Bayesian Design for Sampling Anomalous Spatio-Temporal Data

Bayesian Design for Sampling Anomalous Spatio-Temporal 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 Bayesian Design for Sampling Anomalous Spatio-Temporal Data Katie Buchhorn, Kerrie Mengersen, Edgar Santos-Fernandez, James McGree This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4104439/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 Data collected from arrays of sensors are essential for informed decision-making in various systems. However, the presence of anomalies can compromise the accuracy and reliability of insights drawn from the collected data or information obtained via statistical analysis. This study aims to develop a robust Bayesian optimal experimental design (BOED) framework with anomaly detection methods for high-quality data collection. We introduce a general framework that involves anomaly generation, detection and error scoring when searching for an optimal design. This method is demonstrated using two comprehensive simulated case studies: the first study uses a spatial dataset, and the second uses a spatio-temporal river network dataset. As a baseline approach, we employed a commonly used prediction-based utility function based on minimising errors. Results illustrate the trade-off between predictive accuracy and anomaly detection performance for our method under various design scenarios. An optimal design robust to anomalies ensures the collection and analysis of more trustworthy data, playing a crucial role in understanding the dynamics of complex systems such as the environment, therefore enabling informed decisions in monitoring, management, and response. nomaly Detection Bayesian Design Optimal Experimental Design Robust Design Sensor Data Spatio-temporal Model Spatial Model 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. 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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-4104439","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280424714,"identity":"ffae29fe-04bd-4992-b364-e8f52e09710d","order_by":0,"name":"Katie Buchhorn","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACCQYeBgbGhgNAJvMBiNAB4rWwJTAwJJCmhceAOC2SDbzHJH7uuCNnPrvn423eHwxyfDcSGD/z4NEizcCXJtl75pmxzJ2zm615EhiMJW8kMEvj0yLHwGMmzdh2OHGGRO42aaCWxA03EhiI1ZLzDKSlHqiF+Td+hyG0sIG0JBjcSGDDa4tkM1+yZW/bYWMJiTRjyzlpEoYzzzxss5yDR4vE8d6DN362HZaTkEh+eOONjY083/Hkwzfe4NHCwIysH4yA0YRPA5qVxCsdBaNgFIyCkQQATc9HC7fgTNEAAAAASUVORK5CYII=","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Katie","middleName":"","lastName":"Buchhorn","suffix":""},{"id":280424715,"identity":"1aac00cf-b061-4653-bb22-7c3d65181ea8","order_by":1,"name":"Kerrie Mengersen","email":"","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kerrie","middleName":"","lastName":"Mengersen","suffix":""},{"id":280424716,"identity":"70ef6697-f71c-4d48-a3de-645c084203f1","order_by":2,"name":"Edgar Santos-Fernandez","email":"","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Edgar","middleName":"","lastName":"Santos-Fernandez","suffix":""},{"id":280424717,"identity":"7f03a6f7-a4de-45d5-a461-cf57d747fe98","order_by":3,"name":"James McGree","email":"","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"McGree","suffix":""}],"badges":[],"createdAt":"2024-03-15 03:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4104439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4104439/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60155900,"identity":"589adb25-99fa-4e3c-b8e3-e4a7252f75ec","added_by":"auto","created_at":"2024-07-12 11:56:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":860239,"visible":true,"origin":"","legend":"","description":"","filename":"StatisticsandComputingBayesianDesignforAnomalousData6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4104439/v1_covered_f0f77b3c-09dd-49ed-8f9f-070aee1f61e0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bayesian Design for Sampling Anomalous Spatio-Temporal Data","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":"nomaly Detection, Bayesian Design, Optimal Experimental Design, Robust Design, Sensor Data, Spatio-temporal Model, Spatial Model","lastPublishedDoi":"10.21203/rs.3.rs-4104439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4104439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Data collected from arrays of sensors are essential for informed decision-making in various systems. 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