Spatial Data Warehouse Modelling

Spatial Data Warehouse Modelling

Maria Luisa Damiani (Università di Milano, Italy and Ecole Polytechnique Fédérale, Switzerland) and Stefano Spaccapietra (Ecole Polytechnique Fédérale de Lausanne, Switzerland)
DOI: 10.4018/978-1-60566-232-9.ch014


This chapter is concerned with multidimensional data models for spatial data warehouses. Over the last few years different approaches have been proposed in the literature for modelling multidimensional data with geometric extent. Nevertheless, the definition of a comprehensive and formal data model is still a major research issue. The main contributions of the chapter are twofold: First, it draws a picture of the research area; second it introduces a novel spatial multidimensional data model for spatial objects with geometry (MuSD – multigranular spatial data warehouse). MuSD complies with current standards for spatial data modelling, augmented by data warehousing concepts such as spatial fact, spatial dimension and spatial measure. The novelty of the model is the representation of spatial measures at multiple levels of geometric granularity. Besides the representation concepts, the model includes a set of OLAP operators supporting the navigation across dimension and measure levels.
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A topic that over recent years has received growing attention from both academy and industry concerns the integration of spatial data management with multidimensional data analysis techniques. We refer to this technology as spatial data warehousing, and consider a spatial data warehouse to be a multidimensional database of spatial data. Following common practice, we use here the term spatial in the geographical sense, i.e., to denote data that includes the description of how objects and phenomena are located on the Earth. A large variety of data may be considered to be spatial, including: data for land use and socioeconomic analysis; digital imagery and geo-sensor data; location-based data acquired through GPS or other positioning devices; environmental phenomena. Such data are collected and possibly marketed by organizations such as public administrations, utilities and other private companies, environmental research centres and spatial data infrastructures. Spatial data warehousing has been recognized as a key technology in enabling the interactive analysis of spatial data sets for decision-making support (Rivest et al., 2001; Han et al., 2002). Application domains in which the technology can play an important role are, for example, those dealing with complex and worldwide phenomena such as homeland security, environmental monitoring and health safeguard. These applications pose challenging requirements for integration and usage of spatial data of different kinds, coverage and resolution, for which the spatial data warehouse technology may be extremely helpful.

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