A formula for computing the number of quadtree node fragments created by a shift
Pattern Recognition Letters
Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
Efficient secondary memory processing of window queries on spatial data
Information Sciences—Informatics and Computer Science: An International Journal
Time and space efficient secondary memory representation of quadtrees
Information Systems
Analysis of the n-Dimensional Quadtree Decomposition for Arbitrary Hyperrectangles
IEEE Transactions on Knowledge and Data Engineering
PROBE Spatial Data Modeling and Query Processing in an Image Database Application
IEEE Transactions on Software Engineering
An Exact Closed-Form Formula for d-Dimensional Quadtree Decomposition of Arbitrary Hyperrectangles
IEEE Transactions on Knowledge and Data Engineering
Incremental Processing of Continual Range Queries over Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Information Sciences: an International Journal
Efficiently managing large-scale raster species distribution data in PostgreSQL
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
CudaGIS: report on the design and realization of a massive data parallel GIS on GPUs
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
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Abstract--Decomposing a query window into maximal quadtree blocks is a fundamental problem in quadtree-based spatial database. Recently, Proietti presented the first optimal algorithm for solving this problem. Given a query window of size n_1 \times n_2, Proietti's algorithm takes O(n_l) time, where n_l = max(n_1, n_2). Based on a strip-splitting approach, this paper presents a newoptimal algorithm for solving the same problem. Experimental results reveal that our proposed algorithm is quite competitive with Proietti's algorithm.