Geolocation Accuracy via Logistic-Geohash Matching
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Solution Overview
Problem
Existing geolocation techniques are error-prone and unreliable, often failing to accurately identify the location of a user or entity, especially when devices are used in different locations and times, leading to incorrect service provisioning and reduced relevance in applications like advertising and security.
Innovation Solution
A method that collects and processes data messages to generate reliable geolocation data by mapping message geolocation to geographic areas, identifying statistical patterns, and matching them with real-world locations, using logistic-geohashes and geogroups to determine a representative point and distance, thereby selecting accurate geolocation pairs for improved precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional geolocation techniques are used to determine client location, then the system can obtain location data with minimal processing, but the location data is error-prone and unreliable
Solution Approach 1:
The system performs preliminary actions by collecting multiple geolocation data points from various messages before making a final location determination. It proactively gathers data from email messages, text messages, and social media posts, then processes this accumulated data to identify the most reliable location, rather than relying on a single potentially erroneous data point.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring geolocation data from multiple sources and using statistical analysis to evaluate the reliability of each data point. It feeds this reliability information back into the location determination process, adjusting the weight given to different data sources based on their proven accuracy and consistency.
2Measurement precision
If multiple geolocation data points are collected and processed to improve accuracy, then location reliability improves, but computational complexity and processing time increase
Solution Approach 1:
The system changes parameters by transforming raw geolocation data into standardized formats with associated reliability scores. It converts diverse data sources (email headers, message metadata, social media geotags) into a unified parameter structure that includes location coordinates, confidence levels, and data source identifiers, making the data more manageable and analyzable.
Solution Approach 2:
The system segments the geolocation determination process into distinct stages: data collection from multiple sources, initial filtering of obviously erroneous data, statistical analysis of remaining data points, and final location selection. This segmentation allows each stage to be optimized independently and reduces the overall computational burden.
3Reliability
If statistical patterns are analyzed to identify regular communication locations, then accurate user location can be determined, but processing time and computational resources increase
Solution Approach 1:
The system employs periodic action by analyzing geolocation data in batches rather than continuously processing every single data point in real-time. It collects geolocation information from multiple messages over a period, then performs statistical analysis at scheduled intervals to identify patterns and determine user location, reducing the frequency of intensive computations.
Solution Approach 2:
The system applies partial action by focusing statistical analysis only on data points that meet certain criteria, such as being from trusted sources or showing consistent patterns. It doesn't process every single geolocation data point equally, but rather selects a subset that is most likely to contain accurate location information, reducing overall processing requirements.
Data Source
AI summary
Reliable location data is found using messages with unreliable location data. A logistic-geohash is found for a plurality of messages. Records of are accessed data messages, each data message including a geographic locator and a sender-identifier. For each sender-identifier, one or more geogroups are determined. For each geogroup: determining, a representative point of the geogroup is found to represent the geogroup. Using the representative point of the geogroup, a geogroup-geohash is found. Pair of geogroup: logistic address are found based on a matching of at least some of the first plurality of digits and some of the second plurality of digits. For each pair of geogroup:logistic address, a distance is determined between the representative point of the geogroup and the representative point of the logistic address. Geogroup:logistic address pairs are selected. The selected geogroup:logistic address pairs are stored in memory as representing a logistic location of the sender-identifier.