Are Safer Looking Neighborhoods More Lively? A Multimodal Investigation into Urban Life
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Abstract (arXiv)
Policy makers, urban planners, architects, sociologists, and economists are interested in creating urban areas that are both lively and safe. But are the safety and liveliness of neighborhoods independent characteristics? Or are they just two sides of the same coin? In a world where people avoid unsafe looking places, neighborhoods that look unsafe will be less lively, and will fail to harness the natural surveillance of human activity. But in a world where the preference for safe looking neighborhoods is small, the connection between the perception of safety and liveliness will be either weak or nonexistent. In this paper we explore the connection between the levels of activity and the perception of safety of neighborhoods in two major Italian cities by combining mobile phone data (as a proxy for activity or liveliness) with scores of perceived safety estimated using a Convolutional Neural Network trained on a dataset of Google Street View images scored using a crowdsourced visual perception survey. We find that: (i) safer looking neighborhoods are more active than what is expected from their population density, employee density, and distance to the city centre; and (ii) that the correlation between appearance of safety and activity is positive, strong, and significant, for females and people over 50, but negative for people under 30, suggesting that the behavioral impact of perception depends on the demographic of the population. Finally, we use occlusion techniques to identify the urban features that contribute to the appearance of safety, finding that greenery and street facing windows contribute to a positive appearance of safety (in agreement with Oscar Newman's defensible space theory). These results suggest that urban appearance modulates levels of human activity and, consequently, a neighborhood's rate of natural surveillance.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Aug 2016.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 0; 2024: 0; 2025: 1; 2026: 2.
9 papers.
- 2021Nice perfume. How long did you marinate in it? Multimodal Sarcasm Explanationcited by 25
- 2022CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detectioncited by 21
- 2021Risk Prediction on Traffic Accidents using a Compact Neural Model for Multimodal Information Fusion over Urban Big Datacited by 3
- 2021FiLMing Multimodal Sarcasm Detection with Attentioncited by 3
- 2022Computational Sarcasm Analysis on Social Media: A Systematic Reviewcited by 2
- 2022City-Wide Perceptions of Neighbourhood Quality using Street View Imagescited by 1
- 2025Analyzing Sustainability Messaging in Large-Scale Corporate Social Mediacited by 0
- 2026How Many Visual Levers Drive Urban Perception? Interventional Counterfactuals via Multiple Localised Editscited by 0
- 2026From Pixels to Policy: A Multi-Agent System for Intervention and Geo-Spatial Decision Supportcited by 0
- 2026From Pixels to Policy: A Multi-Agent System for Intervention and Geo-Spatial Decision Support1 Oct 2026
- 2026How Many Visual Levers Drive Urban Perception? Interventional Counterfactuals via Multiple Localised Edits23 Apr 2026
- 2025Analyzing Sustainability Messaging in Large-Scale Corporate Social Media3 Nov 2025
- 2022City-Wide Perceptions of Neighbourhood Quality using Street View Images22 Nov 2022
- 2022Computational Sarcasm Analysis on Social Media: A Systematic Review13 Sep 2022
- 2022CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection12 Apr 2022
- 2021Nice perfume. How long did you marinate in it? Multimodal Sarcasm Explanation9 Dec 2021
- 2021FiLMing Multimodal Sarcasm Detection with Attention9 Aug 2021
- 2021Risk Prediction on Traffic Accidents using a Compact Neural Model for Multimodal Information Fusion over Urban Big Data21 Feb 2021