Smelly Maps: The Digital Life of Urban Smellscapes

TL;DR

This paper introduces a novel approach to mapping urban smellscapes by creating a smell dictionary and analyzing social media data, revealing correlations with air quality indicators in cities.

Contribution

It develops the first urban smell dictionary and demonstrates how social media data can reliably map city smells, linking them to environmental quality.

Findings

Smell-related words cluster into ten categories.

Certain smell categories correlate with air quality data.

Social media tags can effectively map urban smellscapes.

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 geo-referenced 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.