{"id":"https://openalex.org/W7116084562","doi":"https://doi.org/10.1145/3764912.3770815","title":"A Graph-based Deep Population Downscaling Model on Irregular Spatial Units","display_name":"A Graph-based Deep Population Downscaling Model on Irregular Spatial Units","publication_year":2025,"publication_date":"2025-11-03","ids":{"openalex":"https://openalex.org/W7116084562","doi":"https://doi.org/10.1145/3764912.3770815"},"language":null,"primary_location":{"id":"doi:10.1145/3764912.3770815","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3764912.3770815","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery","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":null,"display_name":"Meicheng Xiong","orcid":"https://orcid.org/0009-0005-6227-6541"},"institutions":[{"id":"https://openalex.org/I4210101327","display_name":"Twin Cities Orthopedics","ror":"https://ror.org/01en4s460","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210101327"]},{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Meicheng Xiong","raw_affiliation_strings":["University of Minnesota, Twin Cities, Minneapolis, USA"],"affiliations":[{"raw_affiliation_string":"University of Minnesota, Twin Cities, Minneapolis, USA","institution_ids":["https://openalex.org/I4210101327","https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":null,"display_name":"Di Zhu","orcid":"https://orcid.org/0000-0002-3237-6032"},"institutions":[{"id":"https://openalex.org/I4210101327","display_name":"Twin Cities Orthopedics","ror":"https://ror.org/01en4s460","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210101327"]},{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Di Zhu","raw_affiliation_strings":["University of Minnesota, Twin Cities, Minneapolis, USA"],"affiliations":[{"raw_affiliation_string":"University of Minnesota, Twin Cities, Minneapolis, USA","institution_ids":["https://openalex.org/I4210101327","https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130238516","https://openalex.org/I4210101327"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.62743347,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"37","last_page":"44"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11963","display_name":"Impact of Light on Environment and Health","score":0.696399986743927,"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"}},"topics":[{"id":"https://openalex.org/T11963","display_name":"Impact of Light on Environment and Health","score":0.696399986743927,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.06870000064373016,"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/T14509","display_name":"demographic modeling and climate adaptation","score":0.04820000007748604,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/downscaling","display_name":"Downscaling","score":0.8669000267982483},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.5724999904632568},{"id":"https://openalex.org/keywords/raster-graphics","display_name":"Raster graphics","score":0.5284000039100647},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4569000005722046},{"id":"https://openalex.org/keywords/raster-data","display_name":"Raster data","score":0.4162999987602234},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.37549999356269836},{"id":"https://openalex.org/keywords/multivariate-interpolation","display_name":"Multivariate interpolation","score":0.3720000088214874},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.3691999912261963},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.36579999327659607}],"concepts":[{"id":"https://openalex.org/C41156917","wikidata":"https://www.wikidata.org/wiki/Q682831","display_name":"Downscaling","level":3,"score":0.8669000267982483},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5724999904632568},{"id":"https://openalex.org/C181844469","wikidata":"https://www.wikidata.org/wiki/Q182270","display_name":"Raster graphics","level":2,"score":0.5284000039100647},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5174999833106995},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4569000005722046},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.42080000042915344},{"id":"https://openalex.org/C2692088","wikidata":"https://www.wikidata.org/wiki/Q182270","display_name":"Raster data","level":3,"score":0.4162999987602234},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.3919999897480011},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.3862999975681305},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.37549999356269836},{"id":"https://openalex.org/C203332170","wikidata":"https://www.wikidata.org/wiki/Q6334079","display_name":"Multivariate interpolation","level":3,"score":0.3720000088214874},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.3691999912261963},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3630000054836273},{"id":"https://openalex.org/C158739034","wikidata":"https://www.wikidata.org/wiki/Q1907114","display_name":"Metropolitan area","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C2777016058","wikidata":"https://www.wikidata.org/wiki/Q7574061","display_name":"Spatial distribution","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C158709400","wikidata":"https://www.wikidata.org/wiki/Q3578586","display_name":"Spatial ecology","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31029999256134033},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.2842999994754791},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C52079815","wikidata":"https://www.wikidata.org/wiki/Q7229808","display_name":"Population model","level":3,"score":0.2662999927997589},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C2780408538","wikidata":"https://www.wikidata.org/wiki/Q3615217","display_name":"Ancillary data","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3764912.3770815","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3764912.3770815","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1543801710","https://openalex.org/W1966086989","https://openalex.org/W1983720606","https://openalex.org/W1986670971","https://openalex.org/W1990433983","https://openalex.org/W1994047751","https://openalex.org/W2057442840","https://openalex.org/W2058860308","https://openalex.org/W2080504767","https://openalex.org/W2104280368","https://openalex.org/W2131120753","https://openalex.org/W2144157522","https://openalex.org/W2144314902","https://openalex.org/W2979827618","https://openalex.org/W2985193458","https://openalex.org/W3001755538","https://openalex.org/W3040323135","https://openalex.org/W3183565977","https://openalex.org/W4309711566","https://openalex.org/W4319832761","https://openalex.org/W4390807498","https://openalex.org/W4390900833","https://openalex.org/W4407677158"],"related_works":[],"abstract_inverted_index":{"Estimating":[0],"high-resolution":[1],"population":[2,22,88,160],"distribution":[3],"has":[4,10],"been":[5],"a":[6,85],"critical":[7],"challenge":[8],"and":[9,16,46,54,66,76,103,124,152],"garnered":[11],"substantial":[12],"attention":[13,100],"in":[14,40,137,157],"research":[15],"applications.":[17],"Current":[18],"downscaling":[19,89],"practices":[20],"of":[21,127],"data,":[23],"from":[24,107,162],"coarse":[25],"source":[26,122,132],"units":[27,118,130],"to":[28,150,166],"fine":[29],"target":[30,117,129],"units,":[31],"generally":[32],"framed":[33],"as":[34,74],"areal":[35],"interpolation":[36],"tasks,":[37],"highlighting":[38],"models":[39,58],"traditional":[41],"dasymetric":[42],"mapping,":[43],"machine":[44],"learning,":[45],"deep":[47,87],"learning":[48],"approaches.":[49],"Despite":[50],"high":[51],"numerical":[52],"accuracy":[53],"spatiotemporal":[55],"resolution,":[56],"these":[57],"are":[59,67],"primarily":[60],"based":[61],"on":[62,97],"raster":[63],"spatial":[64,71],"structure":[65],"designed":[68],"for":[69],"regular":[70],"supports,":[72],"such":[73],"grids":[75],"pixels.":[77],"In":[78],"this":[79],"study,":[80],"we":[81],"propose":[82],"X-Patch":[83,146],"GAT,":[84],"graph-based":[86],"model":[90,111],"operating":[91],"across":[92,131],"irregular":[93],"sourcing":[94],"boundaries.":[95],"Drawing":[96],"the":[98,104,114,120,125,138],"graph":[99],"network":[101],"framework":[102],"patching":[105],"idea":[106],"remote":[108],"sensing,":[109],"our":[110],"captures":[112],"both":[113],"inter-relationships":[115],"among":[116],"within":[119],"same":[121],"unit":[123],"cross-relationships":[126],"adjacent":[128],"units.":[133],"A":[134],"case":[135],"study":[136],"Twin":[139],"Cities":[140],"Metropolitan":[141],"Area,":[142],"U.S.,":[143],"demonstrated":[144],"that":[145],"GAT":[147],"is":[148],"easy":[149],"train":[151],"can":[153],"outperform":[154],"all":[155],"baselines":[156],"deriving":[158],"downscaled":[159],"data":[161],"census":[163],"block":[164],"groups":[165],"blocks.":[167]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-12-19T00:00:00"}
