Functional Zone Identification Using Two-Stage Clustering
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Solution Overview
Problem
Existing methods lack an efficient and accurate way to identify functional zones within a geographic region, which is crucial for understanding spatial structure and land usage, as they often rely on incomplete or inaccurate data sources and require human trajectory data that is difficult to obtain.
Innovation Solution
A computer system uses a two-stage clustering approach to determine functional zones by first identifying geoclusters based on position data and then clustering these geoclusters based on functional data using feature vectors, allowing for the classification of zones without relying on human trajectory data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human trajectory data is used to identify functional zones, then accuracy of functional zone identification can be improved, but data collection difficulty and complexity increase
Solution Approach 1:
The patent introduces an intermediary approach by using check-in data from mobile devices as a proxy for human trajectory data. Instead of directly collecting complex trajectory information, the system uses simplified check-in records that capture location and temporal information, which serves as a mediator to infer functional zones without requiring full trajectory data collection
Solution Approach 2:
The patent extracts only the essential components needed for functional zone identification from complex human trajectory data. By focusing on check-in events (location + time) rather than complete movement paths, the system extracts minimal sufficient data to achieve accurate functional zone classification while avoiding the complexity of collecting full trajectory information
2Measurement precision
If comprehensive data sources are used for functional zone identification, then accuracy can be improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the data processing into distinct stages: first aggregating check-in data to identify geoclusters based on spatial proximity, then using these geoclusters as inputs for functional zone classification. This segmentation allows the system to handle comprehensive data systematically in manageable steps, reducing overall system complexity while maintaining accuracy
Solution Approach 2:
The patent performs preliminary action by pre-aggregating check-in data into geoclusters before functional zone classification. This preliminary clustering of spatial data points simplifies subsequent processing by reducing the complexity of analyzing individual check-in records to identifying functional patterns at the zone level
Data Source
AI summary
A computer system may use machine learning to determine functional zones within a geographical region. The computer system may determine position data and functional data for locations within a geographical region. The computer system may use machine learning to determine functional zones with a two-stage clustering approach. During the first stage, the computer system may determine geoclusters by clustering locations based on the position data. During the second stage, the computer system may determine functional zones by clustering the geoclusters based on the functional data.