Optimizing Range Queries with 2D Location Data

This project aims to optimize range queries on 2D location data, a critical task for location-based applications such as Uber, Lyft, and Google. We tackle the inefficiency of traditional spatial data structures like Quad-trees and R-Trees for range queries by employing Hilbert curves to map 2D coordinates onto a 1D line. This approach facilitates efficient range queries using B-Trees and Prefix-trees. The project involves implementing these methods, profiling performance, and comparing algorithmic efficiency for handling large-scale location data. The solution improves the performance of spatial queries, providing faster response times while minimizing computational overhead. This work contributes to the growing field of geospatial data management, particularly for applications that require quick proximity-based searches.