SENDAS: Scalable ENrichment for mobility DAta Sets

SENDAS: Scalable ENrichment for mobility DAta Sets | 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 SENDAS: Scalable ENrichment for mobility DAta Sets Henrique Santana, Germano Santos, Fabrício Silva, Thais Silva This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9371914/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Recent technological advancement and popularization have grown the availability of geo-referenced mobility data, collected from different sources. Extracting knowledge from such large amounts of data is an active research and industrial issue, with adiverse range of applications. However, existing solutions currently lack scalability. Moreover, some important features are not available in such solutions. To cover this gap, we introduce SENDAS – Scalable ENrichment for mobility DAta Sets – a new framework enabling parallel and distributed execution for mobility analysis techniques. We present definitions extending and improving the efficiency of state-of-the-art methods to extract stay points, areas of interest, semantic motifs, origin-destination flows, among other metrics. The performance results reveal that it was possible to speed up the processing time up to 8.6 times when compared to existing solutions in large-scale datasets. In addition, we present a case study of SENDAS by comparing the mobility patterns of millions of users during the COVID-19 pandemic period. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers invited by journal 28 Apr, 2026 Editor assigned by journal 12 Apr, 2026 Submission checks completed at journal 12 Apr, 2026 First submitted to journal 09 Apr, 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. 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-9371914","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631075483,"identity":"0ebfaa09-8583-489d-82c7-4a441e0ba240","order_by":0,"name":"Henrique Santana","email":"","orcid":"","institution":"Universidade Federal de Viçosa","correspondingAuthor":false,"prefix":"","firstName":"Henrique","middleName":"","lastName":"Santana","suffix":""},{"id":631075484,"identity":"2d11801b-ae9e-4f6e-8028-df266e0a7231","order_by":1,"name":"Germano Santos","email":"","orcid":"","institution":"Universidade Federal de Viçosa","correspondingAuthor":false,"prefix":"","firstName":"Germano","middleName":"","lastName":"Santos","suffix":""},{"id":631075485,"identity":"298e0366-2280-43c8-b10f-ad9d92decc34","order_by":2,"name":"Fabrício Silva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie3PMerCMBTH8Z8E2iXYNVO9QqSLk14lpUOX9o9ugqJ18Qz1Fh7AIVCIi57BiuB/EenoaBQVF1NHh3y3N3x47wE22w/mZhQQYPdBloAPIkFMhMp3IoAAjviCvNIkzGqJu1VVOe74cNd7Gc678VJRTgYrA6F/US4UC0ATrkmU3kl+/Ex6SAJ9DAszJPqXDUmXu7kgVBq2eKcH8f5LTaYxV04NYc8tTHAphoXgisgaco5w+8VhpxtZtxeaFLnxsLRoXMYT3/PiQ1XxUaupGrND30BeOe/DN8Bms9lshq4/2E9E+qCxswAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Federal de Viçosa","correspondingAuthor":true,"prefix":"","firstName":"Fabrício","middleName":"","lastName":"Silva","suffix":""},{"id":631075486,"identity":"41f95520-5bc2-4d07-a42b-ed7164f21c34","order_by":3,"name":"Thais Silva","email":"","orcid":"","institution":"Universidade Federal de Viçosa","correspondingAuthor":false,"prefix":"","firstName":"Thais","middleName":"","lastName":"Silva","suffix":""}],"badges":[],"createdAt":"2026-04-09 19:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9371914/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9371914/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108977971,"identity":"b4950eee-49ed-4ec1-93e9-ad5ad8cb1b93","added_by":"auto","created_at":"2026-05-11 11:33:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":973780,"visible":true,"origin":"","legend":"","description":"","filename":"sendasgeoinformatica.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9371914/v1_covered_e4ff6846-bad5-4396-a22f-3736e0d2da46.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SENDAS: Scalable ENrichment for mobility DAta Sets","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"geoinformatica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gein","sideBox":"Learn more about [GeoInformatica](http://link.springer.com/journal/10707)","snPcode":"10707","submissionUrl":"https://submission.nature.com/new-submission/10707/3","title":"GeoInformatica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9371914/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9371914/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Recent technological advancement and popularization have grown the availability of geo-referenced mobility data, collected from different sources. 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