Urban Functional Group Discovery via Mobility Topic Modeling
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Solution Overview
Problem
Current methods for discovering functional groups in urban areas are insufficient as they primarily rely on points of interest (POIs) in isolation, failing to account for the compound functional nature of districts and interactions between sections, which limits their effectiveness in applications such as urban planning and business location selection.
Innovation Solution
A technique that segments an area into sections using mobility patterns and POIs, employing a topic model framework to infer function distributions and cluster sections based on similarity, thereby determining functional groups and estimating functionality intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If points of interest (POIs) are used in isolation to infer district functionality, then the analysis is simple and straightforward, but the accuracy and comprehensiveness of functional group discovery is insufficient
Solution Approach 1:
The patent combines multiple data sources (POIs, mobility patterns, check-in data) and multiple analysis methods (topic models, clustering algorithms, statistical analysis) into an integrated framework for discovering functional groups. This merging of previously isolated analysis approaches enables comprehensive functional group discovery that accurately captures the compound functional nature of urban districts.
2Adaptability or versatility
If only POI information is considered, then the data processing is simple and fast, but the ability to capture compound functional nature of districts is insufficient
Solution Approach 1:
The patent creates a composite analytical framework that integrates multiple types of urban data (POIs, mobility patterns, check-in records) similar to how composite materials combine different substances to achieve superior properties. This composite approach enables the system to capture the compound functional nature of districts by synthesizing information from diverse data sources, each contributing unique insights into district functionality.
3Productivity
If isolation-based POI analysis is used, then the computational resources required are minimal, but the effectiveness for urban planning and business location selection is limited
Solution Approach 1:
The patent segments the urban area into functional groups based on similarity in functional characteristics derived from multiple data sources. This segmentation approach organizes the complex data into meaningful units (functional groups) that can be directly applied to urban planning and business location selection, transforming large quantities of raw data into actionable insights about urban functionality.
Data Source
AI summary
Disclosed herein are techniques and systems for discovering functional groups in an area, such as an urban area. A process includes segmenting a map of the area into sections, and inferring, for each section, a distribution of functions according to a topic model framework which considers mobility patters of users and points of interest (POIs) in the section. The topic model framework regards the section as a document, each function as a topic, the mobility patterns as words, and a POI feature vector for the section as metadata. The process may further include clustering the sections based at least in part on a similarity of the distribution of functions between each of the sections to obtain functional groups, estimating a functionality intensity for each of the functional groups, and annotating each of the functional groups.