{"id":"https://openalex.org/W3106732641","doi":"https://doi.org/10.1080/00401706.2022.2115558","title":"A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets","display_name":"A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets","publication_year":2022,"publication_date":"2022-08-22","ids":{"openalex":"https://openalex.org/W3106732641","doi":"https://doi.org/10.1080/00401706.2022.2115558","mag":"3106732641"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.2022.2115558","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2022.2115558","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Technometrics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/dataset/A_Scalable_Partitioned_Approach_to_Model_Massive_Nonstationary_Non-Gaussian_Spatial_Datasets/20530066","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021700695","display_name":"Benjamin Seiyon Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin Seiyon Lee","raw_affiliation_strings":["Department of Statistics, George Mason University, Fairfax, VA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, George Mason University, Fairfax, VA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100359866","display_name":"Jae\u2010Woo Park","orcid":"https://orcid.org/0000-0003-1155-8811"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jaewoo Park","raw_affiliation_strings":["Department of Applied Statistics, Yonsei University, Seoul, Republic of Korea","Department of Statistics and Data Science, Yonsei University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Statistics, Yonsei University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Department of Statistics and Data Science, Yonsei University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100359866"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":0.3724,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.46911154,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"65","issue":"1","first_page":"105","last_page":"116"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.992900013923645,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5564988851547241},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4667019844055176},{"id":"https://openalex.org/keywords/george","display_name":"George (robot)","score":0.42122000455856323},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41347646713256836},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.34915685653686523},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.32792574167251587},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27559101581573486},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20730561017990112},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06853753328323364}],"concepts":[{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5564988851547241},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4667019844055176},{"id":"https://openalex.org/C67101536","wikidata":"https://www.wikidata.org/wiki/Q5535910","display_name":"George (robot)","level":2,"score":0.42122000455856323},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41347646713256836},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.34915685653686523},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.32792574167251587},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27559101581573486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20730561017990112},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06853753328323364},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1080/00401706.2022.2115558","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2022.2115558","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Technometrics","raw_type":"journal-article"},{"id":"pmh:oai:figshare.com:article/20530066","is_oa":true,"landing_page_url":"https://figshare.com/articles/dataset/A_Scalable_Partitioned_Approach_to_Model_Massive_Nonstationary_Non-Gaussian_Spatial_Datasets/20530066","pdf_url":null,"source":{"id":"https://openalex.org/S4306402621","display_name":"INDIGO (University of Illinois at Chicago)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39422238","host_organization_name":"University of Illinois Chicago","host_organization_lineage":["https://openalex.org/I39422238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dataset"},{"id":"doi:10.6084/m9.figshare.20530066.v1","is_oa":true,"landing_page_url":"https://doi.org/10.6084/m9.figshare.20530066.v1","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/20530066","is_oa":true,"landing_page_url":"https://figshare.com/articles/dataset/A_Scalable_Partitioned_Approach_to_Model_Massive_Nonstationary_Non-Gaussian_Spatial_Datasets/20530066","pdf_url":null,"source":{"id":"https://openalex.org/S4306402621","display_name":"INDIGO (University of Illinois at Chicago)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39422238","host_organization_name":"University of Illinois Chicago","host_organization_lineage":["https://openalex.org/I39422238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Dataset"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":72,"referenced_works":["https://openalex.org/W11496332","https://openalex.org/W1520585082","https://openalex.org/W1524392826","https://openalex.org/W1553594703","https://openalex.org/W1726639901","https://openalex.org/W1837874438","https://openalex.org/W1972949070","https://openalex.org/W1973310094","https://openalex.org/W1974771473","https://openalex.org/W1975633784","https://openalex.org/W1977211365","https://openalex.org/W1997999159","https://openalex.org/W2004191385","https://openalex.org/W2004807582","https://openalex.org/W2012522536","https://openalex.org/W2024547890","https://openalex.org/W2025207305","https://openalex.org/W2027081786","https://openalex.org/W2033066640","https://openalex.org/W2035698857","https://openalex.org/W2041178793","https://openalex.org/W2049228615","https://openalex.org/W2049312801","https://openalex.org/W2066397388","https://openalex.org/W2072063408","https://openalex.org/W2076268017","https://openalex.org/W2078454401","https://openalex.org/W2080006911","https://openalex.org/W2085210969","https://openalex.org/W2097360283","https://openalex.org/W2114895019","https://openalex.org/W2124611503","https://openalex.org/W2130761473","https://openalex.org/W2135046866","https://openalex.org/W2143022286","https://openalex.org/W2144898279","https://openalex.org/W2147169375","https://openalex.org/W2159443068","https://openalex.org/W2171033594","https://openalex.org/W2240895166","https://openalex.org/W2271554986","https://openalex.org/W2273597220","https://openalex.org/W2305001871","https://openalex.org/W2340721224","https://openalex.org/W2474848796","https://openalex.org/W2485765038","https://openalex.org/W2510190756","https://openalex.org/W2660868805","https://openalex.org/W2738087101","https://openalex.org/W2767004793","https://openalex.org/W2769156153","https://openalex.org/W2771875082","https://openalex.org/W2785799516","https://openalex.org/W2952060454","https://openalex.org/W2963253923","https://openalex.org/W2963302271","https://openalex.org/W2963888630","https://openalex.org/W2979609650","https://openalex.org/W2993310070","https://openalex.org/W2994727681","https://openalex.org/W2996495385","https://openalex.org/W3081731186","https://openalex.org/W3098069145","https://openalex.org/W3102144874","https://openalex.org/W3121757393","https://openalex.org/W3122367555","https://openalex.org/W3174108767","https://openalex.org/W4256427486","https://openalex.org/W4294541781","https://openalex.org/W4298330827","https://openalex.org/W4298876635","https://openalex.org/W6732445679"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W1907436174","https://openalex.org/W4240638320","https://openalex.org/W2490303147","https://openalex.org/W2903353145","https://openalex.org/W1589220756","https://openalex.org/W2910657826","https://openalex.org/W2481781685","https://openalex.org/W2993721864","https://openalex.org/W655185936"],"abstract_inverted_index":{"Nonstationary":[0],"non-Gaussian":[1,80],"spatial":[2,37,51,64,110,126],"data":[3,20,27,97],"are":[4,44],"common":[5],"in":[6,102,156],"many":[7],"disciplines,":[8],"including":[9],"climate":[10],"science,":[11],"ecology,":[12],"epidemiology,":[13],"and":[14,24,115,152,173],"social":[15],"sciences.":[16],"Examples":[17],"include":[18],"count":[19],"on":[21,28,70],"disease":[22],"incidence":[23],"binary":[25],"satellite":[26],"cloud":[29],"mask":[30],"(cloud/no-cloud).":[31],"Modeling":[32],"such":[33,96],"datasets":[34,81,147,177],"as":[35],"stationary":[36],"processes":[38,134],"can":[39,153],"be":[40,154],"unrealistic":[41],"since":[42],"they":[43],"collected":[45],"over":[46],"large":[47,79],"heterogeneous":[48],"domains":[49],"(i.e.,":[50],"behavior":[52],"differs":[53],"across":[54],"subregions).":[55],"Although":[56],"several":[57],"approaches":[58],"have":[59,67],"been":[60],"developed":[61],"for":[62,78,94,162],"nonstationary":[63,76,118],"models,":[65],"these":[66,87],"focused":[68],"primarily":[69],"Gaussian":[71],"responses.":[72],"In":[73],"addition,":[74],"fitting":[75],"models":[77,119],"is":[82],"computationally":[83],"prohibitive.":[84],"To":[85],"address":[86],"challenges,":[88],"we":[89,130],"propose":[90],"a":[91,121,136,158],"scalable":[92],"algorithm":[93],"modeling":[95],"by":[98],"leveraging":[99],"parallel":[100],"computing":[101,105],"modern":[103],"high-performance":[104],"systems.":[106],"We":[107,166],"partition":[108],"the":[109,132],"domain":[111],"into":[112],"disjoint":[113],"subregions":[114],"fit":[116],"locally":[117],"using":[120,135],"carefully":[122],"curated":[123],"set":[124],"of":[125],"basis":[127],"functions.":[128],"Then,":[129],"combine":[131],"local":[133],"novel":[137],"neighbor-based":[138],"weighting":[139],"scheme.":[140],"Our":[141],"approach":[142],"scales":[143],"well":[144],"to":[145,170],"massive":[146,175],"(e.g.,":[148],"2.7":[149],"million":[150],"samples)":[151],"implemented":[155],"nimble,":[157],"popular":[159],"software":[160],"environment":[161],"Bayesian":[163],"hierarchical":[164],"modeling.":[165],"demonstrate":[167],"our":[168],"method":[169],"simulated":[171],"examples":[172],"two":[174],"real-world":[176],"acquired":[178],"through":[179],"remote":[180],"sensing.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
