文章信息：Daniele Quercia, Rossano Schifanella, Luca Maria Aiello, & Kate McLean. (2015). Smelly Maps: The Digital Life of Urban Smellscapes. Proceedings of the International AAAI Conference on Web and Social Media: 9(1), Article 1. https://doi.org/10.1609/icwsm.v9i1.14621

整理人：杨文，2023级硕士生

整理时间：2024年7月26日

Abstract：Smell has a huge influence over how we perceive places. Despite its importance, smell has been crucially overlooked by urban planners and scientists alike, not least because it is difficult to record and analyze at scale. One of the authors of this paper has ventured out in the urban world and conducted “smellwalks” in a variety of cities: participants were exposed to a range of different smellscapes and asked to record their experiences. As a result, smell-related words have been collected and classified, creating the first dictionary for urban smell. Here we explore the possibility of using social media data to reliably map the smells of entire cities. To this end, for both Barcelona and London, we collect geogreferenced picture tags from Flickr and Instagram, and geo-referenced tweets from Twitter. We match those tags and tweets with the words in the smell dictionary. We find that smell-related words are best classified in ten categories. We also find that specific categories (e.g., industry, transport, cleaning) correlate with governmental air quality indicators, adding validity to our study.

摘要：气味对我们感知地方的方式有巨大的影响。尽管气味很重要，但城市规划者和科学家们都严重忽视了它，尤其是因为它很难大规模记录和分析。这篇论文的一位作者在乌兰巴托世界冒险，在多个城市进行了“嗅觉行走”：参与者接触了一系列不同的嗅觉景观，并被要求记录他们的经历。因此，与气味相关的单词被收集和分类，创建了第一本城市气味词典。在这里，我们探索了使用社交媒体数据可靠地绘制整个城市气味地图的可能性。为此，对于巴塞罗那和伦敦，我们从Flickr和Instagram收集地理参考图片标签，并从Twitter收集地理参考推文。我们将这些标签和推文与气味词典中的单词进行匹配。我们发现，与气味相关的单词最好分为十类。我们还发现，特定类别（如工业、运输、清洁）与政府空气质量指标相关，这增加了我们研究的有效性。

图 阿姆斯特丹smellwalk参与者手写注释的可视化

图 城市嗅觉景观分类学。顶级类别位于内圈；第二级类别（如果可用）位于外圈；单词的例子在最外圈。

图 伦敦街道上气味类别的成对相关性

图 气味相关标签强度热图

原文链接：https://ojs.aaai.org/index.php/ICWSM/article/view/14621