{"id":"https://openalex.org/W4318147438","doi":"https://doi.org/10.1109/bigdata55660.2022.10020401","title":"Using Graph Neural Networks to Investigate the Relationship Between the Socioeconomic Factors and Emergency Medical Service (EMS) Median Response Time in New York City","display_name":"Using Graph Neural Networks to Investigate the Relationship Between the Socioeconomic Factors and Emergency Medical Service (EMS) Median Response Time in New York City","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147438","doi":"https://doi.org/10.1109/bigdata55660.2022.10020401"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020401","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020401","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-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/A5055253153","display_name":"Thilanka Munasinghe","orcid":"https://orcid.org/0000-0002-0911-750X"},"institutions":[{"id":"https://openalex.org/I1282113950","display_name":"United States Department of Homeland Security","ror":"https://ror.org/00jyr0d86","country_code":"US","type":"funder","lineage":["https://openalex.org/I1282113950"]},{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Thilanka Munasinghe","raw_affiliation_strings":["University at Albany,College of Emergency Preparedness Homeland Security and Cybersecurity,New York,U.S.A","College of Emergency Preparedness Homeland Security and Cybersecurity, University at Albany, New York, U.S.A"],"affiliations":[{"raw_affiliation_string":"University at Albany,College of Emergency Preparedness Homeland Security and Cybersecurity,New York,U.S.A","institution_ids":["https://openalex.org/I1282113950"]},{"raw_affiliation_string":"College of Emergency Preparedness Homeland Security and Cybersecurity, University at Albany, New York, U.S.A","institution_ids":["https://openalex.org/I392282"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054771021","display_name":"Brandon Behlendorf","orcid":"https://orcid.org/0000-0003-4817-0781"},"institutions":[{"id":"https://openalex.org/I1282113950","display_name":"United States Department of Homeland Security","ror":"https://ror.org/00jyr0d86","country_code":"US","type":"funder","lineage":["https://openalex.org/I1282113950"]},{"id":"https://openalex.org/I392282","display_name":"University at Albany, State University of New York","ror":"https://ror.org/012zs8222","country_code":"US","type":"education","lineage":["https://openalex.org/I392282"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brandon Behlendorf","raw_affiliation_strings":["University at Albany,College of Emergency Preparedness Homeland Security and Cybersecurity,New York,U.S.A","College of Emergency Preparedness Homeland Security and Cybersecurity, University at Albany, New York, U.S.A"],"affiliations":[{"raw_affiliation_string":"University at Albany,College of Emergency Preparedness Homeland Security and Cybersecurity,New York,U.S.A","institution_ids":["https://openalex.org/I1282113950"]},{"raw_affiliation_string":"College of Emergency Preparedness Homeland Security and Cybersecurity, University at Albany, New York, U.S.A","institution_ids":["https://openalex.org/I392282"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5055253153"],"corresponding_institution_ids":["https://openalex.org/I1282113950","https://openalex.org/I392282"],"apc_list":null,"apc_paid":null,"fwci":0.8125,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.73720137,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"6781","last_page":"6783"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9740999937057495,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9740999937057495,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11095","display_name":"Emergency and Acute Care Studies","score":0.9490000009536743,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10298","display_name":"Urban Transport and Accessibility","score":0.923799991607666,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"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/computer-science","display_name":"Computer science","score":0.6257590651512146},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.505978524684906},{"id":"https://openalex.org/keywords/socioeconomic-status","display_name":"Socioeconomic status","score":0.5018978118896484},{"id":"https://openalex.org/keywords/census","display_name":"Census","score":0.4219781458377838},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.36115989089012146},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.3483259677886963},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.20092186331748962},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14783906936645508},{"id":"https://openalex.org/keywords/environmental-health","display_name":"Environmental health","score":0.1217317283153534}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6257590651512146},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.505978524684906},{"id":"https://openalex.org/C147077947","wikidata":"https://www.wikidata.org/wiki/Q1515895","display_name":"Socioeconomic status","level":3,"score":0.5018978118896484},{"id":"https://openalex.org/C52130261","wikidata":"https://www.wikidata.org/wiki/Q39825","display_name":"Census","level":3,"score":0.4219781458377838},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.36115989089012146},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.3483259677886963},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.20092186331748962},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14783906936645508},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.1217317283153534}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020401","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020401","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"No poverty","id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1971541813","https://openalex.org/W2041967777","https://openalex.org/W2558748708","https://openalex.org/W2751808960","https://openalex.org/W2756203131","https://openalex.org/W2809418595","https://openalex.org/W2901375380","https://openalex.org/W2962711740","https://openalex.org/W2963757395","https://openalex.org/W2963988117","https://openalex.org/W3035678942","https://openalex.org/W3098230582","https://openalex.org/W3103720336","https://openalex.org/W3119488854","https://openalex.org/W3152893301","https://openalex.org/W4210243639","https://openalex.org/W4220980558","https://openalex.org/W4289262485","https://openalex.org/W4294558607","https://openalex.org/W6738964360","https://openalex.org/W6743410771","https://openalex.org/W6748856961","https://openalex.org/W6754929296","https://openalex.org/W6763057493","https://openalex.org/W6785059380"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2128472366","https://openalex.org/W621243299","https://openalex.org/W5594354","https://openalex.org/W2601163983","https://openalex.org/W4244351752","https://openalex.org/W2364090708","https://openalex.org/W1512152715","https://openalex.org/W2089958248","https://openalex.org/W2289283781"],"abstract_inverted_index":{"In":[0],"this":[1,314],"working-in-progress":[2],"study,":[3],"we":[4,80,108,303],"plan":[5,109],"to":[6,12,39,110,319],"use":[7,111],"Graph":[8,310],"Neural":[9,311],"Networks":[10,312],"(GNNs)":[11],"investigate":[13],"whether":[14,83],"and":[15,59,125,152,167,188,204,215,225,238,263,283],"how":[16,302],"socio-demographic":[17,293],"factors":[18,99,139,278],"influence":[19,279],"p":[20],"redicted":[21],"r":[22],"esponse":[23],"t":[24,273],"imes":[25],"of":[26,37,61,75,87,90,106,121,154,253,295,309,316],"EMS":[27,42,91,112,280],"services":[28],"in":[29,66,207,288,313],"New":[30,122],"York":[31,123],"City":[32],"(NYC).":[33],"Currently,":[34],"the":[35,41,70,84,118,128,141,155,163,199,218,232,235,243,292,297,306,329],"application":[36],"GNNs":[38],"predict":[40],"response":[43,92,175,265,281,289],"time":[44,93,176,266],"is":[45,173,177,250],"relatively":[46],"novel.":[47],"Operational":[48],"models,":[49],"which":[50,249],"prioritize":[51],"task-specific":[52],"features":[53,182,191,294],"(such":[54,183],"as":[55,145,184,247],"call":[56,200,260,298],"priority":[57],"level":[58],"day/time":[60],"incident)":[62],"have":[63],"been":[64],"deployed":[65],"several":[67],"contexts":[68],"around":[69],"world.":[71],"Leveraging":[72],"unique":[73],"capabilities":[74],"different":[76,104,180],"emergent":[77],"GNN":[78],"architectures,":[79],"will":[81],"evaluate":[82],"predictive":[85],"accuracy":[86],"operational":[88,181],"models":[89],"are":[94,100,192,217,240,286],"improved":[95],"when":[96],"neighborhood-level":[97],"socio-economic":[98,138],"included.":[101],"Focusing":[102],"on":[103],"neighborhoods":[105],"NYC,":[107],"Incident":[113],"Dispatch":[114],"Data":[115,131],"obtained":[116,161],"from":[117,162],"Fire":[119],"Department":[120],"(FDNY)":[124],"accessed":[126],"through":[127],"NYC":[129],"Open":[130],"Portal.":[132],"We":[133,196,300],"couple":[134],"incident":[135],"data":[136],"with":[137,179],"at":[140],"neighborhood":[142],"level,":[143,151],"such":[144],"average":[146],"income,":[147],"population":[148,156],"density,":[149],"poverty":[150],"percentage":[153],"speaking":[157],"a":[158,208,251,268],"second":[159],"language,":[160],"U.S":[164],"Census":[165],"Bureau":[166],"other":[168],"sources.":[169],"Our":[170],"starting":[171],"assumption":[172],"that":[174,189,276,284],"associated":[178],"Call":[185],"Priority":[186,201,222,261],"Level)":[187],"these":[190],"consistent":[193],"across":[194],"neighborhoods.":[195],"then":[197],"represent":[198],"Level":[202,262],"Set":[203,206],"Neighborhood":[205],"graph":[209],"G":[210],"=":[211],"(U,V,E).":[212],"The":[213,229],"U":[214,237,254],"V":[216,239],"node":[219],"sets":[220],"for":[221],"Levels":[223],"(PLs)":[224],"Neighborhoods":[226],"(NBs),":[227],"respectively.":[228],"relationships":[230,258],"or":[231],"links":[233],"between":[234,259],"nodes":[236],"represented":[241],"by":[242,291,328],"edge":[244],"set":[245],"denoted":[246],"E,":[248],"subset":[252],"\u00d7":[255],"V.":[256],"If":[257],"median":[264],"fluctuate":[267],"cross":[269],"n":[270],"eighborhoods,":[271],"i":[272],"s":[274],"uggests":[275],"extra-operational":[277],"times,":[282],"there":[285],"inequities":[287],"shaped":[290],"where":[296],"originated.":[299],"explore":[301],"can":[304],"leverage":[305],"novel":[307],"techniques":[308,327],"field":[315],"study":[317],"compared":[318],"previous":[320],"work":[321],"done":[322],"using":[323],"traditional":[324],"machine":[325],"learning":[326],"research":[330],"community.":[331]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}