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Exploring fear in urban environments: Place and space analysis of social media data.

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Title: Exploring fear in urban environments: Place and space analysis of social media data.
Authors: Zhou, Ying1 (AUTHOR) yzhou59@buffalo.edu, Crooks, Andrew1 (AUTHOR) atcrooks@buffalo.edu
Source: Applied Geography. Jul2026, Vol. 192, pN.PAG-N.PAG. 1p.
Subjects: Fear of crime, Geographic spatial analysis, Content analysis, Public safety, Natural language processing, Emotions, Urban planning
Geographic Terms: New York (N.Y.)
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. • Natural Language Processing methods for detecting emotions from social media data. • The relationship between safety-related fear and crime. • The factors of place and space that contribute to fear. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
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. • Natural Language Processing methods for detecting emotions from social media data. • The relationship between safety-related fear and crime. • The factors of place and space that contribute to fear. [ABSTRACT FROM AUTHOR]
ISSN:01436228
DOI:10.1016/j.apgeog.2026.104051