{"id":"https://openalex.org/W2914814410","doi":"https://doi.org/10.1145/3308558.3313704","title":"Learning Task-Specific City Region Partition","display_name":"Learning Task-Specific City Region Partition","publication_year":2019,"publication_date":"2019-05-13","ids":{"openalex":"https://openalex.org/W2914814410","doi":"https://doi.org/10.1145/3308558.3313704","mag":"2914814410"},"language":"en","primary_location":{"id":"doi:10.1145/3308558.3313704","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313704","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":"The World Wide Web Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3308558.3313704","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100740070","display_name":"Hongjian Wang","orcid":"https://orcid.org/0000-0002-7918-4548"},"institutions":[{"id":"https://openalex.org/I113979032","display_name":"Twitter (United States)","ror":"https://ror.org/04wt43v05","country_code":"US","type":"company","lineage":["https://openalex.org/I113979032"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hongjian Wang","raw_affiliation_strings":["Twitter Inc"],"affiliations":[{"raw_affiliation_string":"Twitter Inc","institution_ids":["https://openalex.org/I113979032"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060178403","display_name":"Porter Jenkins","orcid":"https://orcid.org/0009-0001-3213-8333"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Porter Jenkins","raw_affiliation_strings":["Penn State"],"affiliations":[{"raw_affiliation_string":"Penn State","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100777770","display_name":"Hua Wei","orcid":"https://orcid.org/0000-0002-3735-1635"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hua Wei","raw_affiliation_strings":["Penn State"],"affiliations":[{"raw_affiliation_string":"Penn State","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004882141","display_name":"Fei Wu","orcid":"https://orcid.org/0000-0003-2139-8807"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Wu","raw_affiliation_strings":["Penn State"],"affiliations":[{"raw_affiliation_string":"Penn State","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016516907","display_name":"Zhenhui Li","orcid":"https://orcid.org/0000-0001-7221-2588"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhenhui Li","raw_affiliation_strings":["Penn State"],"affiliations":[{"raw_affiliation_string":"Penn State","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100740070"],"corresponding_institution_ids":["https://openalex.org/I113979032"],"apc_list":null,"apc_paid":null,"fwci":0.9157,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.79737502,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3300","last_page":"3306"},"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":0.9997000098228455,"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":0.9997000098228455,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.994700014591217,"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/T10298","display_name":"Urban Transport and Accessibility","score":0.9866999983787537,"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/partition","display_name":"Partition (number theory)","score":0.7150323987007141},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6954620480537415},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.680274486541748},{"id":"https://openalex.org/keywords/real-estate","display_name":"Real estate","score":0.667822003364563},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5498440861701965},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5236230492591858},{"id":"https://openalex.org/keywords/demographics","display_name":"Demographics","score":0.4632592797279358},{"id":"https://openalex.org/keywords/sample-space","display_name":"Sample space","score":0.45480817556381226},{"id":"https://openalex.org/keywords/residential-real-estate","display_name":"Residential real estate","score":0.4180786907672882},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4104456305503845},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37433451414108276},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33880293369293213},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17379838228225708},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.07846713066101074}],"concepts":[{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.7150323987007141},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6954620480537415},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.680274486541748},{"id":"https://openalex.org/C82279013","wikidata":"https://www.wikidata.org/wiki/Q684740","display_name":"Real estate","level":2,"score":0.667822003364563},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5498440861701965},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5236230492591858},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.4632592797279358},{"id":"https://openalex.org/C100279318","wikidata":"https://www.wikidata.org/wiki/Q467440","display_name":"Sample space","level":2,"score":0.45480817556381226},{"id":"https://openalex.org/C2991739101","wikidata":"https://www.wikidata.org/wiki/Q674950","display_name":"Residential real estate","level":3,"score":0.4180786907672882},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4104456305503845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37433451414108276},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33880293369293213},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17379838228225708},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.07846713066101074},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3308558.3313704","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313704","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":"The World Wide Web Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3308558.3313704","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313704","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":"The World Wide Web Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1503398984","https://openalex.org/W1515851193","https://openalex.org/W1898216265","https://openalex.org/W1969865391","https://openalex.org/W1971402834","https://openalex.org/W1986534397","https://openalex.org/W1988134474","https://openalex.org/W2022490362","https://openalex.org/W2109594430","https://openalex.org/W2121863487","https://openalex.org/W2127048411","https://openalex.org/W2128366083","https://openalex.org/W2135194391","https://openalex.org/W2153207204","https://openalex.org/W2155968351","https://openalex.org/W2165178985","https://openalex.org/W2296041297","https://openalex.org/W2297059404","https://openalex.org/W2514525802","https://openalex.org/W2541205425","https://openalex.org/W2558600380","https://openalex.org/W2560674852","https://openalex.org/W2561568083","https://openalex.org/W2746553466","https://openalex.org/W2768009948","https://openalex.org/W2779342116","https://openalex.org/W2910952060","https://openalex.org/W2964098640","https://openalex.org/W4214717370"],"related_works":["https://openalex.org/W1486221226","https://openalex.org/W3121516497","https://openalex.org/W2376394614","https://openalex.org/W4236767682","https://openalex.org/W626760438","https://openalex.org/W2624876294","https://openalex.org/W4383955949","https://openalex.org/W2336092431","https://openalex.org/W2910240856","https://openalex.org/W2163258209"],"abstract_inverted_index":{"The":[0],"proliferation":[1],"of":[2,74,100,139,178],"publicly":[3],"accessible":[4],"urban":[5,12,40],"data":[6,25],"provide":[7],"new":[8,98],"insights":[9],"on":[10,80,161],"various":[11],"tasks.":[13],"A":[14],"frequently":[15],"used":[16],"approach":[17],"is":[18,121],"to":[19,35,106,174],"treat":[20],"each":[21],"region":[22,103],"as":[23],"a":[24,29,97,112,115,148],"sample":[26],"and":[27,44,170],"build":[28],"model":[30],"over":[31],"all":[32],"the":[33,37,45,108,132,155,176],"regions":[34,75],"observe":[36],"correlations":[38],"between":[39],"features":[41],"(e.g.,":[42,48,64,82],"demographics)":[43],"target":[46],"variable":[47],"crime":[49,84,168],"count).":[50],"To":[51,130],"define":[52],"regions,":[53],"most":[54],"existing":[55],"studies":[56],"use":[57],"fixed":[58],"grids":[59],"or":[60,67],"pre-defined":[61],"administrative":[62],"boundaries":[63],"census":[65],"tracts":[66],"community":[68],"areas).":[69],"In":[70,92],"reality,":[71],"however,":[72],"definitions":[73],"should":[76],"be":[77],"different":[78],"depending":[79],"tasks":[81],"regional":[83],"count":[85,169],"prediction":[86],"vs.":[87],"real":[88,163,171],"estate":[89,172],"prices":[90],"estimation).":[91],"this":[93,120],"paper,":[94],"we":[95,134],"propose":[96,147],"problem":[99,125],"task-specific":[101],"city":[102,113],"partitioning,":[104],"aiming":[105],"find":[107],"best":[109],"partition":[110],"in":[111,165],"w.r.t.":[114],"given":[116],"task.":[117],"We":[118,145,158],"prove":[119],"an":[122],"NP-hard":[123],"search":[124,156],"with":[126],"no":[127],"trivial":[128],"solution.":[129],"learn":[131],"partition,":[133],"first":[135],"study":[136],"two":[137,162],"variants":[138],"Markov":[140],"Chain":[141],"Monte":[142],"Carlo":[143],"(MCMC).":[144],"further":[146],"reinforcement":[149],"learning":[150],"scheme":[151],"for":[152],"effective":[153],"sampling":[154],"space.":[157],"conduct":[159],"experiments":[160],"datasets":[164],"Chicago":[166],"(i.e.,":[167],"price)":[173],"demonstrate":[175],"effectiveness":[177],"our":[179],"proposed":[180],"method.":[181]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
