📄 Abstract
Locating a point within a partitioned two-dimensional space is a problem that appears simple at first but grows complicated once real, unevenly distributed data is involved. This paper implements the function quadrant_point_locator(), which classifies and retrieves points relative to a movable origin by sorting them into four quadrants and an axis list. There are several existing approaches to this kind of problem, such as point-in-polygon testing, kd-trees, uniform grids and quadtrees, and each carries its own trade-off between simplicity, adaptivity and update cost. quadrant_point_locator() is designed as a lighter alternative that still adapts reasonably well to clustered data without the bookkeeping of a full tree structure. This manuscript implements the function quadrant_point_locator(), which uses four buckets and an index map to classify, query, insert and delete points. It examines the execution of the algorithm with respect to its time complexity and space complexity, and reports a case study comparing it against a uniform grid and a linear scan on a synthetic, sensor-style dataset of two-dimensional points.
🏷️ Keywords
📚 How to Cite:
Cadet E Egan , IMPLEMENTATION OF quadrant_point_locator() ALGORITHM WHICH CLASSIFIES POINTS OF A TWO-DIMENSIONAL CARTESIAN PLANE INTO QUADRANTS. FURTHER EXAMINING THE TIME COMPLEXITY OF THE ALGORITHM AND ANALYSING THE EFFICACY OF THE ALGORITHM – A CASE STUDY , Volume 12 , Issue 8, August 2026, EPRA International Journal of Multidisciplinary Research (IJMR) , Pages: 498 - 501 ,