Spatial-Temporal Trail Comparison for Demographic Profiling
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Solution Overview
Problem
Current location-based services lack the ability to associate historical location records of individuals with demographic data, preventing the detection of similarities between users based on location history and demographic affiliations.
Innovation Solution
Systems and methods for associating spatial-temporal data points with demographic characteristics, creating semantic trails that allow for the derivation of individual profiles without prior user profiling, enabling the comparison of these trails to determine group affiliations and demographic attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location-based services process spatial-temporal data to provide location-aware content delivery, then the relevance of media content to user context is improved, but the ability to detect similarities between users and perform demographic analysis is lost
Solution Approach 1:
The patent segments spatial-temporal data into discrete location points with timestamps, associating each point with demographic characteristics. This segmentation allows the system to process location data while preserving demographic information for similarity detection and profiling purposes.
Solution Approach 2:
The patent introduces semantic coordinates as an intermediary layer between raw spatial-temporal data and demographic analysis. These semantic coordinates serve as a bridge that enables both precise location tracking and demographic information extraction without losing either type of data.
2Productivity
If systems are built for specific sensor types and purposes, then the focused analysis capability is improved, but the ability to perform general location analytics and user similarity detection is reduced
Solution Approach 1:
The patent creates a universal location analytics system that can process data from multiple sensor types (GPS, cellular towers, WiFi) and perform multiple functions including location tracking, demographic analysis, and user similarity detection. This multi-functional approach eliminates the need for separate specialized systems while maintaining analysis efficiency.
Solution Approach 2:
The patent changes the parameters of location data processing by introducing semantic coordinates and demographic characteristics as additional dimensions. This allows the system to adapt to different analysis requirements while maintaining efficient processing through standardized data structures and comparison algorithms.
3Measurement precision
If historical location records are analyzed to determine user location and time, then the accuracy of location-based profiling is improved, but the complexity of data processing and comparison increases
Solution Approach 1:
The patent creates simplified copies of complex spatial-temporal data by generating semantic trails that capture essential location patterns and demographic characteristics. These semantic trails serve as compressed representations that maintain profiling accuracy while reducing data processing complexity through standardized comparison formats.
Data Source
AI summary
Systems and computer-implemented methods are provided for comparing, associating and deriving associations between two or more spatial temporal data trails. One or more spatial-temporal data trails comprising one or more places are received at a processor. Each place is identified by a spatial temporal data point. And each spatial-temporal data trail is associated with an individual. The similarity between pairs of places is determined to establish one or more groups of places or one or more groups of individuals. Similarity and/groups can be determined based on demographics associated with the place or individual.