{"id":"https://openalex.org/W2041866129","doi":"https://doi.org/10.1080/136588198241897","title":"Mean-variance analysis of the performance of spatial ordering methods","display_name":"Mean-variance analysis of the performance of spatial ordering methods","publication_year":1998,"publication_date":"1998-05-01","ids":{"openalex":"https://openalex.org/W2041866129","doi":"https://doi.org/10.1080/136588198241897","mag":"2041866129"},"language":"en","primary_location":{"id":"doi:10.1080/136588198241897","is_oa":false,"landing_page_url":"https://doi.org/10.1080/136588198241897","pdf_url":null,"source":{"id":"https://openalex.org/S4210181446","display_name":"International Journal of Geographical Information Systems","issn_l":"0269-3798","issn":["0269-3798","1362-3087"],"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"International Journal of Geographical Information Science","raw_type":"journal-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/A5077956826","display_name":"Akhil Kumar","orcid":"https://orcid.org/0000-0003-3572-3789"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"AKHIL KUMAR","raw_affiliation_strings":["Supply Chain and Information Systems"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Supply Chain and Information Systems","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5077956826"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.15470774,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"12","issue":"3","first_page":"269","last_page":"289"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10757","display_name":"Geographic Information Systems Studies","score":0.9749000072479248,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.668361485004425},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6597492694854736},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5831875801086426},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.542584240436554},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.48379772901535034},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.47268444299697876},{"id":"https://openalex.org/keywords/sample-variance","display_name":"Sample variance","score":0.4454447329044342},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.44282567501068115},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.43787914514541626},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4183228015899658},{"id":"https://openalex.org/keywords/spiral","display_name":"Spiral (railway)","score":0.4115482568740845},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3821770250797272},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3600791096687317},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23673087358474731}],"concepts":[{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.668361485004425},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6597492694854736},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5831875801086426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.542584240436554},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.48379772901535034},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.47268444299697876},{"id":"https://openalex.org/C2993021520","wikidata":"https://www.wikidata.org/wiki/Q175199","display_name":"Sample variance","level":3,"score":0.4454447329044342},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.44282567501068115},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.43787914514541626},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4183228015899658},{"id":"https://openalex.org/C174128100","wikidata":"https://www.wikidata.org/wiki/Q846907","display_name":"Spiral (railway)","level":2,"score":0.4115482568740845},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3821770250797272},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3600791096687317},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23673087358474731},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/136588198241897","is_oa":false,"landing_page_url":"https://doi.org/10.1080/136588198241897","pdf_url":null,"source":{"id":"https://openalex.org/S4210181446","display_name":"International Journal of Geographical Information Systems","issn_l":"0269-3798","issn":["0269-3798","1362-3087"],"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"International Journal of Geographical Information Science","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.139.3571","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.3571","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cob.ohio-state.edu/~wmuhanna/pdf/SpatialOrdering-IJGIS98.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1493714695","https://openalex.org/W1494491238","https://openalex.org/W1981561414","https://openalex.org/W1994669411","https://openalex.org/W2001917688","https://openalex.org/W2002168691","https://openalex.org/W2008196645","https://openalex.org/W2015190123","https://openalex.org/W2031939633","https://openalex.org/W2060454056","https://openalex.org/W2067060789","https://openalex.org/W2074429597","https://openalex.org/W2096137215","https://openalex.org/W2118269922","https://openalex.org/W2128288949","https://openalex.org/W2149173084","https://openalex.org/W2165558283"],"related_works":["https://openalex.org/W4290802965","https://openalex.org/W97789383","https://openalex.org/W3087516072","https://openalex.org/W2727156679","https://openalex.org/W1570799877","https://openalex.org/W2057598446","https://openalex.org/W2034778756","https://openalex.org/W3122470096","https://openalex.org/W2131347664","https://openalex.org/W2744007692"],"abstract_inverted_index":{"Geographical":[0],"Information":[1],"Systems":[2],"(GIS)":[3],"involve":[4],"the":[5,13,39,47,51,61,81,85,90,102,110,116,150,154,158,168,176,181,185,196,203,207,257],"manipulation":[6],"of":[7,15,41,83,93,101,108,115,118,121,134,148,174,198,259],"large":[8],"spatial":[9,44,95],"data":[10,24],"sets,":[11],"and":[12,50,57,112,138,160,187,202,209,235,249],"performance":[14,40,92,100,217,258],"these":[16,23,216],"systems":[17],"is":[18,104,180],"often":[19],"determined":[20],"by":[21,184],"how":[22],"sets":[25],"are":[26,157,230],"organized":[27],"on":[28,161],"secondary":[29],"storage":[30],"(disk).":[31],"This":[32],"paper":[33,79],"describes":[34],"a":[35,131,135,145],"simulation":[36],"study":[37,195],"investigating":[38],"two":[42,170],"non-recursive":[43],"clustering":[45,76,99],"methods":[46,53,103],"Inverted":[48],"Naive":[49],"Spiral":[52,155,186],"in":[54,69,106,190],"extensive":[55],"detail":[56],"comparing":[58],"them":[59],"with":[60],"Hilbert":[62,188],"fractal":[63],"method":[64,156,179],"that":[65,125,191,228],"has":[66,253],"been":[67],"shown":[68],"previous":[70,241],"studies":[71,242],"to":[72,129,237],"outperform":[73],"other":[74,169],"recursive":[75],"methods.":[77,97,171],"The":[78,98,140],"highlights":[80],"importance":[82],"analysing":[84],"sample":[86],"variance":[87,113],"when":[88],"evaluating":[89],"relative":[91],"various":[94],"ordering":[96],"examined":[105,245],"terms":[107,173],"both":[109,233],"mean":[111,151],"values":[114,152],"number":[117],"clusters":[119],"(runs":[120],"consecutive":[122],"disk":[123],"blocks)":[124],"must":[126],"be":[127],"accessed":[128],"retrieve":[130],"query":[132,200],"region":[133],"given":[136],"size":[137,201],"orientation.":[139],"results":[141,218],"show":[142],"that,":[143],"for":[144,153,167,212,222,232,256],"blocking":[146,224],"factor":[147],"1,":[149],"best,":[159],"average,":[162],"about":[163],"30%":[164],"better":[165],"than":[166],"In":[172],"variance,":[175],"inverted":[177],"naive":[178],"best":[182],"followed":[183],"methods,":[189],"order.":[192],"We":[193],"also":[194,244,250],"impact":[197],"varying":[199],"skew":[204],"ratio":[205],"(between":[206],"X":[208],"Y":[210],"dimensions)":[211],"each":[213],"method.":[214],"While":[215],"do":[219],"not":[220],"generalize":[221],"higher":[223],"factors,":[225],"we":[226],"believe":[227],"they":[229],"useful":[231],"researchers":[234],"practitioners":[236],"know":[238],"because":[239,251],"several":[240],"have":[243],"this":[246],"special":[247],"case,":[248],"it":[252],"important":[254],"implications":[255],"GIS":[260],"applications.":[261]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
