Resident Area Prediction Using Area Relationship Models
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Solution Overview
Problem
Current methods for predicting a user's resident area, such as manual questionnaire surveys, are inefficient and yield low accuracy, limiting their practical application in fields like epidemic control and urban planning.
Innovation Solution
A data-driven resident area prediction method using an area relationship model that incorporates resident area data, visiting POI data, and basic attribute information, leveraging deep learning and multi-task learning to determine time-sequence relationships and predict user resident areas accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual questionnaire surveys are used to predict user resident area, then implementation is simple, but prediction efficiency and accuracy are low
Solution Approach 1:
The patent replaces manual questionnaire surveys with an automated computer-based prediction system that processes electronic data. The system uses processors to automatically analyze resident area data, POI visiting data, and area relationship models to predict user resident areas, eliminating the need for manual data collection and analysis while significantly improving prediction efficiency and accuracy.
2Measurement precision
If manual questionnaire surveys are used to predict user resident area, then implementation is simple, but prediction accuracy is low
Solution Approach 1:
The patent replaces manual questionnaire surveys with an automated computer-based prediction system that processes electronic data. The system uses processors to automatically analyze resident area data, POI visiting data, and area relationship models to predict user resident areas, eliminating the need for manual data collection and analysis while significantly improving prediction prediction accuracy.
Solution Approach 2:
The patent introduces an area relationship model as an intermediary component that captures spatial relationships between different geographic areas. This model serves as a mediator that processes raw data and provides structured information for prediction, improving accuracy by incorporating geographic context without requiring complex manual analysis.
3Productivity
If automated data processing is used to predict user resident area, then prediction efficiency and accuracy are improved, but data privacy protection requirements increase
Solution Approach 1:
The patent introduces an area relationship model as an intermediary that processes geographic data without requiring direct access to sensitive personal information. The model aggregates and anonymizes data at the area level rather than individual level, enabling efficient prediction while reducing privacy risks through data abstraction and aggregation.
Data Source
AI summary
This disclosure discloses a resident area prediction method, apparatus, device and storage medium, involving artificial intelligence technology, big data, deep learning and multi-task learning. The specific implementation plan is: acquiring a resident area data of a target user, and the resident area data including the resident area of the target user and the corresponding resident time; obtaining an association relationship between the resident areas of the target user by inputting the resident area data into an area relationship model, and the area relationship model is used to reflect a position relationship between the areas; determining a time-sequence relationship between the areas visited by the target user, according to the association relationship, the resident time and the visiting POI data; predicting a target resident area of the target user, according to the time-sequence relationship and the basic attribute information of the target user.