Shape-based Methods in Mobility Data Analysis: Effectiveness and Limitations

Shape-based Methods in Mobility Data Analysis: Effectiveness and Limitations | 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 Shape-based Methods in Mobility Data Analysis: Effectiveness and Limitations Cristiano Landi, Riccardo Guidotti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5369626/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Although Mobility Data Analysis (MDA) has been explored for a long time, it still lags behind advancements in other fields.A common issue in MDA is the lack of methods' standardization and reusability. On the other hand, for instance, in time series analysis, the existing methods are typically general-purpose, and it is possible to apply them across diverse datasets and applications without extensive customization. Still, in MDA, most contributions are ad-hoc and designed to address specific research questions, which limits their generalizability and reusability. Recently, some researchers explored the application of shapelet transform to trajectory data, i.e., extracting discriminatory sub-trajectories from training data to be used as classification features. Unlike current MDA methods, this line of research eliminates the need for feature engineering, greatly improving its ability to generalize. While shapelets on mobility data have shown state-of-the-art performance on public classification datasets, it is still not clear why they work. Are these subtrajectories merely proxies for geographic location, or do they also capture motion dynamics? We empirically show that shapelet-based approaches are a viable alternative to classical methods and flexible enough to solve MDA tasks related solely to trajectory shape, solely to movement dynamics, and those related to both. Additionally, we investigate the problem of Geographic Transferability, showing that such approaches offer a promising starting point for tackling this challenge. Mobility Data Analysis Interpretable Trajectory Classification Geographic Shapelets Geographic Transferability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Jan, 2025 Reviews received at journal 23 Dec, 2024 Reviews received at journal 04 Dec, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviewers agreed at journal 21 Nov, 2024 Reviewers invited by journal 21 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Submission checks completed at journal 04 Nov, 2024 First submitted to journal 31 Oct, 2024 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-5369626","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378175915,"identity":"e7aa3e1d-5208-4d82-85a9-a3e6af0fd747","order_by":0,"name":"Cristiano Landi","email":"data:image/png;base64,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","orcid":"","institution":"University of Pisa","correspondingAuthor":true,"prefix":"","firstName":"Cristiano","middleName":"","lastName":"Landi","suffix":""},{"id":378175916,"identity":"732bef60-c2fd-4be6-a86a-85bb8a72b977","order_by":1,"name":"Riccardo Guidotti","email":"","orcid":"","institution":"University of Pisa","correspondingAuthor":false,"prefix":"","firstName":"Riccardo","middleName":"","lastName":"Guidotti","suffix":""}],"badges":[],"createdAt":"2024-11-01 00:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5369626/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5369626/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69270367,"identity":"c4a437ca-c67d-473a-97b3-80717cd12ef0","added_by":"auto","created_at":"2024-11-18 15:12:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3020346,"visible":true,"origin":"","legend":"","description":"","filename":"GeoinformaticaGASMUnderstandingGeolet1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5369626/v1_covered_cb5c82c3-8b3b-45cd-a681-02f8ec9a60a8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Shape-based Methods in Mobility Data Analysis: Effectiveness and Limitations","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Mobility Data Analysis, Interpretable Trajectory Classification, Geographic Shapelets, Geographic Transferability","lastPublishedDoi":"10.21203/rs.3.rs-5369626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5369626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough Mobility Data Analysis (MDA) has been explored for a long time, it still lags behind advancements in other fields.A common issue in MDA is the lack of methods' standardization and reusability. 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