to easily and rapidly navigate into the quality information using a Spatial On-Line Analytical Processing (SOLAP) client-tied to its GIS application. QIMM potential is illustrated by examples, and then a prototype and ways to communicate data quality to users are explored.

Integrating gis components with knowledge discovery technology for environmental health decision support

This paper presents a new category of decision-support tools that builds on todays Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP) technologies to facilitate Geographic Knowledge Discovery (GKD). This new category, named Spatial OLAP (SOLAP), has been an R&D topic for about 5 years in a few university labs and is now being implemented by early adopters in different fields, including public health where it provides numerous advantages. In this paper, we present an example of a SOLAP application in the field of environmental health: the ICEM-SE project. After having presented this example, we describe the design of this system and explain how it provides fast and easy access to the detailed and aggregated data that are needed for GKD and decision-making in public health. The SOLAP concept is also described and a comparison is made with traditional GIS applications.

Thirty Years of Research on Spatial Data Quality: Achievements, Failures, and Opportunities

This article reflects on the past 30 years of academic research in the field of spatial data quality and tries to identify the main achievements, failures, and opportunities for future research. Most of this reflection results from a panel discussion that took place during the Sixth International Symposium on Spatial Data Quality (ISSDQ) in July 2009.

Modelling geospatial application databases using UML-based repositories aligned with international standards in geomatics

This paper presents the result of recent work on the use of geospatial repositories to store the conceptual content of object oriented application database schemas and dictionaries aligned with international standards in geographic information (ISO/TC 211 and OGC). According to software engineering and database concepts, a geospatial repository can be defined as a collection of (meta) data structured in a manner to provide information about the semantics, geometry, temporality, and the integrity constraint of data stored in a geospatial database. For the last 6 years, ISO/TC 211 and OGC have been developing standards about geographic information to enable access and interoperability of geographic information; parts of these standards impact directly on geospatial repositories. Our work demonstrates that it is possible to develop a geospatial repository aligned with these standards and to implement it in a UML-based visual modelling tool.

A multidimensional and multiversion structure for OLAP applications

When changes occur on data organization, conventional multidimensional structures are not adapted because dimensions are supposed to be static. In many cases, especially when time covered by the data warehouse is large, dimensions of the hypercube must be redesigned in order to integrate evolutions. We propose an approach allowing to track history but also to compare data, mapped into static structures. We define a conceptual model building a Mutiversion Fact Table from the Temporal Multidimensional Schema and we introduce the notion of temporal modes of representation corresponding to different ways to analyze data and their evolution.

Handling evolutions in multidimensional structures

Building multidimensional systems requires gathering data from heterogeneous sources throughout time. Then, data is integrated in multidimensional structures organized around several axes of analysis, or dimensions. But these analysis structures are likely to vary over time and the existing multidimensional models do not (or only partially) take these evolutions into account. Hence, a dilemma appears for the designer of data warehouses: either keeping trace of evolutions therefore limiting the capability of comparison for analysts, or mapping all data in a given version of the structure that entails alteration (or even loss) of data. We propose a novel approach that offers another alternative, allowing to track history but also to compare data, mapped into static structures. We define a conceptual model and provide possible logical adaptations to implement it on current commercial OLAP systems. At last, we present the global architecture that we used for our prototype.

Revisiting the Concept of Geospatial Data Interoperability within the Scope of Human Communication Processes

Geospatial data interoperability has been the target of major efforts by standardization bodies (e.g. OGC, ISO/TC 211) and the research community since the beginning of the 1990s. It is seen as a solution for sharing and integrating geospatial data, more specifically to solve the syntactic, schematic, and semantic as well as the spatial and temporal heterogeneities between various representations of real-world phenomena. A few models have been proposed to automatically overcome heterogeneity of geospatial data and, as a result, increase the interoperability of geospatial data. However, the addition of a conceptual framework of geospatial data interoperability would contribute to understanding geospatial data interoperability, the appreciation of where existing contributions specifically apply, and would foster new contributions. In this paper, we revisit the concept of geospatial data interoperability within the broader scope of human communication and cognition. Human communication appears to be a rich framework since humans interoperate more easily than computers do. Accordingly, we present a conceptual framework of geospatial data interoperability that is broader in scope than existing frameworks and supported by several practical examples. An ontology of geospatial data interoperability is also introduced in order to refine the description of the conceptual framework. In such a communication-based framework, the notions of concept,

Modeling Geospatial Databases with Plug-Ins for Visual Languages: A Pragmatic Approach and the Impacts of 16 Years of Research and Experimentations on Perceptory

Modeling geospatial databases for GIS applications has always posed several challenges for system analysts, developers and their clients. Numerous improvements to modeling formalisms have been proposed by the research community over the last 15 years, most remaining within academia. This paper presents generic extensions (called Plug-Ins for Visual Languages or PVL) to facilitate spatial and temporal modeling of databases. For the first time, we explain its intrinsic relationship with an extended repository and how it has been influenced by pragmatic lessons learned from real life projects. We describe how we use PVLs with UML and how 16 years of fundamental research, diverse experimentations and feedbacks from users over the world shaped our approach. The final section presents Perceptory, a free repository-based UML+ PVL CASE developed to improve geospatial database modeling.

Towards a SOLAP-based public participation GIS.

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.