{"id":"https://openalex.org/W2896679662","doi":"https://doi.org/10.2312/vmv.20181261","title":"Correlated Point Sampling for Geospatial Scalar Field Visualization","display_name":"Correlated Point Sampling for Geospatial Scalar Field Visualization","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2896679662","doi":"https://doi.org/10.2312/vmv.20181261","mag":"2896679662"},"language":"en","primary_location":{"id":"doi:10.2312/vmv.20181261","is_oa":true,"landing_page_url":"https://doi.org/10.2312/vmv.20181261","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.2312/vmv.20181261","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059768994","display_name":"Riccardo Roveri","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Roveri, Riccardo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055750487","display_name":"Dirk J. Lehmann","orcid":"https://orcid.org/0000-0002-8089-4408"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lehmann, Dirk J.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033076979","display_name":"Markus Gro\u00df","orcid":"https://orcid.org/0009-0003-9324-779X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gross, Markus","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5088860259","display_name":"Tobias G\u00fcnther","orcid":"https://orcid.org/0000-0002-3020-0930"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G\u00fcnther, Tobias","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5059768994"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.08476367,"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":"119","last_page":"126"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13067","display_name":"Geological Modeling and Analysis","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13067","display_name":"Geological Modeling and Analysis","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12698","display_name":"3D Modeling in Geospatial Applications","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9474999904632568,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.7704365849494934},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.6056753396987915},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5956035256385803},{"id":"https://openalex.org/keywords/scalar","display_name":"Scalar (mathematics)","score":0.5066936612129211},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.41180557012557983},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3250446021556854},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.20077750086784363},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.19987186789512634},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18054646253585815},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.12727299332618713},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.09220904111862183}],"concepts":[{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.7704365849494934},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.6056753396987915},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5956035256385803},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.5066936612129211},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.41180557012557983},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3250446021556854},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.20077750086784363},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.19987186789512634},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18054646253585815},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.12727299332618713},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.09220904111862183},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.2312/vmv.20181261","is_oa":true,"landing_page_url":"https://doi.org/10.2312/vmv.20181261","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"},{"id":"mag:2896679662","is_oa":false,"landing_page_url":"https://diglib.eg.org/handle/10.2312/vmv20181261","pdf_url":null,"source":{"id":"https://openalex.org/S4306421152","display_name":"Vision Modeling and Visualization","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Vision Modeling and Visualization","raw_type":null}],"best_oa_location":{"id":"doi:10.2312/vmv.20181261","is_oa":true,"landing_page_url":"https://doi.org/10.2312/vmv.20181261","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2886118084","https://openalex.org/W116126665","https://openalex.org/W2128495311","https://openalex.org/W2942071478","https://openalex.org/W223235667","https://openalex.org/W3025607236","https://openalex.org/W2070275453","https://openalex.org/W2511119594","https://openalex.org/W2351622305","https://openalex.org/W2159145062","https://openalex.org/W2368887565","https://openalex.org/W3210454413","https://openalex.org/W2675368045","https://openalex.org/W580482213","https://openalex.org/W1553666223","https://openalex.org/W2888854077","https://openalex.org/W2488583316","https://openalex.org/W2789530736","https://openalex.org/W2767213912","https://openalex.org/W3005939525"],"abstract_inverted_index":{"Multi-variate":[0],"visualizations":[1],"of":[2,8,38,62,110],"geospatial":[3,102,135],"data":[4,64,136],"often":[5],"use":[6,50],"combinations":[7],"different":[9,19],"visual":[10],"cues,":[11],"such":[12],"as":[13,156],"color":[14,154,171],"and":[15,87,105,161,180],"texture.":[16],"For":[17,46],"textures,":[18],"point":[20,39,80,98],"distributions":[21,148],"(blue":[22],"noise,":[23,84],"regular":[24,85,181],"grids,":[25],"etc.)":[26],"can":[27],"encode":[28,43],"nominal":[29],"data.":[30],"In":[31],"this":[32],"paper,":[33],"we":[34,49,68,95,106],"study":[35,72],"the":[36,47,57,76,92,108,111,117,121,163,169,187],"suitability":[37],"distribution":[40],"interpolation":[41],"to":[42,73,165,168],"quantitative":[44,63],"information.":[45],"interpolation,":[48],"a":[51,70,97,144],"texture":[52],"synthesis":[53],"algorithm,":[54],"which":[55,139],"paves":[56],"path":[58],"towards":[59],"an":[60],"encoding":[61],"using":[65],"points.":[66],"First,":[67],"conduct":[69],"user":[71,112,164],"perceptually":[74],"linearize":[75],"transitions":[77],"between":[78,177],"uniform":[79],"distributions,":[81],"including":[82],"blue":[83,178],"grids":[86,182],"hexagonal":[88],"grids.":[89],"Based":[90],"on":[91,132],"linearization":[93],"models,":[94],"implement":[96],"sampling-based":[99],"visualization":[100],"for":[101],"scalar":[103],"fields":[104],"assess":[107],"accuracy":[109],"perception":[113],"abilities":[114],"by":[115],"comparing":[116],"perceived":[118],"transition":[119,122],"with":[120,143,153],"expected":[123],"from":[124],"our":[125,130],"linearized":[126],"models.":[127],"We":[128,173],"illustrate":[129],"technique":[131],"several":[133],"real":[134],"sets,":[137],"in":[138,151],"users":[140],"identify":[141],"regions":[142],"certain":[145],"distribution.":[146],"Point":[147],"work":[149],"well":[150],"combination":[152],"data,":[155],"they":[157],"require":[158],"little":[159],"space":[160],"allow":[162],"see":[166],"through":[167],"underlying":[170],"maps.":[172],"found":[174],"that":[175],"interpolations":[176],"noise":[179],"worked":[183],"perceptively":[184],"best":[185],"among":[186],"tested":[188],"candidates.":[189]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}