{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:29:29Z","timestamp":1785511769569,"version":"3.56.0"},"reference-count":27,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T00:00:00Z","timestamp":1579132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The U.S. electric-power infrastructure urgently needs renovation. Recent major power outages in California, New York, Texas, and Florida have highlighted U.S. electric-power unreliability. The media have discussed the U.S. aging power infrastructure and the Public Utilities Commission has demanded a comprehensive review of the causes of recent power outages. This paper explores geographic information systems (GIS) and a spatially enhanced predictive power-outage model to address: How may spatial analytics enhance our understanding of power outages? To answer this research question, we developed a spatial analysis framework that utilities can use to investigate power-failure events and their causes. Analysis revealed areas of statistically significant power outages due to multiple causes. This study\u2019s GIS model can help to advance smart-grid reliability by, for example, elucidating power-failure root causes, defining a data-responsive blackout solution, or implementing a continuous monitoring and management solution. We unveil a novel use of spatial analytics to enhance power-outage understanding. Future work may involve connecting to virtually any type of streaming-data feed and transforming GIS applications into frontline decision applications, showing power-outage incidents as they occur. GIS can be a major resource for electronic-inspection systems to lower the duration of customer outages, improve crew response time, as well as reduce labor and overtime costs.<\/jats:p>","DOI":"10.3390\/ijgi9010054","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T04:14:41Z","timestamp":1579234481000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Spatial Analytics Framework to Investigate Electric Power-Failure Events and Their Causes"],"prefix":"10.3390","volume":"9","author":[{"given":"Vivian","family":"Sultan","sequence":"first","affiliation":[{"name":"Center for Information Systems and Technology, Claremont Graduate University, Claremont, CA 91711, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Hilton","sequence":"additional","affiliation":[{"name":"Center for Information Systems and Technology, Claremont Graduate University, Claremont, CA 91711, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"LaCommare, K., and Eto, J. (2004). Understanding the Cost of Power Interruptions to U.S. Electricity Consumers.","DOI":"10.2172\/834270"},{"key":"ref_2","unstructured":"Eto, J. (2017). The National Cost of Power Interruptions to Electricity Consumers\u2014Revised Update."},{"key":"ref_3","unstructured":"President\u2019s Council of Economic Advisers and the U.S. Department of Energy\u2019s Office of Electricity Delivery and Energy Reliability, with Assistance from the White House Office of Science and Technology (2015, November 19). Economic Benefits of Increasing Electric Grid Resilience to Weather Outages, Available online: http:\/\/energy.gov\/sites\/prod\/files\/2013\/08\/f2\/Grid ResiliencyReport_FINAL.pdf."},{"key":"ref_4","unstructured":"Porter, J. (2019, August 13). The $306 Billion Question: How to Make Outage Management Better?. 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Weather-Related Power Outages and Electric System Resiliency, Available online: https:\/\/fas.org\/sgp\/crs\/misc\/R42696.pdf."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"National Academies of Sciences, Engineering, and Medicine (2017). Enhancing the Resilience of the Nation\u2019s Electricity System, The National Academies Press. Available online: https:\/\/doi.org\/10.17226\/24836.","DOI":"10.17226\/24836"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jweia.2007.04.002","article-title":"Electric utility distribution analysis for extreme winds","volume":"96","author":"Reed","year":"2008","journal-title":"J. Wind Eng. Ind. Aerodyn."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1109\/TSG.2016.2546181","article-title":"Data-Driven Power Outage Detection by Social Sensors","volume":"7","author":"Sun","year":"2016","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_24","unstructured":"Guven, N., Ozay, N., and Tunah, E. (1996, January 16). GIS based outage analysis system for electric distribution networks. Proceedings of the 8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (MELECON 96), Bari, Italy."},{"key":"ref_25","unstructured":"EPRI Electric Power Research Institute (2019, August 12). EPRI Distribution Modernization Demonstration (DMD) Data Mining Initiative. (n.d.). Available online: https:\/\/smartgrid.epri.com\/DMD-DMI.aspx."},{"key":"ref_26","unstructured":"Georgia Spatial Data Infrastructure (2019, August 13). About | GaSDI. Available online: https:\/\/www.georgiaspatial.org\/gasdi\/about."},{"key":"ref_27","unstructured":"NOAA\u2019s National Centers for Environmental Information (2019, August 13). Storm Events Database | National Centers for Environmental Information, Available online: https:\/\/www.ncdc.noaa.gov\/stormevents\/."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/1\/54\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:26:32Z","timestamp":1760365592000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/1\/54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,16]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["ijgi9010054"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9010054","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,16]]}}}