Nighttime Lights Data as an Indicator of Electricity Outage Vulnerability: Case Study of Winter Storm Uri

Nighttime Lights Data as an Indicator of Electricity Outage Vulnerability: Case Study of Winter Storm Uri | 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 Nighttime Lights Data as an Indicator of Electricity Outage Vulnerability: Case Study of Winter Storm Uri Alexandra Claire Kahl, Liem Tran, Budhendra Bhaduri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4957276/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 Power outage data aggregated at a specific administrative level, such as census tract or census block, is not publicly available. This creates barriers to understanding spatial distribution of outage vulnerability, resulting in challenges in outage preparedness and disaster response. We work to better understand spatial power outage vulnerability by deriving simulated values through implementation of maximum entropy models, which represent likelihood of outage occurrence at the census tract level in Houston, TX. We develop a model utilizing nighttime light data to produce power outage likelihood values at the census block level. Nighttime lights outages during Winter Storm Uri (February 11–18, 2021) are used as inputs in the MaxEnt machine learning model. Energy transmission, generation, hospitals, emergency services and tree coverage data are used as predictors within MaxEnt. Results show that the model performs relatively well with a mean area under the curve of 0.758 (a common threshold for model evaluation is 0.70). Power line density, tree coverage and proximity to schools are the most influential variables in power outage vulnerability (contributing percentages are 73.0%, 7.1% and 5.2%, respectively). Utilizing MaxEnt prediction, we generate likelihood of outage occurrence values between 0 and 1 for each census block. Our work provides a novel methodology for nighttime lights processing and new applications for MaxEnt. These results provide insight into which census tracts are the most vulnerable to power outage during extreme weather events. nighttime lights outage resilience winter storm 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-4957276","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346414547,"identity":"41282e8c-5388-416f-bfa3-6bd3fa0f73a9","order_by":0,"name":"Alexandra Claire Kahl","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIie2QOwrCQBBAx2ZtJNuOCMkVEgJqkcPsNHoDSSEYEFJ5AUE8g1XqyEJsNqRNmco6lbUbtbDKxs5iH8zAwDzmA2Cx/CMTYNDEAM6ryj9hVIQCnbvuwQqlvyj8IO8NnaXLeM5mrQLXqUW/gmW68CmTIUPBMK8hnJoUqGCOlK0oRdBKC3QxKV41fiCdtKIX65SdUfHLg56SRPoD78WEb1ICpTYoikjfQvulUhgcVdOvuGqdYbtFl3N5reMi8pybYcoXo0QnHNxusVgslh6eAd5GFcrh3HoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3018-830X","institution":"The University of Tennessee Knoxville","correspondingAuthor":true,"prefix":"","firstName":"Alexandra","middleName":"Claire","lastName":"Kahl","suffix":""},{"id":346414548,"identity":"60b13bee-1b0f-4b62-80b1-46a2657abcbc","order_by":1,"name":"Liem Tran","email":"","orcid":"","institution":"University of Tennessee System: The University of Tennessee System","correspondingAuthor":false,"prefix":"","firstName":"Liem","middleName":"","lastName":"Tran","suffix":""},{"id":346414549,"identity":"5e661edc-cf92-4727-8d82-8ae7e7b1b23f","order_by":2,"name":"Budhendra Bhaduri","email":"","orcid":"","institution":"Oak Ridge National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Budhendra","middleName":"","lastName":"Bhaduri","suffix":""}],"badges":[],"createdAt":"2024-08-22 10:28:45","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4957276/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4957276/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69476201,"identity":"3f6abe25-92cb-4219-99cc-36376a6980d5","added_by":"auto","created_at":"2024-11-20 19:31:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":855150,"visible":true,"origin":"","legend":"","description":"","filename":"AKahlNatHazardsSubmission827.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4957276/v1_covered_c4e798d9-3e45-45e2-88c1-222bed827286.pdf"}],"financialInterests":"","formattedTitle":"Nighttime Lights Data as an Indicator of Electricity Outage Vulnerability: Case Study of Winter Storm Uri","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":true,"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":"nighttime lights, outage, resilience, winter storm","lastPublishedDoi":"10.21203/rs.3.rs-4957276/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4957276/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePower outage data aggregated at a specific administrative level, such as census tract or census block, is not publicly available. This creates barriers to understanding spatial distribution of outage vulnerability, resulting in challenges in outage preparedness and disaster response. We work to better understand spatial power outage vulnerability by deriving simulated values through implementation of maximum entropy models, which represent likelihood of outage occurrence at the census tract level in Houston, TX.\u003c/p\u003e \u003cp\u003eWe develop a model utilizing nighttime light data to produce power outage likelihood values at the census block level. Nighttime lights outages during Winter Storm Uri (February 11\u0026ndash;18, 2021) are used as inputs in the MaxEnt machine learning model. Energy transmission, generation, hospitals, emergency services and tree coverage data are used as predictors within MaxEnt.\u003c/p\u003e \u003cp\u003eResults show that the model performs relatively well with a mean area under the curve of 0.758 (a common threshold for model evaluation is 0.70). 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