Using Computational Systematic Social Observation to Identify Environmental Correlates of Fear of Crime

Using Computational Systematic Social Observation to Identify Environmental Correlates of Fear of Crime | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Using Computational Systematic Social Observation to Identify Environmental Correlates of Fear of Crime Reka Solymosi, Simon Parkinson, Andrea Pődör, Saad Khan, Muhammad Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5016349/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There is great importance in understanding whether people perceive an environment as safe or unsafe. Perceptions are influenced by the built environment, and through better understanding, design interventions can be made to improve the feeling of safety. There is a rich body of research on this topic, yet it requires a lot of manual effort. In this work, we present an approach named Computational Systematic Social Observation (CSSO) to automate the collection and analysis process. The approach uses Google Street View and the Google Vision API to extract characteristics (herein referred to as features) of the built environment that is used to automate the process of understanding whether people will feel fear or safety. In testing this approach, we extracted 1.3M images for the 100 locations and identified 297 features of the built environment. A measure of dependency demonstrated that some are more strongly associated with areas where people express a feeling of safety or fear. Further, through empirical testing, it is observed that these features can be used for classification. The results demonstrate the potential of the technique and were compared with human coders. The presented methodology and experimental research provide a foundation for systematic computational observation to identify environmental correlates of fear of crime. Criminology Computational Systematic Social Observation (CSSO) Google Street View Computational Observation Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5016349","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348238599,"identity":"7eba4f54-7e9d-4b5f-9d05-6223a72e378d","order_by":0,"name":"Reka Solymosi","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Reka","middleName":"","lastName":"Solymosi","suffix":""},{"id":348238600,"identity":"a328324b-ead4-48d1-8379-e38c2fc4f583","order_by":1,"name":"Simon 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