ABSTRACT
Geographical data warehouses contain data coming from multiple sources potentially collected at different times and using different techniques. One of the most important concerns about geographical data warehouses is the quality or reliability of the data
5.1 Introduction ....................................................................................................99 5.2 Fundamental Concepts of Spatial Data Quality and Uncertainty in a
Geographic Knowledge Discovery Context ................................................. 100 5.2.1 Geospatial Data Quality and Uncertainty ........................................ 101
5.3 Existing Approaches to Prevent Users from Spatial Data Misuses .............. 103 5.4 An Approach Based on Risk Management to Prevent Data Misuses
in Data Warehousing and GKD Contexts ..................................................... 104 5.5 Legal Issues Related to the Development of Spatial Datacubes ................... 108
5.5.1 Legal Criteria for Spatial Datacube Producers Related to the Internal Quality of Data .............................................................. 108
5.5.2 Legal Criteria for Spatial Datacube Producers Related to the External Quality of Data ............................................................ 109
5.5.3 Legal Criteria for Users of Spatial Datacubes .................................. 110 5.5.4 Legal Pertinence of a Risk-Management Approach ......................... 110
5.6 Conclusion .................................................................................................... 111 References .............................................................................................................. 111
used for knowledge discovery, decision making, and, nally, action. In fact, this is the ultimate objective aimed by using this type of database. On the other hand, with increasing maturity and the proliferation of data warehouses and related applications (e.g., OLAP, data mining, and dashboards), a recent survey indicated that for the second year in a row, data quality has become the rst concern for companies using these technologies (Knightsbridge 2006). Similarly, a recent survey of Canadian decision makers using spatial data has identied data quality as the third most important obstacle in increasing the use of spatial data (Environics Research Group 2006). Thus, while data quality has become the number one concern for users of nonspatial data warehouses, it is also recognized as an emerging issue for spatial data (Sonnen 2007, Sanderson 2007) and the quality of spatial datacubes is being investigated seriously within university laboratories. In this context, the concept of data quality is making its way into the realm of geographic knowledge discovery, leading us to think in terms of risks for the users, for the developers, and for the suppliers of data, especially in terms of prevention mechanisms and possible legal consequences.
