{"id":"https://openalex.org/W7154069620","doi":"https://doi.org/10.1145/3772318.3791244","title":"Redundant is Not Redundant: Automating Efficient Categorical Palettes Design Unifying Color &amp; Shape Encodings with CatPAW","display_name":"Redundant is Not Redundant: Automating Efficient Categorical Palettes Design Unifying Color &amp; Shape Encodings with CatPAW","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154069620","doi":"https://doi.org/10.1145/3772318.3791244"},"language":null,"primary_location":{"id":"doi:10.1145/3772318.3791244","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3772318.3791244","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3772318.3791244","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080359624","display_name":"Chin Tseng","orcid":"https://orcid.org/0000-0003-2025-0755"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chin Tseng","raw_affiliation_strings":["University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA"],"raw_orcid":"https://orcid.org/0000-0003-2025-0755","affiliations":[{"raw_affiliation_string":"University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100379697","display_name":"Arran Zeyu Wang","orcid":"https://orcid.org/0000-0002-7491-7570"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arran Zeyu Wang","raw_affiliation_strings":["University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA"],"raw_orcid":"https://orcid.org/0000-0002-7491-7570","affiliations":[{"raw_affiliation_string":"University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032598257","display_name":"Ghulam Jilani Quadri","orcid":"https://orcid.org/0000-0002-8054-5048"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ghulam Jilani Quadri","raw_affiliation_strings":["School of Computer Science, University of Oklahoma, Norman, Oklahoma, USA"],"raw_orcid":"https://orcid.org/0000-0002-8054-5048","affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Oklahoma, Norman, Oklahoma, USA","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056903170","display_name":"Danielle Albers Szafir","orcid":"https://orcid.org/0000-0003-3634-8597"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danielle Albers Szafir","raw_affiliation_strings":["University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA"],"raw_orcid":"https://orcid.org/0000-0003-3634-8597","affiliations":[{"raw_affiliation_string":"University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53005563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9354000091552734,"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":0.9354000091552734,"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/T12650","display_name":"Aesthetic Perception and Analysis","score":0.00559999980032444,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11094","display_name":"Face Recognition and Perception","score":0.004800000227987766,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.8356000185012817},{"id":"https://openalex.org/keywords/palette","display_name":"Palette (painting)","score":0.652899980545044},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6014999747276306},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5192000269889832},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4620000123977661},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4465999901294708},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.38179999589920044}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.8356000185012817},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6923999786376953},{"id":"https://openalex.org/C2779674283","wikidata":"https://www.wikidata.org/wiki/Q425548","display_name":"Palette (painting)","level":2,"score":0.652899980545044},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6014999747276306},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5192000269889832},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4620000123977661},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45320001244544983},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4465999901294708},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3799999952316284},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36559998989105225},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.33649998903274536},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3255000114440918},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2937000095844269},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C166955791","wikidata":"https://www.wikidata.org/wiki/Q629579","display_name":"Macro","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3772318.3791244","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3772318.3791244","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2602.06792","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2602.06792","pdf_url":"https://arxiv.org/pdf/2602.06792","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3772318.3791244","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3772318.3791244","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1512226365","display_name":null,"funder_award_id":"IIS-2320920","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3615387309","display_name":"CAREER: HCC: Developing Perceptually-Driven Tools for Estimating Visualization Effectiveness","funder_award_id":"2320920","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4019597227","display_name":null,"funder_award_id":"2127309","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":72,"referenced_works":["https://openalex.org/W884650706","https://openalex.org/W1830709933","https://openalex.org/W1949897911","https://openalex.org/W1971781829","https://openalex.org/W1974767305","https://openalex.org/W1977400967","https://openalex.org/W2000512760","https://openalex.org/W2013989095","https://openalex.org/W2017521531","https://openalex.org/W2020919587","https://openalex.org/W2026678302","https://openalex.org/W2030246490","https://openalex.org/W2031843365","https://openalex.org/W2037894060","https://openalex.org/W2043523210","https://openalex.org/W2054901814","https://openalex.org/W2058199791","https://openalex.org/W2095620196","https://openalex.org/W2112179747","https://openalex.org/W2114566476","https://openalex.org/W2117470435","https://openalex.org/W2127868335","https://openalex.org/W2135415614","https://openalex.org/W2137167595","https://openalex.org/W2140504904","https://openalex.org/W2145928901","https://openalex.org/W2150085383","https://openalex.org/W2163965546","https://openalex.org/W2293090538","https://openalex.org/W2300653232","https://openalex.org/W2339160999","https://openalex.org/W2511469011","https://openalex.org/W2529842378","https://openalex.org/W2605940686","https://openalex.org/W2610760451","https://openalex.org/W2611374796","https://openalex.org/W2733332118","https://openalex.org/W2751344272","https://openalex.org/W2752662042","https://openalex.org/W2753766497","https://openalex.org/W2781353212","https://openalex.org/W2788435232","https://openalex.org/W2810117028","https://openalex.org/W2884436096","https://openalex.org/W2891592782","https://openalex.org/W2897140761","https://openalex.org/W2907492257","https://openalex.org/W2942499426","https://openalex.org/W2969737323","https://openalex.org/W2969974087","https://openalex.org/W3010733272","https://openalex.org/W3013119608","https://openalex.org/W3016958626","https://openalex.org/W3035965352","https://openalex.org/W3093653605","https://openalex.org/W3185482315","https://openalex.org/W3190684581","https://openalex.org/W4200375399","https://openalex.org/W4213281020","https://openalex.org/W4244513086","https://openalex.org/W4297462941","https://openalex.org/W4311673272","https://openalex.org/W4327711707","https://openalex.org/W4361230615","https://openalex.org/W4378009585","https://openalex.org/W4379095643","https://openalex.org/W4388429267","https://openalex.org/W4389988591","https://openalex.org/W4392223457","https://openalex.org/W4392224055","https://openalex.org/W4396832750","https://openalex.org/W4402580345"],"related_works":[],"abstract_inverted_index":{"Colors":[0],"and":[1,36,54,88,140],"shapes":[2],"are":[3,42],"commonly":[4],"used":[5],"to":[6,18,23,114],"encode":[7],"categories":[8],"in":[9,69,96,130],"multi-class":[10],"scatterplots.":[11],"Designers":[12],"often":[13],"combine":[14],"the":[15,30,74,91],"two":[16],"channels":[17],"create":[19],"redundant":[20,51,98,137],"encodings,":[21],"aiming":[22],"enhance":[24],"class":[25],"distinctions.":[26],"However,":[27],"evidence":[28],"for":[29,38,77,93,119],"effectiveness":[31],"of":[32,127],"redundancy":[33,65],"remains":[34],"conflicted,":[35],"guidelines":[37],"constructing":[39],"effective":[40,120,136],"combinations":[41,139],"limited.":[43],"This":[44],"paper":[45],"presents":[46],"four":[47],"crowdsourced":[48],"experiments":[49],"evaluating":[50],"color\u2013shape":[52,138],"encodings":[53],"identifying":[55,135],"high-performing":[56],"configurations":[57],"across":[58],"different":[59],"category":[60],"numbers.":[61],"Results":[62],"show":[63],"that":[64,111],"significantly":[66],"improves":[67],"accuracy":[68],"assessing":[70],"class-level":[71],"correlations,":[72],"with":[73],"strongest":[75],"benefits":[76],"5\u20138":[78],"categories.":[79],"We":[80],"also":[81],"find":[82],"pronounced":[83],"interaction":[84],"effects":[85],"between":[86],"colors":[87],"shapes,":[89],"underscoring":[90],"need":[92],"careful":[94],"pairing":[95],"designing":[97],"encodings.":[99],"Drawing":[100],"on":[101],"these":[102,142],"findings,":[103],"we":[104],"introduce":[105],"a":[106,145],"categorical":[107,121,128],"palette":[108,147],"design":[109,148],"tool":[110],"enables":[112],"designers":[113],"construct":[115],"empirically":[116],"grounded":[117],"palettes":[118],"visualization.":[122],"Our":[123],"work":[124],"advances":[125],"understanding":[126],"perception":[129],"data":[131],"visualization":[132],"by":[133],"systematically":[134],"embedding":[141],"insights":[143],"into":[144],"practical":[146],"tool.":[149]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-14T00:00:00"}
