Location Type Identification via Time-Location Mapping
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Solution Overview
Problem
Existing location-based service technologies face challenges in accurately determining user geographical locations over time, as they rely on precise GPS data and lack a method to infer location types based on regular user activities, leading to inefficiencies in providing location-specific services.
Innovation Solution
A method and apparatus that utilize a time-location type mapping relationship table to calculate the probability of a user's location type by clustering geographical locations and defining probability values for each time interval and location type, allowing for the determination of the most likely location type based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS data is used to determine user geographical locations, then location accuracy is improved, but the system cannot infer location types based on user activity patterns
Solution Approach 1:
The system pre-establishes a time-location type mapping relationship table that stores the correspondence between time intervals, geographical locations, and location types. This preliminary preparation enables the system to quickly determine location types without requiring complex real-time analysis, thus resolving the contradiction between using simple GPS data and obtaining location type information.
Solution Approach 2:
The patent introduces a time-location type mapping relationship table as an intermediary between GPS location data and location type determination. This mapping table acts as a bridge that translates basic geographical coordinates into meaningful location types by incorporating time dimension and historical activity patterns, thereby recovering location type information that would otherwise be lost.
2Device complexity
If location type determination relies on precise GPS data only, then device complexity is reduced, but service relevance is worsened
Solution Approach 1:
The patent transforms the location determination problem from a two-dimensional spatial problem (latitude and longitude) to a three-dimensional problem by adding the time dimension. The time-location type mapping relationship table incorporates time intervals as a third dimension, enabling the system to distinguish location types (such as home, work, recreation) based on when the user visits specific locations, thereby improving service relevance without significantly increasing device complexity.
Solution Approach 2:
The system changes the parameters used for location determination from purely spatial coordinates to a combination of spatial coordinates and temporal parameters. By introducing time intervals and using the sum of probability values across different time periods, the system enriches the parameter set while maintaining relatively simple computational logic, thus improving service relevance without excessive complexity increase.
3Measurement precision
If historical location data is aggregated to determine location types, then location type identification accuracy is improved, but data processing time is increased
Solution Approach 1:
The system performs preliminary aggregation of historical location data by pre-establishing the time-location type mapping relationship table, which stores the sum of probability values for each location type across multiple time intervals. This preliminary processing of historical data enables fast real-time queries without requiring complex computations during actual location type determination, thus resolving the contradiction between accuracy and processing time.
Solution Approach 2:
The patent uses the sum of probability values across all time intervals for each location type, which may be considered an excessive approach as it processes more data than strictly necessary. However, this comprehensive aggregation ensures high accuracy in location type identification by considering all available historical evidence, while the pre-computed nature of the sum values keeps processing time acceptable.
Data Source
AI summary
Aspects of the disclosure provide a method of determining types of user geographical locations. A target geographical location corresponding to a plurality of uploading times can be obtained. At least one time-location type mapping relationship table can be obtained. Based on the obtained at least one time-location type mapping relationship table, for each of location types in the at least one time-relation type mapping relationship table, a sum of probability values of the respective location type can be calculated to obtain a degree that the target geographical location belongs to each of the location types. Each of the probability values corresponds to one of the uploading times of the target geographical location. Which of the location types corresponds to the target geographical location can be determined according to the degrees that the target geographical location belongs to each of the location types.