{"id":"https://openalex.org/W2950331846","doi":"https://doi.org/10.1109/percomw.2019.8730574","title":"NN-SAR: A Neural Network Approach for Spatial AutoRegression","display_name":"NN-SAR: A Neural Network Approach for Spatial AutoRegression","publication_year":2019,"publication_date":"2019-03-01","ids":{"openalex":"https://openalex.org/W2950331846","doi":"https://doi.org/10.1109/percomw.2019.8730574","mag":"2950331846"},"language":"en","primary_location":{"id":"doi:10.1109/percomw.2019.8730574","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percomw.2019.8730574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5012598887","display_name":"Pranita Dewan","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pranita Dewan","raw_affiliation_strings":["IBM T.J Watson Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J Watson Research Center","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011324325","display_name":"Raghu Ganti","orcid":"https://orcid.org/0000-0003-3658-7918"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raghu Ganti","raw_affiliation_strings":["IBM T.J Watson Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J Watson Research Center","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072108555","display_name":"Mudhakar Srivatsa","orcid":"https://orcid.org/0000-0002-5874-3750"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mudhakar Srivatsa","raw_affiliation_strings":["IBM T.J Watson Research Center"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J Watson Research Center","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011370879","display_name":"Sebastian Stein","orcid":"https://orcid.org/0000-0003-2858-8857"},"institutions":[{"id":"https://openalex.org/I43439940","display_name":"University of Southampton","ror":"https://ror.org/01ryk1543","country_code":"GB","type":"education","lineage":["https://openalex.org/I43439940"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sebastian Stein","raw_affiliation_strings":["University of Southampton"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southampton","institution_ids":["https://openalex.org/I43439940"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":null,"first_page":"783","last_page":"789"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9821000099182129,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9821000099182129,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9448000192642212,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9337000250816345,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6886714696884155},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6109769940376282},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6064030528068542},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.4384724199771881},{"id":"https://openalex.org/keywords/vector-autoregression","display_name":"Vector autoregression","score":0.42173299193382263},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3945396840572357},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.18972435593605042},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12168249487876892}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6886714696884155},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6109769940376282},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6064030528068542},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.4384724199771881},{"id":"https://openalex.org/C133029050","wikidata":"https://www.wikidata.org/wiki/Q385593","display_name":"Vector autoregression","level":2,"score":0.42173299193382263},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3945396840572357},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.18972435593605042},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12168249487876892}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/percomw.2019.8730574","is_oa":false,"landing_page_url":"https://doi.org/10.1109/percomw.2019.8730574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.soton.ac.uk:446413","is_oa":false,"landing_page_url":"https://eprints.soton.ac.uk/446413/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401019","display_name":"ePrints Soton (University of Southampton)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I43439940","host_organization_name":"University of Southampton","host_organization_lineage":["https://openalex.org/I43439940"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference or Workshop Item"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338295","display_name":"Army Research Laboratory","ror":"https://ror.org/011hc8f90"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W956374238","https://openalex.org/W1536680647","https://openalex.org/W1901129140","https://openalex.org/W1967137980","https://openalex.org/W1968840994","https://openalex.org/W2002685456","https://openalex.org/W2028226037","https://openalex.org/W2063077402","https://openalex.org/W2064630666","https://openalex.org/W2068504926","https://openalex.org/W2081990052","https://openalex.org/W2088827030","https://openalex.org/W2088972316","https://openalex.org/W2100495367","https://openalex.org/W2116435618","https://openalex.org/W2118097920","https://openalex.org/W2136922672","https://openalex.org/W2143612262","https://openalex.org/W2163605009","https://openalex.org/W2172041548","https://openalex.org/W2530443992","https://openalex.org/W2592929672","https://openalex.org/W4402833014","https://openalex.org/W6677326919","https://openalex.org/W6677907805","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2171218219","https://openalex.org/W1972271943","https://openalex.org/W2150410159","https://openalex.org/W4327525404","https://openalex.org/W4287185323","https://openalex.org/W3150905897","https://openalex.org/W1520183331","https://openalex.org/W2734842993","https://openalex.org/W3196086590","https://openalex.org/W3197822338"],"abstract_inverted_index":{"Geographic":[0],"phenomena":[1,75],"such":[2],"as":[3,65,115],"weather,":[4],"pollution,":[5],"pollen":[6],"are":[7],"increasingly":[8],"monitored":[9],"through":[10],"deployments":[11],"of":[12,19,67,72,129,148],"wide-area":[13],"networked":[14],"sensors.":[15,59],"However,":[16],"the":[17,73,77,110,119,130,146,155],"coverage":[18],"these":[20],"sensors":[21],"is":[22,39,151],"limited":[23],"to":[24,32,40,52,124,137,154],"key":[25],"densely":[26],"populated":[27],"regions.":[28],"A":[29],"standard":[30],"approach":[31,150],"inferring":[33,53],"missing":[34,139],"spatial":[35,70],"and":[36,80,117],"temporal":[37],"values":[38,55],"use":[41,81],"regression.":[42],"In":[43],"this":[44,62],"paper,":[45],"we":[46],"present":[47,104],"a":[48,69,105],"new":[49],"approach,":[50],"NN-SAR,":[51],"spatiotemporal":[54],"from":[56,76],"existing":[57,78],"deployed":[58],"We":[60,103],"model":[61],"inference":[63],"problem":[64],"that":[66,145],"learning":[68,83],"representation":[71,128],"underlying":[74],"data":[79,96],"deep":[82],"based":[84],"auto-encoder":[85,120],"approach.":[86],"Classical":[87],"auto-encoders":[88],"learn":[89,125],"on":[90,163],"image":[91],"or":[92],"singular":[93],"time":[94],"series":[95],"without":[97],"taking":[98],"\u201cspatial\u201d":[99],"similarities":[100],"into":[101],"account.":[102],"novel":[106],"mechanism":[107],"for":[108],"encoding":[109],"spatially":[111],"distributed":[112],"sensor":[113,140],"readings":[114],"\u201cimages\u201d":[116],"apply":[118],"with":[121],"convolutional":[122],"layers":[123],"an":[126],"efficient":[127],"data,":[131],"which":[132],"can":[133],"then":[134],"be":[135],"used":[136],"infer":[138],"data.":[141],"Preliminary":[142],"results":[143],"indicate":[144],"performance":[147],"our":[149],"far":[152],"superior":[153],"state-of-the-art":[156],"Spatial":[157],"Auto-Regressive":[158],"(SAR)":[159],"models":[160],"by":[161],"20%":[162],"average.":[164]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}