Indexing Non-Uniform Spatial Data

Non-uniformity in data extents is a general characteristic of spatial data. Indexing such non-uniform data using conventional spatial index structures such as R -trees is inefficient for two reasons: (1) The non-uniformity increases the likelihood of overlapping index entries, and, (2) clustering of non-uniform data is likely to index more dead space than clustering of uniform data. To reduce the impact of these anomalies, we propose a new scheme that promotes data objects to higher levels in tree-based index structures. We examine two criteria for promotion of data objects and evaluate their relative merits using an R -tree. In experiments on cartographic data, we observe that our promotion criteria yield upto 45% improvement in query performance