A community life circle identification method and device based on resident travel behavior

By using grid-based processing and cluster analysis based on residents' travel behavior, the boundaries of community living circles are scientifically delineated, solving the problems of insufficient dynamic adaptability and spatial accuracy in existing technologies, and achieving accurate delineation and continuity of community living circles.

CN122819960APending Publication Date: 2026-09-25BEIJING URBAN PLANNING & DESIGN INST +2
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Patent Information

Application Number
CN202611330148.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-08-31
Publication Date
2026-09-25

AI Technical Summary

Technical Problem

Existing community living circle planning technologies are insufficient in terms of dynamic adaptability and spatial accuracy, making it difficult to accurately delineate the boundaries of community living circles, and the delineation is inconsistent.

Method used

Based on residents' travel behavior data, by acquiring pre-defined regional grid data and residential land data within the built-up area, residential land grids are extracted, grid sharing index is calculated, clustering algorithms are used to generate living circle grid clusters, and through correction and fusion, a coherent living circle area is obtained, which is finally matched with urban plots to determine the community living circle.

Benefits of technology

It improves the dynamic adaptability and spatial accuracy of community living circle delineation, ensures the relevance and consistency of residents' activities within the living circle, and adapts the boundaries to the urban form, thus solving the problem of inconsistent delineation.

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Abstract

The application relates to the technical field of urban intelligent planning, and provides a community life circle identification method and device based on resident travel behaviors, which comprises the following steps: based on obtained preset regional grid data and built-up area range residential land data, extracting residential land grids and determining grid sharing indexes between the residential land grids; using a preset clustering algorithm to perform clustering analysis on the grid sharing indexes between the residential land grids, obtaining a plurality of life circle grid groups sharing activity distribution; for each life circle grid group, according to the number of life circle surface domains in the grid group and the distance between the surface domains, splitting the life circle grid group to obtain an initial community life circle; matching and fusing the initial community life circle with urban plots to obtain a final community life circle. The life circle boundary generated by the application is more in line with the actual activity space distribution characteristics of residents, and the spatial precision and dynamic adaptability of the life circle are improved.
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