The spatial locality and a spatial indexing method by dynamic clustering in hypermap system

Abstract

The rapid accessibility of information is an important requirement of the hypermap systems or the geographical informations systems. But the massive volumes of data oblige us to store it in secondary memory which greatly slows down the access time. Thus the indexing technique, which determines the way of secondary memory access, is an essential point in hypermap systems. There are several researches on spatial indexing techniques, but due to the lack of comparative studies which must be based on the analysis of the nature of the spatial data, it is difficult to say that one is better than another.

In this paper, we introduce a parameter called hierarchical variance. It tells us the degree of spatial locality, which is an important characteristic of spatial data. There is a strong relationship between this parameter and the hit ratio. We compare the existing spatial indexing methods from this point of view, and propose a new spatial indexing method, which is also a variation of R-tree and is based on the spatial locality property by using the dynamic clustering method. This method reduces hierarchical variance, that is, it increases the hit ratio.