Abstract
What makes us feel safe when walking around our cities? Previous research has shown that our perception of safety strongly depends on characteristics of the built environment; separately, research has also shown that safety perceptions depend on the people we encounter on the streets. However, it is not clear how the two relate to one another. In this paper, we propose a quantitative method to investigate this relationship. Using an online crowd-sourcing approach, we collected 5452 safety ratings from over 500 users about images showing various combinations of built environment and people inhabiting it. We applied analysis of covariance (ANCOVA) to the collected data and found that familiarity of the scene is the single most important predictor of our sense of safety. Controlling for familiarity, we identified then what features of the urban environment increase or decrease our safety perception.
Original language | English |
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Title of host publication | Urb-IoT 2016 - 2nd International Conference on IoT in Urban Space, Conference Proceedings |
Publisher | Association for Computing Machinery (ACM) |
Pages | 71-77 |
Number of pages | 7 |
Volume | 24-25-May-2016 |
ISBN (Electronic) | 9781450342049 |
DOIs | |
Publication status | Published - 24 May 2016 |
Event | 2nd International Conference on IoT in Urban Space, Urb-IoT 2016 - Tokyo, Japan Duration: 24 May 2016 → 25 May 2016 |
Conference
Conference | 2nd International Conference on IoT in Urban Space, Urb-IoT 2016 |
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Country/Territory | Japan |
City | Tokyo |
Period | 24/05/16 → 25/05/16 |
Keywords
- Crowdsourcing
- Environment
- Perception
- Safety
- Urban