Spatial Correlation Analysis of China's Provincial New Quality Productive Forces Development Level Based on Social Network Analysis

Spatial Correlation Analysis of China's Provincial New Quality Productive Forces Development Level Based on Social Network Analysis | 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 Article Spatial Correlation Analysis of China's Provincial New Quality Productive Forces Development Level Based on Social Network Analysis Wan Xin, Chen Yanjun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7099725/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 New quality productive forces represent a paradigmatic shift in China's economic development trajectory. This study examines the spatiotemporal evolution of new quality productive forces across Chinese provinces to elucidate regional disparities and development patterns. We employed a three-stage analytical framework: (1) BP neural networks to evaluate provincial development levels (2013–2022); (2) modified gravity models to quantify spatial correlations; and (3) social network analysis to map inter-provincial relationships, with provinces as nodes and spatial correlations as edges. Cohesive subgroup analysis and independent cascade models examined agglomeration and spillover effects. Our findings reveal: (1) New quality productive forces demonstrate steady growth with pronounced east-west disparities—eastern coastal regions significantly outperform central and western areas; (2) The inter-provincial correlation network exhibits increasing density, balance, and efficiency; (3) Leading eastern provinces (Beijing, Shanghai, Jiangsu) serve as network hubs, generating substantial spillover effects.Regional development strategies should be differentiated: southeastern coastal and Yangtze River Delta regions should prioritize high-tech industries and regional integration; northern and northwestern regions should leverage resource endowments; southwestern areas should develop specialized agriculture and tourism while strengthening infrastructure; northeastern regions require innovation-driven industrial restructuring. Cross-regional collaboration in high-tech sectors should be promoted, with targeted policy support to reduce development imbalances and enhance central and western regions' capabilities.This research provides empirical evidence for spatially differentiated policies promoting balanced development of new quality productive forces across China's provinces. Social science/Development studies Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences Scientific community and society/Geography Social science/Geography New quality productive forces development level BP neural network Modified gravity model Social network analysis Full Text Additional Declarations No competing interests reported. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7099725","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":501305144,"identity":"c8677b53-b276-4d90-929f-2de54f30e3ab","order_by":0,"name":"Wan Xin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACNobDBx98MJCQ42dvPkCcFj7GY8mGMwosjCV7jiUQp0WO+YyZNM+HisQNM3IMiHQY27E0aR4DCWMDhpyPN94w2MnpNhDSwnP4sOUcoF/MGc5utpzDkGxsdoCQFoljiTfeAG2xbOzdJs3DcCBxG0Et8kD1QIclbjjM84xILQxnjCTBWo7xsBGrBRTIQIdJ9rAZAz1FhF/kG0BR+adOjl/+8cMbbyrs5AhqQQFAT5GiHKKFVB2jYBSMglEwIgAAruhCxI9w1QUAAAAASUVORK5CYII=","orcid":"","institution":"Wuhan Textile University","correspondingAuthor":true,"prefix":"","firstName":"Wan","middleName":"","lastName":"Xin","suffix":""},{"id":501305145,"identity":"d5fc37b1-ced7-44df-8f2e-0dce7e271442","order_by":1,"name":"Chen Yanjun","email":"","orcid":"","institution":"Wuhan Textile University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Yanjun","suffix":""}],"badges":[],"createdAt":"2025-07-11 08:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7099725/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7099725/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107039702,"identity":"46c7b4a7-a844-4d0a-9a61-99881cdbdf14","added_by":"auto","created_at":"2026-04-16 05:41:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":785820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscripts.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7099725/v1_covered_12d2a7e8-7f1a-452d-97dd-cf371dd2d6a5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Correlation Analysis of China's Provincial New Quality Productive Forces Development Level Based on Social Network Analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"New quality productive forces development level, BP neural network, Modified gravity model, Social network analysis","lastPublishedDoi":"10.21203/rs.3.rs-7099725/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7099725/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNew quality productive forces represent a paradigmatic shift in China's economic development trajectory. This study examines the spatiotemporal evolution of new quality productive forces across Chinese provinces to elucidate regional disparities and development patterns. We employed a three-stage analytical framework: (1) BP neural networks to evaluate provincial development levels (2013\u0026ndash;2022); (2) modified gravity models to quantify spatial correlations; and (3) social network analysis to map inter-provincial relationships, with provinces as nodes and spatial correlations as edges. Cohesive subgroup analysis and independent cascade models examined agglomeration and spillover effects. 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