New Paper: Exploring Fear in Urban Environments

In the past we have written about how we have used social media to study a plethora of topics with respect the the form and function of cities among many other things. But one thing we have not explored is fear and more specifically fear of crime and how this can be mined through geosocial media. 

This has now changed with a new paper entitled "Exploring Fear in Urban Environments: Place and Space Analysis of Social Media Data" which has recently been published in Applied Geography.  In this paper, Ying Zhou and myself extract fear related posts from social media and examine the places and spaces where people experience fear, as well as the factors that contribute to it in New York City. 

We do this by utilizing Natural Language Processing (NLP) techniques for sentiment and text analysis, including a RoBERTa-based emotion classification model and the BERTopic model for topic modeling. The former model narrowed the raw data to those with the dominant emotion of fear, and the latter analyzed space- and place-related features that contribute to the fear sentiment. Then, the selected social media data were analyzed using spatial clustering methods (i.e., Hotspot Analysis (Getis-Ord Gi*) and Local Moran’s I) and compared with urban crime data for weekly trends and spatial patterns. As such the paper has the following research objectives:

1. exploring places where people expressed fear through social media; 
2. making comparisons between safety-related fear and crime from the perspective of both time and space; 
3. extracting urban environmental and social features that lead to fear.

If this sounds of interest, and you wish to find out more with respect to our findings, below you can read the abstract to the paper, see some of the figures which describe our research methodology and results while at the bottom of the post you can find a link to the paper itself. Finally the code we utilized in the paper can be found at https://osf.io/y7xfc/overview.

Abstract:

One goal of creating livable cities is to enhance public safety. While previous research in urban studies has focused on correlations between physical environments and crime, it has typically relied on criminal statistics. However, fear of crime is an emotional response to perceived risks rather than a direct reflection of crime levels, so it cannot be analyzed solely by crime data. Additionally, urban planning today has gradually shifted its focus from a top-down to a bottom-up approach, making it essential to understand and foster spaces where residents feel safe. This research examines the spaces and places where people experience fear, as well as the factors that contribute to it, in New York City. We utilized social media data to gather people’s expressions of the city and identified posts expressing fear emotion using the RoBERTa-based model and a rule-based classifier. Then, the selected social media data and crime were compared temporally by weekly trends and spatially by clustering methods (i.e., Hotspot Analysis (Getis-Ord Gi*) and Local Moran’s I). The results show that their temporal and spatial patterns partially have limited alignment. To delve into the origins of fear, we extend our analysis by adopting BERTopic to identify topics and summarize them into themes (e.g., places, transportation, people, others) to understand the bottom-up emergence of fear, thereby informing a people-centered approach to research on urban issues. 

Keywords: Social media; Natural language processing; Sentiment analysis; Urban environment.

Methodology framework.

An example of textual analysis on fear-related tweets: from machine-generated topics to human-interpreted themes describing fear in NYC.

Weekly trends comparison between safety-related fear and violent crime.

Clustering features analysis by the method of hotspot analysis (Getis-Ord Gi∗).

Full Reference: 

Zhou, Y. and Crooks, A.T. (2026), Exploring Fear in Urban Environments: Place and Space Analysis of Social Media Data, Applied Geography, 192: 104051 (pdf)

New Paper: Connecting senses: The cross-modal associations between smell and vision in understanding urban environments

In a previous post we wrote about how one can mine social media to uncover smells and how they shapes peoples perceptions of urban spaces. Building off this work we (Qingqing Chen, Ate Poorthuis and myself) have a new paper entitled "Connecting senses: The cross-modal associations between smell and vision in understanding urban environments" published in Geographical Analysis. 

In this paper we build upon this but at the same time move away from social media to explore the relationship between smell and vision. We do this utilizing street view imagery from New York City, in this case we are using Mapillary. A subset of these images were labeled with pre-defined smell categories (e.g.,  ‘Nature’, ‘Food’, ‘Transportation & Fuel’) in order to develop a deep learning smell classifier capable of classifying perceived smells from the images. We then use  advanced image processing techniques (e.g., ResNet50, VGG16, Inception-V3, MobileNet and EfficientNet.) to extract visual cues from the street view imagery to predict smells. 

If this sounds of interest, below we provide an abstract to the paper along with some of the figures which show our workflow and accompanying results. At the bottom of the post you can see the full referece to the paper, while at https://figshare.com/s/94cdec3b14c206e6d225 we provide our code to allow others to replicate or extend this to other areas. 

Abstract:   

Smell is a crucial yet understudied sensory dimension in urban environments, bridging tangible elements (e.g., exhaust, flowers) with intangible impacts on emotions, social interactions and well-being. While geographical and urban research increasingly acknowledges multisensory experiences, much of geospatial analysis still emphasized the visual dimension. This research advances spatial thinking by examining cross-modal associations between smell and vision in urban environments. Specifically, we utilize advanced image processing techniques to extract visual cues from street view imagery (i.e., Mapillary) and apply causal analysis to examine their effects on smell expectations recorded from participants. The results show that visual cues can predict smells in straightforward urban settings (e.g., parks or less densely populated areas). However, in complex urban environments, the predictive power of visual cues diminishes as diverse and overlapping scents obscure specific smells, even in visually distinct areas. These findings underscore the importance of a multisensory approach in urban analytics, enhancing our understanding of the interplay between sensory experiences and informing urban design strategies that integrate multiple senses to create engaging and inclusive environments. This is especially important for individuals with sensory impairments, such as anosmia or visual impairments, who rely on other senses to compensate for their perception of urban environments. 

Keywords: Smell and Vision; Cross-modal Associations; Multisensory Experiences; Image Processing; Street View Imagery (SVI)

An overview of research workflow.

The visual feature extraction framework based on key patterns identified from questionnaires.

Spatial distribution of images with different perceived smells and example participant notes identifying potential smell sources responsible for perceiving the dominant smells.

Identified important features for smell categories that are relatively indirect to be inferred from visual cues. Left: Identified important features; Right: Examples of feature  contribution in individual images.

Full Referece: 

Chen, Q. Poorthuis, A. and Crooks A.T. (2026), Connecting senses: The cross-modal associations between smell and vision in understanding urban environments, Geographical Analysis, 58 (3): e70046.. Available at https://doi.org/10.1111/gean.70046. (pdf)