基于大语言模型和社交媒体数据的城市公园公众活动丰富度测度——以上海为例  

Measurement of the Public Activity Richness of Urban Park Based on Large Language Models and Social Media Data:A Case Study of Shanghai

作　　者：仲玥 刘雨轩 叶宇 ZHONG Yue;LIU Yuxuan;YE Yu(the Key Laboratory of Ecology and Energy-Saving Study of Dense Habitat,Ministry of Education;Built Environment Technology Center in the College of Architecture and Urban Planning(CAUP),Tongji University)

机构地区：[1]高密度人居环境与生态节能教育部重点实验室 [2]同济大学建筑与城市规划学院 [3]同济大学建成环境技术中心

出　　处：《风景园林》2024年第9期34-41,共8页Landscape Architecture

基　　金：国家自然科学基金面上项目“基于多源数据和深度学习的公共空间品质评价模型与设计支持研究”(编号52078343);上海市基础研究特区计划“基于多模态知识增强大模型的城市风貌特色塑造”。

摘　　要：【目的】基于社交媒体数据的公园研究已成为热点。然而,既有研究依赖单模态数据和自然语言处理(natural language processing,NLP)技术,研究结果的精确度有待提升。随着大语言模型(large language models,LLM)的发展,分析社交媒体数据可实现更精确的城市公园公众活动丰富度解析。【方法】先利用LLM解析包含文本、图像和视频的多模态社交媒体数据,再运用聚类算法探究用户的情感倾向和活动丰富度,生成活动热力图,构建公园公众活动丰富度的量化方法。【结果】以传统问卷方法为参照标准,对比分析发现基于多模态数据的LLM分析法的准确性远优于单模态数据分析法,证实了研究方法的有效性。并将LLM分析法应用于上海外环内的20个城市公园,构建出大规模、高精度的公园公众活动丰富度的全景测度方法。【结论】创新性地利用LLM和多模态社交媒体数据分析城市公园公众活动丰富度,有利于推动人工智能在城市研究领域的学术发展和应用。[Objective]Urban parks are one of the most vital carriers of public services.Public perception and usage of urban parks can significantly impact their management and planning.In recent years,social media data has emerged as a critical source for understanding public interaction within urban spaces,making park analysis based on social media a research hotspot.However,the current research typically focuses on single-mode data analysis(such as text or image),and relies on traditional machine learning and natural language processing(NLP)techniques,which may limit the comprehensiveness and accuracy of research results.Advancements in artificial intelligence,particularly in large language models(LLM),have made significant breakthroughs in language understanding,reasoning,and image recognition,providing the technical foundation for using multi-modal social media data,including image and text,to analyze the rich urban park activities.This research aims to explore the methods for quantitative analysis of multi-modal social media big data to build a more accurate measurement system for park public activity richness.[Methods]Taking Shanghai Gongqing National Forest Park,the most popular and discussed urban park on the social media platform“Xiaohongshu”,as an example,this research employs a combination of classical questionnaire methods,LLM analysis,and traditional classical analysis methods.First,through the design and implementation of a semantic analysis questionnaire,multiple uniform surveys are conducted at the 43 most popular spots in Gongqing National Forest Park to understand public activity preferences and perceptions of different scenes.Descriptive statistical methods are used for analyzing activity intention data.Respondents are presented with images of various park scenes and their locations,and are required to detail their expected activities such as walking,running,or picnicking.The semantic differential(SD)method is used to analyze site perception data.Through statistical analysis of respondents'ratings on

关 键 词：风景园林 城市公园 公众活动丰富度 大语言模型 多模态数据 社交媒体数据 

分 类 号：TU985[建筑科学—城市规划与设计] TU986