Toward satisfactory public accessibility: A crowdsourcing approach through online reviews to inclusive urban design

Lingyao Li, Songhua Hu, Yinpei Dai, Min Deng, Parisa Momeni, Gabriel Laverghetta, Lizhou Fan, Zihui Ma, Xi Wang, Siyuan Ma, Jay Ligatti, Libby Hemphill
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引用次数: 0

Abstract

As urban populations grow, the need for accessible urban design has become urgent. Traditional survey methods for assessing public perceptions of accessibility are often limited in scope. Crowdsourcing via online reviews offers a valuable alternative to understanding public perceptions, and advancements in large language models can facilitate their use. This study uses Google Maps reviews across the United States and fine-tunes Llama 3 model with the Low-Rank Adaptation technique to analyze public sentiment on accessibility. At the POI level, most categories -- restaurants, retail, hotels, and healthcare -- show negative sentiments. Socio-spatial analysis reveals that areas with higher proportions of white residents and greater socioeconomic status report more positive sentiment, while areas with more elderly, highly-educated residents exhibit more negative sentiment. Interestingly, no clear link is found between the presence of disabilities and public sentiments. Overall, this study highlights the potential of crowdsourcing for identifying accessibility challenges and providing insights for urban planners.
实现令人满意的公共无障碍环境:通过在线评论为包容性城市设计提供众包方法
随着城市人口的增长,对无障碍城市设计的需求日益迫切。评估公众对无障碍环境看法的传统调查方法往往范围有限。通过在线评论进行众包,为了解公众看法提供了一种有价值的替代方法,而大型语言模型的进步可以促进其使用。本研究使用谷歌地图在美国各地的评论,并利用低等级适应技术对 Llama 3 模型进行了微调,以分析公众对无障碍环境的看法。社会空间分析表明,白人居民比例较高、社会经济地位较高的地区报告了更多的积极情绪,而老年人和高学历居民较多的地区则表现出更多的消极情绪。总之,这项研究强调了众包在识别无障碍挑战和为城市规划者提供见解方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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