Reverse Geocoding with Mercator Projection for Fast POI Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing real user monitoring (RUM) systems for mobile devices face computational inefficiencies in determining geographical proximity due to the reliance on latitude-longitude coordinates and the great-circle distance formula, which becomes computationally expensive when processing large numbers of requests.
Innovation Solution
A customized reverse geolocation system that transforms latitude-longitude coordinates to Cartesian coordinates using Mercator projections, constructs a supplemental R-tree data structure for efficient point-of-interest searches, and identifies nearest points-of-interest using Euclidean distances, enabling scalable and real-time location computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the great-circle distance formula is used to compute distance between mobile device location and points-of-interest, then measurement precision is improved, but computational complexity increases and processing speed decreases
Solution Approach 1:
The patent transforms the coordinate system from spherical (latitude-longitude) to Cartesian (x-y coordinates) by changing the mathematical parameters. This allows replacing the computationally expensive great-circle distance formula with the simpler Euclidean distance formula while maintaining sufficient accuracy for determining geographical proximity in real-time RUM applications.
Solution Approach 2:
The patent uses a simplified approximation approach (Euclidean distance in Cartesian coordinates) that is computationally cheaper than the exact great-circle distance formula. This disposable approximation method provides sufficient accuracy for the application's needs without the computational overhead of trigonometric functions.
2Measurement precision
If latitude-longitude coordinates are used for reverse geocoding, then measurement precision is improved, but computational overhead increases
Solution Approach 1:
The system changes the coordinate representation from spherical coordinates (latitude-longitude) to Cartesian coordinates (x-y). This parameter transformation reduces the computational energy required for distance calculations while maintaining the ability to accurately determine geographical proximity through the transformed coordinate system.
3Measurement precision
If multiple distance computations are performed in real-time for RUM, then measurement precision is improved, but device complexity and processing time increase
Solution Approach 1:
By transforming coordinates to Cartesian form and using Euclidean distance, the patent simplifies the computational system required for real-time multiple distance computations. This reduces the complexity of the computation system while maintaining the ability to perform accurate proximity determinations for multiple points-of-interest simultaneously.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides fast and scalable identification of geographically proximal points-of-interest for mobile devices, reducing computational overhead and enabling efficient real-time monitoring with minimal accuracy loss.
Implementation Method 1
A coordinate transformation unit is configured to transform the co-ordinates from the first format to a second format including a pair of Cartesian co-ordinates (x-y co-ordinates), e.g., via a Mercator projection.
Data Source
AI summary
Described herein is a framework for identifying one or more points-of-interest that are geographically proximal to a geographical location of the mobile device. Geographical location information of a mobile device is transformed from a first format to a second format by performing a Mercator projection of the geographical location information in the first format. Using a point-of-interest search memory structure, one or more points-of-interest are identified that are geographically proximal to a geographical location of the mobile device, wherein the point-of-interest search memory structure includes location information for a plurality of points-of-interest. Information indicative of the one or more points-of-interest are transmitted to an analytical server to enable the analytical server to perform analysis related to the mobile device.