Kernel Density Estimation: Examining the Effect of Kernel Function, Bandwidth, and Grid Cell Size in Mapping Earthquake-Prone in Indonesia

Kernel Density Estimation: Examining the Effect of Kernel Function, Bandwidth, and Grid Cell Size in Mapping Earthquake-Prone in Indonesia | 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 Kernel Density Estimation: Examining the Effect of Kernel Function, Bandwidth, and Grid Cell Size in Mapping Earthquake-Prone in Indonesia Ari Fadli, Tri Kuntoro Priyambodo, Agfianto Eko Putra, Wiwit Suryanto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5264767/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Identifying earthquake-prone areas is critical for disaster mitigation to reduce casualties and economic losses. This study applies Kernel Density Estimation (KDE) to analyze and rank earthquake-prone regions, focusing on variations in kernel functions, bandwidths, and grid sizes. The Indonesian Earthquake Catalog (1964–2023) is used as a case study. The results indicate that different kernel functions have unique strengths. The Epanechnikov kernel provides an even density distribution, particularly in low and medium categories, while the Gaussian kernel captures high concentrations effectively, especially in high and extreme categories. The Biweight kernel performs well in medium and high categories but less effectively identifies extreme density concentrations. Grid size also significantly impacts results; smaller grids (0.25 x 0.25) reveal detailed density patterns but may overemphasize localized concentrations, whereas larger grids (5 x 5) are suited for macro-scale analyses but can obscure finer variations. Bandwidth selection significantly affects density estimates. Smaller bandwidths (0.1) spread density widely, resulting in many grids in the low category but few in the medium, high, and extreme categories. Medium bandwidths (0.3) increase the proportion of medium and high categories, while larger bandwidths (0.5) produce the highest proportion of grids in the medium category, though extreme values remain limited. These variations demonstrate how bandwidth choices influence the balance between localized detail and broader distribution patterns. KDE effectively identifies earthquake-prone areas with varying densities and cluster significance, providing essential insights to prioritize disaster mitigation efforts and improve spatial planning in earthquake-prone regions. The combination of Gaussian kernel, 0.5 bandwidth, and 1x1 degree grid yields the greatest results in completely mapping earthquake risk. Kernel Density Estimation Earthquake Mapping Earthquake Prediction Hotspot Analysis Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 May, 2026 Reviewers invited by journal 27 Mar, 2026 Editor invited by journal 24 Nov, 2024 Editor assigned by journal 22 Nov, 2024 First submitted to journal 21 Nov, 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. 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-5264767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613125423,"identity":"6429b6f0-6969-470d-ae6f-d0d20d54aa86","order_by":0,"name":"Ari Fadli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYFACxoYPDGwWPPxA5oEHcFEDZnxaGmcwsEnwSDYAtSQQp4WBEaSFweAAkInQwoBbC//sw40NP8okZIyvHX4ItOWw3doG5ocfGAqscWqROJfY2NhzToLH7HaaAUhL8rYDbMZAS9NxO+sMY/sD3jaQlgSIFrMDDGZAvxzGqUP+DGNj41+gFuPZ6R+gWti/4dViANTSDLLFQDoHbIud2QEe/LYYgrTIAP0icTun4ECCQXqC2WGeYokEPH6RO8P+sPFNmY09/+z0zR8+VFjbmx1v3/jhwx/cIYbuTobEBlCMJBCrAQTsSVE8CkbBKBgFIwMAAGDWVHh1S2VlAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9551-2580","institution":"Universitas Gadjah Mada","correspondingAuthor":true,"prefix":"","firstName":"Ari","middleName":"","lastName":"Fadli","suffix":""},{"id":613125424,"identity":"1500e4a7-cfef-44d1-b1ff-fe7a64af2113","order_by":1,"name":"Tri Kuntoro Priyambodo","email":"","orcid":"","institution":"Universitas Gadjah Mada Fakultas Matematika dan Ilmu Pengetahuan Alam","correspondingAuthor":false,"prefix":"","firstName":"Tri","middleName":"Kuntoro","lastName":"Priyambodo","suffix":""},{"id":613125425,"identity":"2d92e554-12f6-4e76-ae8d-1bb68fa88b13","order_by":2,"name":"Agfianto Eko Putra","email":"","orcid":"","institution":"Universitas Gadjah Mada Fakultas Matematika dan Ilmu Pengetahuan Alam","correspondingAuthor":false,"prefix":"","firstName":"Agfianto","middleName":"Eko","lastName":"Putra","suffix":""},{"id":613125426,"identity":"a4c4586b-a642-4531-bcb7-25df0a7e4399","order_by":3,"name":"Wiwit Suryanto","email":"","orcid":"https://orcid.org/0000-0002-6275-7880","institution":"Universitas Gadjah Mada Fakultas Matematika dan Ilmu Pengetahuan Alam","correspondingAuthor":false,"prefix":"","firstName":"Wiwit","middleName":"","lastName":"Suryanto","suffix":""}],"badges":[],"createdAt":"2024-10-15 03:52:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5264767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5264767/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105904367,"identity":"2fc8813c-9fe5-4799-8e42-2d0aeac52c29","added_by":"auto","created_at":"2026-04-01 10:07:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2192413,"visible":true,"origin":"","legend":"","description":"","filename":"KDEEarthquakePronewithLineNumberFixx.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5264767/v1_covered_e0a17cec-adc0-4875-8b10-6a096809c1fa.pdf"}],"financialInterests":"","formattedTitle":"Kernel Density Estimation: Examining the Effect of Kernel Function, Bandwidth, and Grid Cell Size in Mapping Earthquake-Prone in Indonesia","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":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Kernel Density Estimation, Earthquake Mapping, Earthquake Prediction, Hotspot Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5264767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5264767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Identifying earthquake-prone areas is critical for disaster mitigation to reduce casualties and economic losses. 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