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Social Perception and Design Support for Tourism and Leisure Districts Based on Multi-Source Data

Year: 2026
Type: article
Abstract: Against the backdrop of China’s urban renewal shift from incremental expansion to stock optimization, tourism and leisure streets face increasing demands for refined, experience-oriented regeneration, yet traditional survey methods fall short in capturing dynamic tourist perceptions at fine spatial scales. This study takes Enzhouyi Ancient Street as a case and integrates multi-source social media data (reviews, short videos, and image-text posts from Douyin, Xiaohongshu, Meituan, and Dianping) with Baidu POIs data, employing SnowNLP-based sentiment quantification, kernel density estimation, and spatial overlay analysis to examine the “space–behavior–sentiment” structure. Negative sentiments cluster at entrance/exit zones, overcrowded popular stalls, and areas with insufficient facilities. Overlay analysis identifies four spatial typologies—high-sentiment–high-density, low-sentiment–high-density, high-sentiment–low-density, and low-sentiment–low-density—each corresponding to distinct problems including circulation–capacity mismatch, discontinuous cultural narratives, underutilized cultural resources, and homogenized commercial formats. By systematically examining the structural characteristics of tourist perceptions and the spatial differentiation mechanisms underlying negative sentiments, the study further analyzes the tourist perception characteristics and development potential of the Enzhouyi tourism and leisure street district in Jiaozuo. It thereby promotes the sustainable transformation of the district from “style restoration” toward “experience enhancement”.
DOI: 10.3390/su182010362
Language: English
Subfield: Urban Studies
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7221504271
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