{"id":"https://openalex.org/W4385489570","doi":"https://doi.org/10.1109/thms.2023.3296692","title":"A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks","display_name":"A User-Driven Sampling Model for Large-Scale Geographical Point Data Visualization via Convolutional Neural Networks","publication_year":2023,"publication_date":"2023-08-02","ids":{"openalex":"https://openalex.org/W4385489570","doi":"https://doi.org/10.1109/thms.2023.3296692"},"language":"en","primary_location":{"id":"doi:10.1109/thms.2023.3296692","is_oa":false,"landing_page_url":"https://doi.org/10.1109/thms.2023.3296692","pdf_url":null,"source":{"id":"https://openalex.org/S2476799526","display_name":"IEEE Transactions on Human-Machine Systems","issn_l":"2168-2291","issn":["2168-2291","2168-2305"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Human-Machine Systems","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/A5072175550","display_name":"Zhiguang Zhou","orcid":"https://orcid.org/0009-0001-4783-6863"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhiguang Zhou","raw_affiliation_strings":["School of Media and Design, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0001-4783-6863","affiliations":[{"raw_affiliation_string":"School of Media and Design, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113022045","display_name":"Fengling Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengling Zheng","raw_affiliation_strings":["School of Computer Science, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102943951","display_name":"Wen Jin","orcid":"https://orcid.org/0000-0002-5992-2531"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Wen","raw_affiliation_strings":["School of Media and Design, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Media and Design, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101855382","display_name":"Yuanyuan Chen","orcid":"https://orcid.org/0000-0002-0892-0771"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Chen","raw_affiliation_strings":["Zhejiang Carbon Neutral Innovation Institute, Zhejiang University of Technology, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang Carbon Neutral Innovation Institute, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100406096","display_name":"Xinyu Li","orcid":"https://orcid.org/0000-0002-1919-2327"},"institutions":[{"id":"https://openalex.org/I90727586","display_name":"Zhejiang University of Finance and Economics","ror":"https://ror.org/055vj5234","country_code":"CN","type":"education","lineage":["https://openalex.org/I90727586"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Li","raw_affiliation_strings":["School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China","institution_ids":["https://openalex.org/I90727586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100664249","display_name":"Yuhua Liu","orcid":"https://orcid.org/0000-0003-0600-4056"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhua Liu","raw_affiliation_strings":["School of Media and Design, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0600-4056","affiliations":[{"raw_affiliation_string":"School of Media and Design, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021510618","display_name":"Yigang Wang","orcid":"https://orcid.org/0000-0002-4131-2719"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yigang Wang","raw_affiliation_strings":["School of Media and Design, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-4131-2719","affiliations":[{"raw_affiliation_string":"School of Media and Design, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100344494","display_name":"Wei Chen","orcid":"https://orcid.org/0000-0002-8365-4741"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Chen","raw_affiliation_strings":["State Key Lab of CAD & CG, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8365-4741","affiliations":[{"raw_affiliation_string":"State Key Lab of CAD & CG, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5072175550"],"corresponding_institution_ids":["https://openalex.org/I50760025"],"apc_list":null,"apc_paid":null,"fwci":1.0589,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.79140784,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"53","issue":"5","first_page":"885","last_page":"894"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10799","display_name":"Data Visualization and Analytics","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9943000078201294,"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/T11106","display_name":"Data Management and Algorithms","score":0.987500011920929,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8250068426132202},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.597049355506897},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5721950531005859},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5631822943687439},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5351235866546631},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.5194896459579468},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4501872956752777},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.4325392246246338},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4270457327365875},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4107890725135803},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3998343050479889},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3373566269874573},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.29361647367477417},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2030142843723297},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.13435286283493042},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.13020476698875427},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10337668657302856}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8250068426132202},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.597049355506897},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5721950531005859},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5631822943687439},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5351235866546631},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.5194896459579468},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4501872956752777},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.4325392246246338},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4270457327365875},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4107890725135803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3998343050479889},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3373566269874573},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29361647367477417},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2030142843723297},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.13435286283493042},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.13020476698875427},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10337668657302856},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/thms.2023.3296692","is_oa":false,"landing_page_url":"https://doi.org/10.1109/thms.2023.3296692","pdf_url":null,"source":{"id":"https://openalex.org/S2476799526","display_name":"IEEE Transactions on Human-Machine Systems","issn_l":"2168-2291","issn":["2168-2291","2168-2305"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Human-Machine Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4869227432","display_name":null,"funder_award_id":"62277013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7178511812","display_name":null,"funder_award_id":"62177040","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8783846746","display_name":null,"funder_award_id":"62132017","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1985599301","https://openalex.org/W2025661610","https://openalex.org/W2048657872","https://openalex.org/W2068688638","https://openalex.org/W2087382273","https://openalex.org/W2088009038","https://openalex.org/W2137028279","https://openalex.org/W2486648592","https://openalex.org/W2793551774","https://openalex.org/W2805536438","https://openalex.org/W2896302278","https://openalex.org/W2949991997","https://openalex.org/W2950244811","https://openalex.org/W2958488638","https://openalex.org/W2964315040","https://openalex.org/W2969838481","https://openalex.org/W2998167534","https://openalex.org/W3041535480","https://openalex.org/W3092806605","https://openalex.org/W3118541802","https://openalex.org/W3195103661"],"related_works":["https://openalex.org/W4254879869","https://openalex.org/W3022576529","https://openalex.org/W2628526247","https://openalex.org/W2596401011","https://openalex.org/W2913569734","https://openalex.org/W4401519790","https://openalex.org/W2702570413","https://openalex.org/W3127229356","https://openalex.org/W2901284887","https://openalex.org/W4401575149"],"abstract_inverted_index":{"Numerous":[0],"sampling":[1,76,98,165,251],"strategies":[2],"have":[3],"been":[4],"proposed":[5],"to":[6,52,102,118,132,138,162,202,217,245],"reduce":[7],"the":[8,20,45,53,79,83,104,120,127,152,155,164,168,174,179,188,222,226,247,254],"visual":[9,80,114,213,260],"clutter":[10],"of":[11,22,47,82,113,123,154,167,182,228,249,256,262],"large-scale":[12,84,263],"geographical":[13,85,105],"point":[14,86,205,210,265],"data":[15,24,87,121,184,229],"visualization,":[16],"which":[17,49,158],"focus":[18],"on":[19,89,151,240],"preservation":[21,255],"original":[23,183],"features,":[25],"such":[26],"as":[27,191,193],"randomness,":[28],"spatial":[29,180],"distribution,":[30],"and":[31,37,60,110,186,212,220,231,236,259],"associated":[32],"relationship.":[33],"However,":[34],"user":[35,58,142,189,232,257],"preferences":[36,143,258],"demands":[38],"are":[39,116,144,159,243],"not":[40],"taken":[41],"into":[42,107],"account":[43],"in":[44,65,126,253],"course":[46],"sampling,":[48,211],"will":[50,177],"lead":[51],"sampled":[54,175,223],"results":[55],"deviating":[56],"from":[57,225],"requirements":[59],"impede":[61],"personalized":[62],"geospatial":[63,264],"analysis":[64,230],"specific":[66],"application":[67],"scenarios.":[68],"In":[69,195],"this":[70],"article,":[71],"we":[72,197],"propose":[73],"a":[74,95,111,147,199],"user-driven":[75],"model":[77,99,149,252],"for":[78],"abstraction":[81],"based":[88,150,239],"convolutional":[90],"neural":[91],"networks":[92],"(CNN).":[93],"First,":[94],"blue":[96],"noise":[97],"is":[100],"applied":[101],"partition":[103],"space":[106],"local":[108,128,170],"areas,":[109,129],"set":[112],"interfaces":[115],"designed":[117],"present":[119],"features":[122],"those":[124],"points":[125,136,176,185,224],"enabling":[130],"users":[131,216],"visually":[133],"select":[134],"representative":[135,156],"according":[137],"their":[139],"requirements.":[140,233],"Then,":[141],"quantified":[145],"with":[146],"CNN":[148,207],"eigenvectors":[153],"points,":[157],"further":[160],"utilized":[161],"guide":[163],"courses":[166],"other":[169],"areas.":[171],"Thus,":[172],"all":[173],"retain":[178],"distribution":[181],"fulfill":[187],"preference":[190],"far":[192],"possible.":[194],"addition,":[196],"implement":[198],"visualization":[200],"framework":[201],"integrate":[203],"manual":[204],"selection,":[206],"training,":[208],"automatic":[209],"comparison,":[214],"allowing":[215],"easily":[218],"obtain":[219],"evaluate":[221],"perspectives":[227],"Quantitative":[234],"comparisons":[235],"case":[237],"studies":[238],"real-world":[241],"datasets":[242],"conducted":[244],"demonstrate":[246],"effectiveness":[248],"our":[250],"exploration":[261],"data.":[266]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2025-10-10T00:00:00"}