Evaluation indicator system construction and data processing method using resilience evaluation of Chinese provinces as a case study

Evaluation indicator system construction and data processing method using resilience evaluation of Chinese provinces as a case study | 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 Evaluation indicator system construction and data processing method using resilience evaluation of Chinese provinces as a case study Mingzhen Wang, Lin Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4108724/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 Comprehensive evaluation is widely used in many fields, and the evaluation results are an important basis for the next decision. In order to obtain reasonable and reliable evaluation results, the evaluation indicator system must be able to scientifically, comprehensively and accurately reflect the object and purpose of the evaluation, and all indicator data must be authoritative, accurate and properly processed. Based on the above requirements, the construction of resilience evaluation indicator system and data processing method are put forward, the specific use steps are combed out, and the calculation process and results are given by taking China’s provincial resilience evaluation as a case study. The construction of the evaluation indicator system includes the primary selection of indicators based on evaluation objectives and existing researches, the optimization selection of indicators based on data acquisition, and the screening selection of indicators based on KMO test statistics and Measure of Sampling Adequacy (MSA). The data processing is carried out after the optimization selection of indicators, including the time sequence three-dimensional data table, the consistency processing of the indicator type, the non-negative processing and the dimensionless processing. Based on the processed time sequence data table, the eigenvectors corresponding to the positive maximum eigenvalues can be calculated, and the elements in the eigenvectors are the weights of each indicator. evaluation indicator data processing disaster resilience Chinese provinces empirical research Full Text 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. 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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-4108724","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288468704,"identity":"014e696c-f78b-46ba-893b-00cfcace6899","order_by":0,"name":"Mingzhen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIie3PIQ7CMBTG8ZYmxTyCbZMdooogFsZR3rJkGA7RZJYLkN0D3WViDhxZUrMZ9CYxBIsheTjE++nvLz4hGPtPcshfKayXnp4oN+gysadAT7QddJu6fk/cu3v3MAg3EL2Q03ykJBGLLZoIsvbKni+EZBOx6dFFUEnQakVLcm8Qr6ANkpNiYTAEAHKSxVK53BdgoKloX2x9GMen32VZVzXTTEk+SP/bnjHG2HdvVrc2e5TfTkAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4852-9443","institution":"Chongqing University of Arts and Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mingzhen","middleName":"","lastName":"Wang","suffix":""},{"id":288468705,"identity":"b5ef0a7b-d998-4e3c-ad2a-c332d59b84ff","order_by":1,"name":"Lin Gao","email":"","orcid":"","institution":"Chongqing University of Arts and Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2024-03-15 14:45:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4108724/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4108724/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57972041,"identity":"a917236c-4bba-4e01-a03d-3ad8fe05e3b7","added_by":"auto","created_at":"2024-06-08 09:59:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":693305,"visible":true,"origin":"","legend":"","description":"","filename":"renamed4fea9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4108724/v1_covered_1227c7cb-0fc3-438c-a05f-6454f7c54269.pdf"}],"financialInterests":"","formattedTitle":"Evaluation indicator system construction and data processing method using resilience evaluation of Chinese provinces as a case study","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":"evaluation indicator, data processing, disaster resilience, Chinese provinces, empirical research","lastPublishedDoi":"10.21203/rs.3.rs-4108724/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4108724/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eComprehensive evaluation is widely used in many fields, and the evaluation results are an important basis for the next decision. 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