Discovery of Fine-grained Subjective Perception of Urban Blocks in User Comment Data

doi:10.3969/j.issn.1006-2475.2025.08.005

Abstract

Abstract: （1. School of Computer Science and Engineering， Shenyang Jianzhu University， Shenyang 110168， China；
2. Liaoning Provincial Key Laboratory of Big Data Management and Analysis for Urban Construction， Shenyang 110168， China；
3. Shenyang Branch of National Special Computer Engineering Technology Research Center， Shenyang 110168， China）
Abstract： As one of the important dimensions for evaluating urban construction and planning， the subjective perception of urban blocks will help to create a more humane and livable urban space. Based on the representation learning technology and combined with the content of user comments， this paper discovers the structural characteristics between the neighborhood and the subjective perception， and solves the problems of coarse granularity and lack of data in the existing neighborhood perception. Firstly， a fine-grained perception category system is proposed， which analyzes the perception dictionary and POI （Points of Interest） user comment data， analyzes the subject words by LDA and combined with hierarchical clustering to construct a fine-grained street perception category. Secondly， in order to solve the problem of incomplete data， a similarity-weighted k-nearest neighbor filling method is designed， which effectively supplementes the missing POI evaluation content through the information of large category， small category， and geographical location. Finally， the autoencoder is used to transform the neighborhood perception into potential feature vectors. The real dataset of Beijing is used to evaluate the grading and ranking of housing prices in the neighborhood， and the effectiveness of the proposed method is verified.

Key words: Key words： urban perception, fine-grain perception, missing value padding, representation learning, KNN

CLC Number:

中图分类号：TP391

References

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