{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T23:21:53Z","timestamp":1788304913677,"version":"build-2803163510"},"reference-count":43,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,4,30]],"date-time":"2017-04-30T00:00:00Z","timestamp":1493510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011131","name":"State Key Laboratory of Resources and Environmental Information System","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011131","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National key research and development program of China","doi-asserted-by":"publisher","award":["2016YFA0600304"],"award-info":[{"award-number":["2016YFA0600304"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671401"],"award-info":[{"award-number":["41671401"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371385"],"award-info":[{"award-number":["41371385"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The ENSO (El Ni\u00f1o Southern Oscillation) is the dominant inter-annual climate signal on Earth, and its relationships with marine environments constitute a complex interrelated system. As traditional methods face great challenges in analyzing which, how and where marine parameters change when ENSO events occur, we propose an ENSO-oriented marine spatial association pattern (EOMSAP) mining algorithm for dealing with multiple long-term raster-formatted datasets. EOMSAP consists of four key steps. The first quantifies the abnormal variations of marine parameters into three levels using the mean-standard deviation criteria of time series; the second categorizes La Ni\u00f1a events, neutral conditions, or El Ni\u00f1o events using an ENSO index; then, the EOMSAP designs a linking\u2013pruning\u2013generating recursive loop to generate (m + 1)-candidate association patterns from an m-dimensional one by combining a user-specified support with a conditional support; and the fourth generates strong association patterns according to the user-specified evaluation indicators. To demonstrate the feasibility and efficiency of EOMSAP, we present two case studies with real remote sensing datasets from January 1998 to December 2012: one considers performance analysis relative to the ENSO-Apriori and Apriori methods; and the other identifies marine spatial association patterns within the Pacific Ocean.<\/jats:p>","DOI":"10.3390\/ijgi6050139","type":"journal-article","created":{"date-parts":[[2017,5,2]],"date-time":"2017-05-02T11:37:20Z","timestamp":1493725040000},"page":"139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3605-6578","authenticated-orcid":false,"given":"Xue","family":"Cunjin","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of the Earth Observation, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liao","family":"Xiaohan","sequence":"additional","affiliation":[{"name":"Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing\u00a0100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1126\/science.1132588","article-title":"ENSO as an integrating concept in earth science","volume":"314","author":"McPhaden","year":"2006","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.pocean.2006.03.004","article-title":"ENSO variability and the eastern tropical Pacific: A review","volume":"69","author":"Wang","year":"2006","journal-title":"Prog. 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