{"id":"https://openalex.org/W4406461743","doi":"https://doi.org/10.1109/bigdata62323.2024.10825109","title":"Data-Driven Socio-Economic Deprivation Prediction via Dimensionality Reduction: The Power of Diffusion Maps","display_name":"Data-Driven Socio-Economic Deprivation Prediction via Dimensionality Reduction: The Power of Diffusion Maps","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406461743","doi":"https://doi.org/10.1109/bigdata62323.2024.10825109"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825109","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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":"https://openalex.org/A5113074493","display_name":"June Moh Goo","orcid":null},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"June Moh Goo","raw_affiliation_strings":["University College London,Department of Civil, Environmental and Geomatics Engineering,London,UK"],"affiliations":[{"raw_affiliation_string":"University College London,Department of Civil, Environmental and Geomatics Engineering,London,UK","institution_ids":["https://openalex.org/I45129253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5113074493"],"corresponding_institution_ids":["https://openalex.org/I45129253"],"apc_list":null,"apc_paid":null,"fwci":0.5021,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.73323744,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2949","last_page":"2957"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.8409000039100647,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.8409000039100647,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.7882000207901001,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.775600016117096,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.7606964707374573},{"id":"https://openalex.org/keywords/diffusion-map","display_name":"Diffusion map","score":0.6065207719802856},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.552881121635437},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.5356857776641846},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5196658372879028},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4864182472229004},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.4241368770599365},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3014419674873352},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1851198673248291},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.14950236678123474},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10555297136306763},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.09464064240455627}],"concepts":[{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.7606964707374573},{"id":"https://openalex.org/C55128770","wikidata":"https://www.wikidata.org/wiki/Q5275440","display_name":"Diffusion map","level":4,"score":0.6065207719802856},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.552881121635437},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.5356857776641846},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5196658372879028},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4864182472229004},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.4241368770599365},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3014419674873352},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1851198673248291},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.14950236678123474},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10555297136306763},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.09464064240455627},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825109","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:10204144","is_oa":false,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10204144/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"In:  2024 IEEE International Conference on Big Data (BigData).  (pp. pp. 2949-2957).  IEEE: Washington, DC, USA. (2025)","raw_type":"Proceedings paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1574447377","https://openalex.org/W1990331496","https://openalex.org/W2064750581","https://openalex.org/W2137570937","https://openalex.org/W2165870892","https://openalex.org/W2169462149","https://openalex.org/W2181401284","https://openalex.org/W2479778494","https://openalex.org/W2891390633","https://openalex.org/W3039510137","https://openalex.org/W4213367101","https://openalex.org/W4225997385","https://openalex.org/W4287710844","https://openalex.org/W4298082496","https://openalex.org/W4300869678","https://openalex.org/W4396741100","https://openalex.org/W6781691594","https://openalex.org/W6996033051"],"related_works":["https://openalex.org/W2362377451","https://openalex.org/W2295388844","https://openalex.org/W4391160746","https://openalex.org/W1552543208","https://openalex.org/W2385810342","https://openalex.org/W2074396517","https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W1641615907","https://openalex.org/W2354420595"],"abstract_inverted_index":{"This":[0],"research":[1],"proposes":[2],"a":[3,15,105],"model":[4,106,198,208],"to":[5,29,38,61,92,123,147,171,199,223],"predict":[6],"the":[7,10,20,34,50,55,62,69,77,84,114,137,149,152,158,172,186,197,201,217],"location":[8],"of":[9,49,68,133,151,157,176,178,192,203],"most":[11,79,159],"deprived":[12,134,160,205],"areas":[13,135,164],"in":[14,87,136,143,212,216,225],"city":[16],"using":[17],"data":[18,23,86],"from":[19],"census.":[21],"Census":[22],"is":[24,90,103,221],"very":[25],"high-dimensional":[26],"and":[27,41,162,188,229],"needs":[28],"be":[30,124,235],"simplified.":[31],"We":[32],"use":[33],"diffusion":[35,56],"map":[36],"algorithm":[37],"reduce":[39],"dimensionality":[40],"find":[42],"patterns.":[43],"Features":[44],"are":[45,140,145,155,165],"defined":[46],"by":[47,110,168],"eigenvectors":[48,59],"Laplacian":[51],"matrix":[52],"that":[53,76,181],"defines":[54],"map.":[57],"The":[58,117,128,174,207,232],"corresponding":[60],"smallest":[63],"eigenvalues":[64],"indicate":[65],"specific":[66],"characteristics":[67],"population.":[70],"Previous":[71],"work":[72],"has":[73],"found":[74,122],"qualitatively":[75],"second":[78],"important":[80],"dimension":[81,102],"for":[82,107],"describing":[83],"census":[85],"Bristol,":[88,144],"UK":[89],"linked":[91],"deprivation.":[93],"In":[94],"this":[95,101],"research,":[96],"we":[97],"analyse":[98],"how":[99],"good":[100],"as":[104],"predicting":[108,213],"deprivation":[109,215],"comparing":[111,169],"it":[112],"with":[113,185],"recognised":[115],"measures.":[116],"Pearson":[118],"correlation":[119],"coefficient":[120],"was":[121],"greater":[125],"than":[126],"0.7.":[127],"top":[129],"10":[130],"per":[131],"cent":[132],"UK,":[138],"which":[139,220],"also":[141],"located":[142],"extracted":[146],"test":[148],"accuracy":[150],"model.":[153,173],"There":[154],"52":[156],"areas,":[161,219],"38":[163],"correctly":[166],"identified":[167],"them":[170],"influence":[175],"scores":[177],"IMD":[179],"domains":[180],"do":[182],"not":[183],"correlate":[184],"models":[187],"Eigenvector":[189],"2":[190],"entries":[191],"non-deprived":[193],"Output":[194],"Areas":[195],"cause":[196],"fail":[200],"prediction":[202],"14":[204],"areas.":[206],"demonstrates":[209],"strong":[210],"performance":[211],"future":[214],"project":[218],"expected":[222],"assist":[224],"government":[226],"resource":[227],"allocation":[228],"funding":[230],"greatly.":[231],"codes":[233],"can":[234],"accessed":[236],"here:":[237],"https://github.com/junegoo94/diffusion_maps.":[238]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
