{"id":"https://openalex.org/W2039610578","doi":"https://doi.org/10.1145/2345316.2345333","title":"Exploring multivariate spatio-temporal change in climate data using image analysis techniques","display_name":"Exploring multivariate spatio-temporal change in climate data using image analysis techniques","publication_year":2012,"publication_date":"2012-07-01","ids":{"openalex":"https://openalex.org/W2039610578","doi":"https://doi.org/10.1145/2345316.2345333","mag":"2039610578"},"language":"en","primary_location":{"id":"doi:10.1145/2345316.2345333","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2345316.2345333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007920767","display_name":"Michael P. McGuire","orcid":"https://orcid.org/0000-0001-7585-8018"},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Michael P. McGuire","raw_affiliation_strings":["Towson University, Baltimore, Maryland"],"affiliations":[{"raw_affiliation_string":"Towson University, Baltimore, Maryland","institution_ids":["https://openalex.org/I4322298"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051208435","display_name":"Aryya Gangopadhyay","orcid":"https://orcid.org/0000-0002-7553-7932"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aryya Gangopadhyay","raw_affiliation_strings":["University of Maryland, Baltimore, Maryland","University of Maryland , Baltimore, Maryland"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore, Maryland","institution_ids":["https://openalex.org/I126744593"]},{"raw_affiliation_string":"University of Maryland , Baltimore, Maryland","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063541070","display_name":"Vandana P. Janeja","orcid":"https://orcid.org/0000-0003-0130-6135"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vandana P. Janeja","raw_affiliation_strings":["University of Maryland, Baltimore, Maryland","University of Maryland , Baltimore, Maryland"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Baltimore, Maryland","institution_ids":["https://openalex.org/I126744593"]},{"raw_affiliation_string":"University of Maryland , Baltimore, Maryland","institution_ids":["https://openalex.org/I126744593"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5007920767"],"corresponding_institution_ids":["https://openalex.org/I4322298"],"apc_list":null,"apc_paid":null,"fwci":0.2588458,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.64964891,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9779000282287598,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7267658710479736},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.6153138279914856},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5876591205596924},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5508178472518921},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5139024257659912},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47677305340766907},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.45914551615715027},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.4449578523635864},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4334762394428253},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.42619800567626953},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2606176435947418}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7267658710479736},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.6153138279914856},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5876591205596924},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5508178472518921},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5139024257659912},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47677305340766907},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.45914551615715027},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.4449578523635864},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4334762394428253},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.42619800567626953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2606176435947418},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2345316.2345333","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2345316.2345333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1546804097","https://openalex.org/W1565526643","https://openalex.org/W1572459612","https://openalex.org/W1831066769","https://openalex.org/W1978601068","https://openalex.org/W1980073965","https://openalex.org/W2005935294","https://openalex.org/W2024165284","https://openalex.org/W2038037288","https://openalex.org/W2062836719","https://openalex.org/W2091775809","https://openalex.org/W2104778285","https://openalex.org/W2113439322","https://openalex.org/W2114198065","https://openalex.org/W2116679011","https://openalex.org/W2120117766","https://openalex.org/W2121970368","https://openalex.org/W2135635984","https://openalex.org/W2145023731","https://openalex.org/W2151382530","https://openalex.org/W2154104079","https://openalex.org/W2160922539","https://openalex.org/W2172088232","https://openalex.org/W2173251738","https://openalex.org/W2249309592","https://openalex.org/W2342204193","https://openalex.org/W3010932768","https://openalex.org/W3100541164","https://openalex.org/W6656357712"],"related_works":["https://openalex.org/W2568858292","https://openalex.org/W1515964938","https://openalex.org/W2389381914","https://openalex.org/W4255837520","https://openalex.org/W2376528221","https://openalex.org/W196800607","https://openalex.org/W2406638334","https://openalex.org/W2359428812","https://openalex.org/W3181296946","https://openalex.org/W2387011115"],"abstract_inverted_index":{"Spatio-temporal":[0],"data":[1],"from":[2],"earth":[3],"observation":[4],"systems":[5],"and":[6,79,153,163],"models":[7],"are":[8],"increasing":[9],"at":[10],"astronomical":[11],"rates":[12],"in":[13,19,102,115,158],"the":[14,35,55,103,113,138,154],"climate":[15,151],"domain.":[16],"This":[17,63],"results":[18,155],"a":[20,39,60,66,87,106,149],"massive":[21],"dataset":[22,152],"that":[23,57,159],"is":[24,109],"increasingly":[25],"difficult":[26],"to":[27,29,44,46,51,59,68,97,111,129,133],"navigate":[28,45],"find":[30,98,134],"interesting":[31,164],"time":[32],"periods":[33],"where":[34],"spatial":[36,139],"pattern":[37,140],"of":[38,89,123,141],"process":[40],"changes.":[41],"The":[42,84,120],"ability":[43],"such":[47],"areas":[48],"can":[49,126],"lead":[50],"new":[52,135,160],"knowledge":[53],"about":[54],"factors":[56],"contribute":[58],"spatio-temporal":[61,72,124,131],"process.":[62],"paper":[64],"proposes":[65],"method":[67],"automatically":[69],"characterize":[70],"multi-variate":[71],"datasets":[73,132],"using":[74],"basic":[75],"image":[76,91],"processing":[77],"techniques":[78],"an":[80],"efficient":[81],"distance":[82,107],"measure.":[83],"approach":[85],"uses":[86],"measure":[88,108,122],"local":[90],"entropy":[92],"combined":[93],"with":[94],"edge":[95],"detection":[96],"naturally":[99],"occurring":[100],"boundaries":[101,117],"dataset.":[104],"Then":[105],"used":[110,128],"track":[112],"change":[114,125],"these":[116],"over":[118,143],"time.":[119,144],"resulting":[121],"be":[127],"explore":[130],"relationships":[136,165],"between":[137,166],"variables":[142,167],"Experiments":[145],"were":[146,156,168],"performed":[147],"on":[148],"real-world":[150],"promising":[157],"patterns":[161],"emerged":[162],"found.":[169]},"counts_by_year":[{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
