A Multidimensional Model for Exploratory Spatiotemporal Analysis
Zarine P. Kemp, Howard Lee · Kent Academic Repository (University of Kent) · 2000
Geographic phenomena often exhibit different characteristics depending on the scale of the observations. Hierarchical reasoning enables the understanding of scale and categorization effects in data analysis. This research focuses on the computational support that is required for reasoning about data at various levels and at multiple dimensions. The model proposed within this framework, enables researchers to obtain insights into information in data repositories by enabling access to a wide range of views of the data along dimensions relevant to the application domain. The framework is characterized by its focus on the multidimensional data cube as the logical model for spatiotemporal analysis. This logical data structure supports functionality that is crucial to exploratory analysis such as calculations and modeling across dimensions, through hierarchies, over temporal intervals and derivation of relevant subsets of the data. Data subsets are extracted by flexible operations for ‘slicing’ and ‘dicing’ through the multidimensional cube, ‘roll-up’ and ‘drill-down’ to enable aggregation at required levels of consolidation and ‘pivoting’ to view the data from different perspectives. The research challenges inherent in the analysis of spatiotemporal data have also been recognized in other application domains such as scientific and statistical databases where similar requirements arise for advanced classification structures, dynamic hierarchies and the need for dimension reduction of data through high level abstractions.