Mapping the Invisible

Readers might of noticed that recently we have been exploring the use of street view images to explore cities or how we can utilize geosocial media to understand the form of function of cities, but one thing we have not explored is the role of smell and how it shapes peoples perceptions of urban spaces. However, in a new paper recently published in the Annals of the American Association of Geographers with Qingqing Chen, Ate Poorthuis we do just that. The paper is entitled "Mapping the Invisible: Decoding Perceived Urban Smells Through Geosocial Media in New York City" In the paper we use text mining techniques to tease out smell related information from over 56 million geolocated tweets which are then assigned to specific small categories (e.g., nature, food, waste) resulting in a new smellscape map for New York city. 

If this sounds of interest, below you can read the abstract to our paper, see our workflow and resulting smellscape map. While the the analysis steps, along with the smell dictionary used, are documented in the research code compendium at  https://figshare.com/s/8418d47cdc5c539b78ab. Finally at the bottom of the page, you can find the full reference and a link to the paper. 

Abstract:

Smells can shape people’s perceptions of urban spaces, influencing how individuals relate themselves to the environment both physically and emotionally. Although the urban environment has long been conceived as a multisensory experience, research has mainly focused on the visual dimension, leaving smell largely understudied. This article aims to construct a flexible and efficient bottom-up framework for capturing and classifying perceived urban smells from individuals based on geosocial media data, thus, increasing our understanding of this relatively neglected sensory dimension in urban studies. We take New York City as a case study and decode perceived smells by teasing out specific smell-related indicator words through text mining techniques from a historical set of geosocial media data (i.e., Twitter/X). The data set consists of more than 56 million data points sent by more than 3.2 million users. The results demonstrate that this approach, which combines quantitative analysis with qualitative insights, can not only reveal “hidden” places with clear spatial smell patterns, but also capture elusive smells that might otherwise be overlooked. By making perceived smells measurable and visible, we can gain a more nuanced understanding of smellscapes and people’s sensory experiences within the urban environment. Overall, we hope our study opens up new possibilities for understanding urban spaces through an olfactory lens and, more broadly, multisensory urban experience research. 

Key Words: geosocial media, multisensory urban experiences, network analysis, New York City, smellscape, text mining, urban smells.

A framework of deriving perceived smells.

An overview of research workflow.

An overview of the six dominant overlapping smells across New York City using the weaving mapping method. The weaving map uses the concept of strands to represent attributes. Each strand here represents one specific smell category, with the intensity of the color changing based on the density of that smell category within each neighborhood (i.e., grid cells).

Full Reference: 

Chen, Q., Poorthuis A. and Crooks, A.T., (2025), Mapping the Invisible: Decoding Perceived Urban Smells through Geosocial Media in New York City, Annals of the American Association of Geographers. Available at https://doi.org/10.1080/24694452.2025.2485233. (pdf)