{"id":"https://openalex.org/W4390100365","doi":"https://doi.org/10.1145/3589132.3625608","title":"Statistical Inference for Spatial Regionalization","display_name":"Statistical Inference for Spatial Regionalization","publication_year":2023,"publication_date":"2023-11-13","ids":{"openalex":"https://openalex.org/W4390100365","doi":"https://doi.org/10.1145/3589132.3625608"},"language":"en","primary_location":{"id":"doi:10.1145/3589132.3625608","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589132.3625608","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625608","source":null,"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625608","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078341677","display_name":"Hussah Alrashid","orcid":"https://orcid.org/0000-0002-0734-4477"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hussah Alrashid","raw_affiliation_strings":["University of California Riverside, Riverside, United States"],"affiliations":[{"raw_affiliation_string":"University of California Riverside, Riverside, United States","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052069452","display_name":"Amr Magdy","orcid":"https://orcid.org/0000-0001-6345-9730"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amr Magdy","raw_affiliation_strings":["University of California Riverside, Riverside, United States"],"affiliations":[{"raw_affiliation_string":"University of California Riverside, Riverside, United States","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004811779","display_name":"Sergio J. Rey","orcid":"https://orcid.org/0000-0001-5857-9762"},"institutions":[{"id":"https://openalex.org/I26538001","display_name":"San Diego State University","ror":"https://ror.org/0264fdx42","country_code":"US","type":"education","lineage":["https://openalex.org/I26538001"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sergio Rey","raw_affiliation_strings":["San Diego State University, San Diego, United States"],"affiliations":[{"raw_affiliation_string":"San Diego State University, San Diego, United States","institution_ids":["https://openalex.org/I26538001"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5078341677"],"corresponding_institution_ids":["https://openalex.org/I103635307"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19173709,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11502","display_name":"Facility Location and Emergency Management","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9905999898910522,"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/contiguity","display_name":"Contiguity","score":0.7687780857086182},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6623318791389465},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.632919192314148},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5665130019187927},{"id":"https://openalex.org/keywords/cardinality","display_name":"Cardinality (data modeling)","score":0.5599527955055237},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5109633207321167},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4740464687347412},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.44264286756515503},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4382737874984741},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3338955044746399},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2946659326553345},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.177564799785614},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13987070322036743}],"concepts":[{"id":"https://openalex.org/C68767595","wikidata":"https://www.wikidata.org/wiki/Q1677999","display_name":"Contiguity","level":2,"score":0.7687780857086182},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6623318791389465},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.632919192314148},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5665130019187927},{"id":"https://openalex.org/C87117476","wikidata":"https://www.wikidata.org/wiki/Q362383","display_name":"Cardinality (data modeling)","level":2,"score":0.5599527955055237},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5109633207321167},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4740464687347412},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.44264286756515503},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4382737874984741},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3338955044746399},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2946659326553345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.177564799785614},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13987070322036743},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3589132.3625608","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589132.3625608","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625608","source":null,"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3589132.3625608","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3589132.3625608","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3589132.3625608","source":null,"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390100365.pdf","grobid_xml":"https://content.openalex.org/works/W4390100365.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1533459960","https://openalex.org/W1749024214","https://openalex.org/W1952692938","https://openalex.org/W1978857010","https://openalex.org/W1984121786","https://openalex.org/W2009528260","https://openalex.org/W2022490362","https://openalex.org/W2029810944","https://openalex.org/W2049726324","https://openalex.org/W2058529928","https://openalex.org/W2067819296","https://openalex.org/W2088024020","https://openalex.org/W2112882545","https://openalex.org/W2121427128","https://openalex.org/W2129027842","https://openalex.org/W2136347018","https://openalex.org/W2165515835","https://openalex.org/W2463078144","https://openalex.org/W2546838348","https://openalex.org/W2753472927","https://openalex.org/W2781105848","https://openalex.org/W2804721087","https://openalex.org/W2807142734","https://openalex.org/W3021334113","https://openalex.org/W3033160334","https://openalex.org/W3097239672","https://openalex.org/W3107671388","https://openalex.org/W3123682778","https://openalex.org/W4287762739","https://openalex.org/W4289646341","https://openalex.org/W4309651349","https://openalex.org/W4309651834","https://openalex.org/W4385343203","https://openalex.org/W6677832872","https://openalex.org/W6749293483","https://openalex.org/W6751728944","https://openalex.org/W6789536395","https://openalex.org/W6948222175"],"related_works":["https://openalex.org/W2085498352","https://openalex.org/W2469797624","https://openalex.org/W2092518157","https://openalex.org/W182477923","https://openalex.org/W137830373","https://openalex.org/W3000984192","https://openalex.org/W2103073163","https://openalex.org/W4286952477","https://openalex.org/W4321348134","https://openalex.org/W4387929287"],"abstract_inverted_index":{"The":[0,92],"process":[1],"of":[2,8,19,32,149,156,168],"regionalization":[3,20,72,105],"involves":[4],"clustering":[5],"a":[6,54,69,88,112,134],"set":[7],"spatial":[9,71,147],"areas":[10],"into":[11],"spatially":[12],"contiguous":[13],"regions.":[14],"Given":[15],"the":[16,30,48,126,138,146,154,166],"NP-hard":[17],"nature":[18],"problems,":[21],"all":[22,60],"existing":[23],"algorithms":[24],"yield":[25],"approximate":[26],"solutions.":[27,63],"To":[28,107],"ascertain":[29],"quality":[31],"these":[33],"approximations,":[34],"it":[35],"is":[36,97],"crucial":[37],"for":[38,79,153],"domain":[39],"experts":[40],"to":[41,53,98],"obtain":[42],"statistically":[43],"significant":[44],"evidence":[45],"on":[46,102],"optimizing":[47],"objective":[49],"function,":[50],"in":[51],"comparison":[52],"random":[55,84],"reference":[56],"distribution":[57],"derived":[58],"from":[59],"potential":[61],"sample":[62,85],"In":[64],"this":[65],"paper,":[66],"we":[67,110],"propose":[68],"novel":[70],"problem,":[73],"denoted":[74],"as":[75],"SISR":[76,96],"(Statistical":[77],"Inference":[78],"Spatial":[80],"Regionalization),":[81],"which":[82],"generates":[83],"solutions":[86],"with":[87,133,159],"predetermined":[89],"region":[90,127,139,142],"cardinality.":[91],"driving":[93],"motivation":[94],"behind":[95],"conduct":[99],"statistical":[100],"inference":[101],"any":[103],"given":[104],"scheme.":[106],"address":[108],"SISR,":[109],"present":[111],"parallel":[113],"technique":[114],"named":[115],"PRRP":[116,121,169],"(P-Regionalization":[117],"through":[118],"Recursive":[119],"Partitioning).":[120],"operates":[122],"over":[123],"three":[124],"phases:":[125],"growing":[128],"phase":[129],"constructs":[130],"initial":[131],"regions":[132,158],"predefined":[135,160],"cardinality,":[136],"while":[137],"merging":[140],"and":[141],"splitting":[143],"phases":[144],"ensure":[145],"contiguity":[148],"unassigned":[150],"areas,":[151],"allowing":[152],"growth":[155],"subsequent":[157],"cardinalites.":[161],"An":[162],"extensive":[163],"evaluation":[164],"shows":[165],"effectiveness":[167],"using":[170],"various":[171],"real":[172],"datasets.":[173]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
