What Makes a Place Safe? Assessing AI-Generated Safety Perception Scores Using Stockholm's Street View Images.
This article investigates what causes an urban environment to be perceived as safe using Stockholm, the capital of Sweden, as the study area. The study integrates AI-generated safety scores from street view images, image segmentation techniques and conventional and crowdsourced data using Geographic...
| Publicado en: | British Journal of Criminology Vol. 66; no. 2; pp. 265 - 290 |
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| Autores principales: | , , , , , , , , |
| Formato: | Artículo |
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Oxford University Press / USA
Mar2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192824828&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192824828 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00070955 BJC jtl: British Journal of Criminology issn: 00070955 maglogo: N pubinfo: dt: Mar2026 vid: 66 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 192824828 10.1093/bjc/azaf017 ppf: 265 ppct: 25 formats: tig: atl: What Makes a Place Safe? Assessing AI-Generated Safety Perception Scores Using Stockholm's Street View Images. aug: au: Ceccato, Vania Kang, Yuhao Abraham, Jonatan Näsman, Per Duarte, Fábio Gao, Song Ljungqvist, Lukas Zhang, Fan Ratti, Carlo affil: Urban and Community Safety Research Group, Senseable Stockholm Laboratory, KTH Royal Institute of Technology, Sweden GISense Lab, Department of Geography & the Environment, The University of Texas at Austin, USA Senseable Stockholm Laboratory, KTH Royal Institute of Technology, Sweden Senseable City Lab, Massachusetts Institute of Technology, USA GeoDS Lab, Department of Geography, University of Wisconsin-Madison, USA Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing, China su: Sweden Stockholm (Sweden) Public safety Cities & towns Image segmentation Geographic information systems Regression analysis Landscape assessment Street photography sug: subj: Public safety Cities & towns Sweden Stockholm (Sweden) Image segmentation Geographic information systems Regression analysis Landscape assessment Street photography keyword: built environment copyrightHolder:Centre for Crime and Justice Studies (formerly ISTD) copyrightYear:2026 crime deep learning GSV image segmentation inLanguage:en publisher:Oxford University Press regression models safety perceptions sameAs:https://dx.doi.org/10.1093/bjc/azaf017 street view images built environment copyrightHolder:Centre for Crime and Justice Studies (formerly ISTD) copyrightYear:2026 crime deep learning GSV image segmentation inLanguage:en publisher:Oxford University Press regression models safety perceptions sameAs:https://dx.doi.org/10.1093/bjc/azaf017 street view images ab: This article investigates what causes an urban environment to be perceived as safe using Stockholm, the capital of Sweden, as the study area. The study integrates AI-generated safety scores from street view images, image segmentation techniques and conventional and crowdsourced data using Geographical Information Systems (GIS) and regression models. After accounting for income, crime and other area characteristics, the models reveal that areas with lower safety scores primarily consist of areas with a relatively large percentage of roads in industrial and/or interstitial mixed residential areas. Conversely, higher safety scores are found in large but distinct combinations of buildings, vegetation and open sky, from detached single-family housing to inner city high-density built areas. To enhance safety in an area, good contextual knowledge of the area is fundamental to prioritize interventions in interstitial mixed residential zones where roads and highways may be the dominant features. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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