The characteristic analysis and pattern classification of Beijing’s commercial districts based on multi-source geographical big data

The characteristic analysis and pattern classification of Beijing’s commercial districts based on multi-source geographical big data | 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 The characteristic analysis and pattern classification of Beijing’s commercial districts based on multi-source geographical big data Zonghan Yang, Ci Song, Peiqi Wang, Xiaotong Wang, Dayu Cheng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9132698/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Commercial districts, as key hubs for diverse commercial activities, play a crucial role in urban life. Yet in recent years, the vitality of commercial districts is declining due to impacts including COVID-19 pandemic. Although various strategies for revitalizing commercial districts have been proposed, it’s essential to first understand their conditions before any interventions being made. Traditional studies primarily focus on location, operational, and service characteristics of commercial districts, yet they suffer from limitations such as poor data usability, a lack of a comprehensive analysis and an oversimplified classification scheme. Geographical big data has provided unprecedented perspectives for commercial district research, thereby presenting new opportunities for overcoming the limitations of traditional studies. To this end, using multisource geographical big data, we delineated commercial districts within Beijing’s 5th Ring Road through clustering algorithms, developed a comprehensive characteristic system from four dimensions of commercial scale and composition, vitality and temporal heterogeneity, radiation and location and environment, and established a set of criteria to classify them into different patterns. The result shows that there are 67 commercial districts within Beijing’ 5th Ring Road, and they differ across different dimensions, according to which we can classify them into seven patterns, including City-level Core, Regional Core, Specialized Comparison, Suburban Hub, Regional Hub, Weekday-oriented Local and Weekend-oriented Local. Our study serves as an example of commercial districts research in the geographical big data era. The result provides practical guidance for the precise planning and refined management of commercial districts, supporting the revitalization of commercial district. Commercial district geographical big data commercial vitality commercial pattern Beijing Full Text Additional Declarations No competing interests reported. Supplementary Files Supplydata.csv Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers invited by journal 24 Mar, 2026 Editor assigned by journal 18 Mar, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 15 Mar, 2026 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. 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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-9132698","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611423321,"identity":"856577f0-4f01-4c5b-b70c-33cd4d26ae1a","order_by":0,"name":"Zonghan Yang","email":"","orcid":"","institution":"Institute of Geographic Sciences and Natural Resources Research","correspondingAuthor":false,"prefix":"","firstName":"Zonghan","middleName":"","lastName":"Yang","suffix":""},{"id":611423322,"identity":"8e480a7e-8a9b-498a-8e34-5ec8f355ccfc","order_by":1,"name":"Ci 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