Location Data Normalization for Cross-Platform Consistency
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Solution Overview
Problem
Inaccurate and inconsistent geocoding of location data across various platforms leads to inefficiencies and missed sales opportunities, as different third-party mapping applications may represent the same location differently, affecting sales and consumer recognition.
Innovation Solution
A system and methodology that imports primary location data, collects and compares it with secondary data from multiple sources, identifies discrepancies, and automatically or manually adjusts the primary data to align with a standardized format for accurate and consistent export to third-party platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If location data is collected from multiple third-party sources, then data coverage and completeness improve, but data consistency and accuracy deteriorate due to different formats and coordinate systems
Solution Approach 1:
The patent introduces a central data management system that acts as an intermediary between multiple third-party location data sources and the final application. This system collects data from various sources (Google, Bing, Yahoo, Foursquare, Yelp) in different formats, standardizes them through a common coordinate system and data structure, and then distributes the normalized data back to applications. The intermediary resolves format inconsistencies and coordinate system differences while maintaining data completeness from multiple sources.
Solution Approach 2:
The system transforms location data parameters by converting coordinates from different reference systems (latitude/longitude, UTM, state plane) into a unified coordinate system. It also transforms data formats, field structures, and representation methods across multiple third-party sources into a standardized format, enabling consistent data processing while preserving the quantitative information from diverse sources.
2Reliability
If location data is standardized to a single format, then data consistency improves, but adaptability to different platform requirements deteriorates
Solution Approach 1:
The patent segments the data standardization process into distinct functional layers: a core normalization layer that enforces consistent coordinate systems and data structures, and an adaptation layer that transforms the standardized data into platform-specific formats. This segmentation allows the system to maintain internal data consistency while externally adapting to diverse platform requirements such as different coordinate systems, field names, and data structures required by various mapping applications.
Solution Approach 2:
The system creates a universal standardized data format that serves as a common foundation for all location data. This universal format can then be adaptively transformed into multiple platform-specific formats, allowing a single standardized representation to serve multiple different applications and platforms with varying requirements.
3Measurement precision
If manual verification of location data is performed, then data accuracy improves, but processing time and operational complexity increase
Solution Approach 1:
The system implements automated self-verification mechanisms that compare location data across multiple third-party sources against each other and against the primary data set. The normalization process automatically identifies and resolves inconsistencies through algorithmic comparison and reconciliation, reducing the need for manual verification while maintaining high data accuracy through systematic cross-validation.
Data Source
AI summary
A system and associated methodology manages localization data. According to one embodiment of the present invention a primary set of data associated with one or more specific locations is imported and matched to a predefined format. Thereafter external, secondary, data associated with each of the localities listed in the primary data set is collected from a plurality of third party location service providers. With the collected secondary data matched to the same predefined format a comparison is made between the plurality of secondary data sets and the primary data set. Differences between the data sets are identified and the primary data set modified as necessary. Thereafter, normalized data from the modified primary data set is exported to the third party location service providers to enhance consistency and reliable of locational data.