{"id":"https://openalex.org/W4417299910","doi":"https://doi.org/10.48550/arxiv.2505.11645","title":"Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach","display_name":"Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach","publication_year":2025,"publication_date":"2025-05-16","ids":{"openalex":"https://openalex.org/W4417299910","doi":"https://doi.org/10.48550/arxiv.2505.11645"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2505.11645","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.11645","pdf_url":"https://arxiv.org/pdf/2505.11645","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2505.11645","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001350022","display_name":"Jinzhou Cao","orcid":"https://orcid.org/0000-0001-6201-3251"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Jinzhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103520304","display_name":"Xiangxu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiangxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069620316","display_name":"Jiashi Chen","orcid":"https://orcid.org/0000-0001-5514-1167"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiashi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022293237","display_name":"Wei Tu","orcid":"https://orcid.org/0000-0002-0255-4037"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tu, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016516907","display_name":"Zhenhui Li","orcid":"https://orcid.org/0000-0001-7221-2588"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhenhui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yang, Xindong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Xindong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001627190","display_name":"Tianhong Zhao","orcid":"https://orcid.org/0000-0002-9290-2049"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Tianhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100404067","display_name":"Qingquan Li","orcid":"https://orcid.org/0000-0002-2438-6046"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qingquan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.2872999906539917,"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":0.2872999906539917,"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/T10226","display_name":"Land Use and Ecosystem Services","score":0.1590999960899353,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.07079999893903732,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.5486999750137329},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5133000016212463},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.4803999960422516},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4763999879360199},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4731999933719635},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.4325999915599823},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.3781000077724457}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6132000088691711},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.5486999750137329},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5133000016212463},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.4803999960422516},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4763999879360199},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4731999933719635},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45730000734329224},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4255000054836273},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4242999851703644},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C49545453","wikidata":"https://www.wikidata.org/wiki/Q69883","display_name":"Urban planning","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2831999957561493}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2505.11645","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.11645","pdf_url":"https://arxiv.org/pdf/2505.11645","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},{"id":"doi:10.48550/arxiv.2505.11645","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2505.11645","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2505.11645","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.11645","pdf_url":"https://arxiv.org/pdf/2505.11645","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fine-grained":[0],"economic":[1,15,43,60,195,208],"mapping":[2],"through":[3,197],"urban":[4,199],"representation":[5,190],"learning":[6,31,56,191],"has":[7],"emerged":[8],"as":[9],"a":[10,80,86,120,203],"crucial":[11],"tool":[12],"for":[13,40,58,71,151,185,206],"evidence-based":[14],"decisions.":[16],"While":[17],"existing":[18,141],"methods":[19],"primarily":[20],"rely":[21],"on":[22],"supervised":[23,97],"or":[24],"unsupervised":[25],"approaches,":[26],"they":[27],"often":[28],"overlook":[29],"semi-supervised":[30,54],"in":[32,126,161],"data-scarce":[33],"scenarios":[34],"and":[35,102,116,149,155,163],"lack":[36],"unified":[37,121],"multi-task":[38,107],"frameworks":[39],"comprehensive":[41],"sectoral":[42,59],"analysis.":[44],"To":[45],"address":[46],"these":[47],"gaps,":[48],"we":[49],"propose":[50],"SemiGTX,":[51],"an":[52],"explainable":[53],"graph":[55,82],"framework":[57,63,192],"mapping.":[61],"The":[62],"is":[64],"designed":[65],"with":[66,94],"dedicated":[67],"fusion":[68],"encoding":[69],"modules":[70],"various":[72],"geospatial":[73],"data":[74,174,200],"modalities,":[75],"seamlessly":[76],"integrating":[77],"them":[78],"into":[79],"cohesive":[81],"structure.":[83],"It":[84],"introduces":[85],"semi-information":[87],"loss":[88],"function":[89],"that":[90],"combines":[91],"spatial":[92],"self-supervision":[93],"locally":[95],"masked":[96],"regression,":[98],"enabling":[99],"more":[100],"informative":[101],"effective":[103],"region":[104,131],"representations.":[105],"Through":[106],"learning,":[108],"SemiGTX":[109],"concurrently":[110],"maps":[111],"GDP":[112],"across":[113],"primary,":[114,153],"secondary,":[115],"tertiary":[117,156],"sectors":[118],"within":[119],"model.":[122],"Extensive":[123],"experiments":[124,160],"conducted":[125],"the":[127,135,152],"Pearl":[128],"River":[129],"Delta":[130],"of":[132,146],"China":[133],"demonstrate":[134],"model's":[136],"superior":[137],"performance":[138],"compared":[139],"to":[140],"methods,":[142],"achieving":[143],"R2":[144],"scores":[145],"0.93,":[147],"0.96,":[148],"0.94":[150],"secondary":[154],"sectors,":[157],"respectively.":[158],"Cross-regional":[159],"Beijing":[162],"Chengdu":[164],"further":[165],"illustrate":[166],"its":[167],"generality.":[168],"Systematic":[169],"analysis":[170],"reveals":[171],"how":[172],"different":[173],"modalities":[175],"influence":[176],"model":[177],"predictions,":[178],"enhancing":[179],"explainability":[180],"while":[181],"providing":[182,202],"valuable":[183],"insights":[184],"regional":[186,194],"development":[187],"planning.":[188],"This":[189],"advances":[193],"monitoring":[196],"diverse":[198],"integration,":[201],"robust":[204],"foundation":[205],"precise":[207],"forecasting.":[209]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
