{"id":"https://openalex.org/W4416334903","doi":"https://doi.org/10.3390/ijgi14110448","title":"Urban Grid Population Inflow Prediction via POI-Enhanced Conditional Diffusion with Dual-Dimensional Attention","display_name":"Urban Grid Population Inflow Prediction via POI-Enhanced Conditional Diffusion with Dual-Dimensional Attention","publication_year":2025,"publication_date":"2025-11-15","ids":{"openalex":"https://openalex.org/W4416334903","doi":"https://doi.org/10.3390/ijgi14110448"},"language":"en","primary_location":{"id":"doi:10.3390/ijgi14110448","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi14110448","pdf_url":"https://www.mdpi.com/2220-9964/14/11/448/pdf?version=1763189144","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2220-9964/14/11/448/pdf?version=1763189144","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000921508","display_name":"Zhiming Gui","orcid":"https://orcid.org/0000-0002-9677-4152"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiming Gui","raw_affiliation_strings":["College of Computer Science, Beijing University of Technology, Beijing 100124, China"],"raw_orcid":"https://orcid.org/0000-0002-9677-4152","affiliations":[{"raw_affiliation_string":"College of Computer Science, Beijing University of Technology, Beijing 100124, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061295258","display_name":"Y Zhong","orcid":"https://orcid.org/0009-0001-1622-5939"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanchao Zhong","raw_affiliation_strings":["College of Computer Science, Beijing University of Technology, Beijing 100124, China"],"raw_orcid":"https://orcid.org/0009-0001-1622-5939","affiliations":[{"raw_affiliation_string":"College of Computer Science, Beijing University of Technology, Beijing 100124, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027351570","display_name":"Zhenji Gao","orcid":"https://orcid.org/0009-0007-9106-039X"},"institutions":[{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"government","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I2799486974","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305","country_code":"CN","type":"other","lineage":["https://openalex.org/I2799486974"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhenji Gao","raw_affiliation_strings":["Integrated Natural Resources Survey Center, CGS, No. 55 Yard, Honglian South Road, Xicheng District, Beijing 100055, China","Technology Innovation Center of Geological Information Engineering of Ministry of Natural Resources, Beijing 100055, China"],"raw_orcid":"https://orcid.org/0009-0007-9106-039X","affiliations":[{"raw_affiliation_string":"Integrated Natural Resources Survey Center, CGS, No. 55 Yard, Honglian South Road, Xicheng District, Beijing 100055, China","institution_ids":["https://openalex.org/I2799486974"]},{"raw_affiliation_string":"Technology Innovation Center of Geological Information Engineering of Ministry of Natural Resources, Beijing 100055, China","institution_ids":["https://openalex.org/I211433327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027351570"],"corresponding_institution_ids":["https://openalex.org/I211433327","https://openalex.org/I2799486974"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31436085,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":"11","first_page":"448","last_page":"448"},"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":0.5091999769210815,"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":0.5091999769210815,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.12729999423027039,"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"}},{"id":"https://openalex.org/T11479","display_name":"Smart Cities and Technologies","score":0.06650000065565109,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inflow","display_name":"Inflow","score":0.6488000154495239},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6414999961853027},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.49720001220703125},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4846000075340271},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.4043999910354614},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.40119999647140503},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.3666999936103821}],"concepts":[{"id":"https://openalex.org/C2776132308","wikidata":"https://www.wikidata.org/wiki/Q11070850","display_name":"Inflow","level":2,"score":0.6488000154495239},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6414999961853027},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6096000075340271},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5684999823570251},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.49720001220703125},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4846000075340271},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.4043999910354614},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.40119999647140503},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3666999936103821},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.358599990606308},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3154999911785126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.304500013589859},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.30160000920295715},{"id":"https://openalex.org/C24552861","wikidata":"https://www.wikidata.org/wiki/Q2670177","display_name":"Data assimilation","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C49545453","wikidata":"https://www.wikidata.org/wiki/Q69883","display_name":"Urban planning","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2540000081062317}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/ijgi14110448","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi14110448","pdf_url":"https://www.mdpi.com/2220-9964/14/11/448/pdf?version=1763189144","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:34a0e1b5968b4411a2f70de1ff28fdca","is_oa":true,"landing_page_url":"https://doaj.org/article/34a0e1b5968b4411a2f70de1ff28fdca","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISPRS International Journal of Geo-Information, Vol 14, Iss 11, p 448 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/ijgi14110448","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi14110448","pdf_url":"https://www.mdpi.com/2220-9964/14/11/448/pdf?version=1763189144","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416334903.pdf","grobid_xml":"https://content.openalex.org/works/W4416334903.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1990444226","https://openalex.org/W2017755807","https://openalex.org/W2112738128","https://openalex.org/W2144499799","https://openalex.org/W2756203131","https://openalex.org/W2774117082","https://openalex.org/W2907492528","https://openalex.org/W2915288435","https://openalex.org/W2942843559","https://openalex.org/W2948978827","https://openalex.org/W2963608065","https://openalex.org/W3000301417","https://openalex.org/W3022643593","https://openalex.org/W3028997739","https://openalex.org/W3096831136","https://openalex.org/W3204937802","https://openalex.org/W4234722734","https://openalex.org/W4246587917","https://openalex.org/W4283319189","https://openalex.org/W4283650591","https://openalex.org/W4298204516","https://openalex.org/W4382316621","https://openalex.org/W4385399619","https://openalex.org/W4392523490","https://openalex.org/W4392896772","https://openalex.org/W4399546751","https://openalex.org/W4403053017"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,26,131],"of":[2,62,85,94,142],"urban":[3,45,56,119,152],"grid-scale":[4],"population":[5],"inflow":[6],"is":[7,79],"crucial":[8],"for":[9],"smart":[10],"city":[11],"management":[12],"and":[13,24,31,67,88,133,145],"emergency":[14],"response.":[15],"However,":[16],"existing":[17,127],"methods":[18,129],"struggle":[19],"to":[20,81],"model":[21,54],"spatial":[22],"heterogeneity":[23],"quantify":[25],"uncertainty,":[27],"limiting":[28],"their":[29],"accuracy":[30,132],"decision-support":[32],"capabilities.":[33],"This":[34],"paper":[35],"proposes":[36],"PDCDM":[37,124],"(POI-enhanced":[38],"Dual-Dimensional":[39],"Conditional":[40],"Diffusion":[41],"Model),":[42],"which":[43],"integrates":[44],"functional":[46,57],"semantic":[47],"awareness":[48],"with":[49,97,114],"conditional":[50,108],"diffusion":[51,72,109],"modeling.":[52],"The":[53],"captures":[55],"attributes":[58],"through":[59],"multi-scale":[60],"Point":[61],"Interest":[63],"(POI)":[64],"feature":[65],"representations":[66],"incorporates":[68],"them":[69],"into":[70],"the":[71,83,107,140,147],"generation":[73],"process.":[74],"A":[75],"dual-dimensional":[76,104],"Transformer":[77],"architecture":[78],"employed":[80],"decouple":[82],"modeling":[84,105],"temporal":[86,99],"dependencies":[87],"inter-grid":[89],"interactions,":[90],"enabling":[91],"adaptive":[92],"fusion":[93],"grid-level":[95],"features":[96],"dynamic":[98],"patterns.":[100],"Building":[101],"upon":[102],"this":[103],"framework,":[106],"mechanism":[110],"generates":[111],"probabilistic":[112],"predictions":[113],"explicit":[115],"uncertainty":[116,134],"quantification.":[117,135],"Real-world":[118],"dataset":[120],"validation":[121],"demonstrates":[122],"that":[123],"significantly":[125],"outperforms":[126],"state-of-the-art":[128],"in":[130,150],"Comprehensive":[136],"ablation":[137],"studies":[138],"validate":[139],"effectiveness":[141],"each":[143],"component":[144],"confirm":[146],"model\u2019s":[148],"practicality":[149],"complex":[151],"scenarios.":[153]},"counts_by_year":[],"updated_date":"2026-06-15T08:34:33.830935","created_date":"2025-11-18T00:00:00"}