Spatial OLAP and Map Generalization: Model and Algebra

Spatial OLAP and Map Generalization: Model and Algebra

Sandro Bimonte (TSCF, CEMAGREF, France), Michela Bertolotto (University College Dublin, Ireland), Jérôme Gensel (Equipe STEAMER, France) and Omar Boussaid (University of Lyon 2, France)
Copyright: © 2012 |Pages: 28
DOI: 10.4018/jdwm.2012010102
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Abstract

Map generalization can be used as a central component of Spatial Decision Support Systems to provide a simplified and more readable cartographic visualization of geographic information. Indeed, it supports the user mental process for discovering important and unknown geospatial relations, trends and patterns. Spatial OLAP (SOLAP) integrates spatial data into OLAP and data warehouse systems. SOLAP models and tools are based on the concepts of spatial dimensions and measures that represent the axes and the subjects of the spatio-multidimensional analysis. Although powerful under some respect, current SOLAP models cannot support map generalization capabilities. This paper provides the first effort to integrate Map Generalization and OLAP. Firstly the authors define all modeling and querying requirements to do this integration, and then present a SOLAP model and algebra that support map generalization concepts. The approach extends SOLAP spatial hierarchies introducing multi-association relationships, supports imprecise measures, and it takes into account spatial dimensions constraints generated by multiple map generalization hierarchies.
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1. Introduction

Map generalization is a process that aims at producing simplified maps at different scales or levels of detail through a set of operators. Map generalization is critical in the spatial decision making process since it allows users to focus on relevant aspects of geographic information ignoring unimportant details. This facilitates the discovery of unknown spatial and thematic relationships and patterns (Vagenont, 2001). Thanks to map generalization operations decision makers can zoom in and-or zoom out into data, filtering geographic information during analysis processes (MacEachren et al., 2004; Cecconi, 2003). Therefore, map generalization can be useful in Spatial Decision Support Systems such as Spatial OLAP.

Spatial OLAP (SOLAP) tools organize information according to the spatio-multidimensional model (Gomez et al., 2009). SOLAP enables the analysis of numerical and spatial data according to several dimensions, which are organized into thematic and spatial hierarchies. This technology is applied in several application domains (Marketos et al., 2008) (e.g., environmental risk, health, etc.).

Integration of map generalization into SOLAP models could improve the analysis capabilities of spatio-multidimensional operators and also greatly improve the visual component of SOLAP tools (Bédard et al., 2002) by allowing adaptive zoom in/out operations (Cecconi, 2003) on spatial dimensions e.g., adjustment of maps representing spatial dimensions, its contents and the symbolization to target scale in consequence of a zooming operation. Unfortunately, most existing SOLAP models (Ahmed et al., 2006; Damiani et al., 2006; Fidalgo et al., 2004; Glorio et al., 2008; Gomez et al., 2009; Jensen et al., 2004; Malinoswky et al., 2008; Pourrabas, 2001; Sampaio et al., 2006; Raffaetà et al., 2011), and consequently SOLAP tools (Bimonte et al., 2010; Raffaetà et al., 2011), do not integrate map generalization on complex hierarchies generated by map generalization operators and integrity constraints which define rules to control the multidimensional exploration process.

The contributions of this work are the following.

  • It represents the first effort that investigates querying issues in spatial data warehouses integrating map generalization.

  • A spatio-multidimensional model, extending (Bimonte et al., 2008), which support previously defined requirements is presented.

  • A new and ad-hoc SOLAP algebra is provided that support a constrained exploration of a spatial data warehouse.

  • An implementation using a ROLAP architecture is presented that avoids new complex inter spatial data warehouses navigation operators.

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