Mean-Variance Analysis of the Performance of Spatial Ordering Methods

Geographical Information Systems (GIS) involve the manipulation of large spatial data sets, and the performance of these systems is often determined by how these data sets are organized on secondary storage (disk). This paper describes a simulation study investigating the performance of two non-recursive spatial clustering methodsÐ the Inverted Naive and the Spiral methodsÐ in extensive detail and comparing them with the Hilbert fractal method that has been shown in previous studies to outperform other recursive clustering methods. The paper highlights the importance of analysing the sample variance when evaluating the relative performance of various spatial ordering methods. The clustering performance of the methods is examined in terms of both the mean and variance values of the number of clusters (runs of consecutive disk blocks) that must be accessed to retrieve a query region of a given size and orientation. The results show that, for a blocking factor of 1, the mean values for ...

• Type: Journal
• Year: 1998
• Where: GIS
• Authors: Akhil Kumar, Waleed A. Muhanna, Raymond A. Patterson
• Keywords: Automated Reasoning, Data Sets, GIS 1998, Inverted Naive, Performance