Abstract
Abstract. This paper presents a study that compares the three space partitioning and spatial indexing techniques, KD Tree, Quad KD Tree, and PR Tree. KD Tree is a data structure proposed by Bentley (Bentley and Friedman, 1979) that aims to cluster objects according to their spatial location. Quad KD Tree is a data structure proposed by Berezcky (Bereczky et al., 2014) that aims to partition objects using heuristic methods. Unlike Bereczky’s partitioning technique, a new partitioning technique is presented based on dividing objects according to space-driven, in the context of this study. PR Tree is a data structure proposed by Arge (Arge et al., 2008) that is an asymptotically optimal R-Tree variant, enables data-driven segmentation. This study mainly aimed to search and render big spatial data in real-time safety-critical avionics navigation map application. Such a real-time system needs to efficiently reach the required records inside a specific boundary. Performing range query during the runtime (such as finding the closest neighbors) is extremely important in performance. The most crucial purpose of these data structures is to reduce the number of comparisons to solve the range searching problem. With this study, the algorithms’ data structures are created and indexed, and worst-case analyses are made to cover the whole area to measure the range search performance. Also, these techniques’ performance is benchmarked according to elapsed time and memory usage. As a result of these experimental studies, Quad KD Tree outperformed in range search analysis over the other techniques, especially when the data set is massive and consists of different geometry types.
Highlights
With the increase in spatial data usage, spatial query arises as a fundamental problem in numerous applications with various geometric problems
The primary purpose of this study is to present a benchmark study of spatial indexing techniques covering an experimental analysis for use in real-time safety-critical avionics navigation map application
The amount of memory usage by these algorithms and the indexing time when run on the real dataset were presented and evaluated
Summary
With the increase in spatial data usage, spatial query arises as a fundamental problem in numerous applications with various geometric problems. Handling spatial queries in an effective manner, one needs access methods based on a data structure called an index are needed. The general idea of spatial indexing is to place spatial data in space or clusters stored in secondary storage. In the space-driven structure, objects are partitioned into rectangular cells, and cells are mapped concerning spatial associations (overlap or intersection). In the data-driven structures, the set of objects is partitioned and grouped according to the distribution of the objects. PR Tree is an example of this structure in this paper These methods primarily try to access spatial data as fast as possible. For this reason, these methods are called spatial access methods(Manolopoulos et al, 2000)
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