A city interest point homotopy pattern mining method and device

By utilizing topic models and network distance matrices for isomorphic pattern mining at the urban functional area scale, the errors caused by neglecting facility differences in existing technologies are resolved, achieving more accurate isomorphic pattern recognition for urban POIs.

CN116401470BActive Publication Date: 2026-01-02CHINA UNIV OF GEOSCIENCES (WUHAN)
0 Cites 0 Cited by

Patent Information

Application Number
CN202310168009.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-02-24
Publication Date
2026-01-02
Estimated Expiration
2043-02-24

AI Technical Summary

Technical Problem

Existing isomorphic pattern mining methods fail to consider factors such as facility size, management methods, and operating hours in urban facility research, leading to the neglect of instance differences within the same type of facility and potentially causing problems such as 'variable facets' and 'ecological fallacies'.

Method used

A topic model based on urban residents' movement trajectories is used to identify urban functional areas, and a network distance matrix is ​​used to mine co-location patterns between points of interest (POIs). Spatial neighborhood relationships are constructed through the DMR topic model and the network distance matrix to identify frequent co-location patterns.

Benefits of technology

It can more accurately reflect the actual situation of the city, reduce the amount of calculation, improve the accuracy and efficiency of isotope pattern mining, and identify heterogeneous patterns under different functional areas.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The present application relates to the technical field of city management, and particularly relates to a city interest point co-location pattern mining method and device. The present application uses a topic model to identify city functional areas based on the mobile trajectory of city residents; in the case of a given city road network, each POI is projected onto the road network, the distance between POIs is calculated by finding a shortest connection path, and the distance is stored in a distance matrix to establish a network distance matrix between city POIs; based on the network distance matrix between POIs in the city road network, co-location pattern mining of POIs is carried out within various functional areas to identify co-location patterns. Compared with traditional co-location pattern mining, the present application takes into account the existence of spatial heterogeneity, and co-location pattern mining is carried out on the premise of functional area identification, which more comprehensively and accurately reflects the actual situation.
Need to check novelty before this filing date? Find Prior Art