{"id":"https://openalex.org/W2560213231","doi":"https://doi.org/10.1145/3003819.3003821","title":"A generic dual grid pruning approach for significant hotspot detection","display_name":"A generic dual grid pruning approach for significant hotspot detection","publication_year":2016,"publication_date":"2016-10-31","ids":{"openalex":"https://openalex.org/W2560213231","doi":"https://doi.org/10.1145/3003819.3003821","mag":"2560213231"},"language":"en","primary_location":{"id":"doi:10.1145/3003819.3003821","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3003819.3003821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd ACM SIGSPATIAL PhD Symposium","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/A5015734523","display_name":"Emre Eftelioglu","orcid":"https://orcid.org/0000-0002-5551-0046"},"institutions":[{"id":"https://openalex.org/I2800403580","display_name":"University of Minnesota System","ror":"https://ror.org/03grvy078","country_code":"US","type":"education","lineage":["https://openalex.org/I2800403580"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Emre Eftelioglu","raw_affiliation_strings":["University of Minnesota"],"affiliations":[{"raw_affiliation_string":"University of Minnesota","institution_ids":["https://openalex.org/I2800403580"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5015734523"],"corresponding_institution_ids":["https://openalex.org/I2800403580"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.17291563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13398","display_name":"Data Analysis with R","score":0.9498999714851379,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hotspot","display_name":"Hotspot (geology)","score":0.9210351705551147},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5981200933456421},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.501483678817749},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3838959336280823},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3449561595916748},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31576836109161377},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.22418147325515747},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.11890846490859985}],"concepts":[{"id":"https://openalex.org/C146481406","wikidata":"https://www.wikidata.org/wiki/Q105131","display_name":"Hotspot (geology)","level":2,"score":0.9210351705551147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5981200933456421},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.501483678817749},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3838959336280823},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3449561595916748},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31576836109161377},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.22418147325515747},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.11890846490859985},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3003819.3003821","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3003819.3003821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd ACM SIGSPATIAL PhD Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1995376165","https://openalex.org/W2002151188","https://openalex.org/W2042659752","https://openalex.org/W2147555723","https://openalex.org/W2150495678","https://openalex.org/W2278483113","https://openalex.org/W2999905431"],"related_works":["https://openalex.org/W2379637199","https://openalex.org/W2405057786","https://openalex.org/W2501832907","https://openalex.org/W2063054109","https://openalex.org/W2079602762","https://openalex.org/W2580355466","https://openalex.org/W2765519165","https://openalex.org/W1888682135","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Given":[0],"a":[1,64,67,78,113,129,140,151,172,175,183,187],"set":[2],"of":[3,20,63,66,81,116,194],"points":[4,21],"in":[5,38],"two":[6],"dimensional":[7],"space,":[8],"statistically":[9],"significant":[10,31,57],"hotspot":[11,24,32,58,72,90,103,125,131,159,167],"detection":[12,33,59,91,132,160],"aims":[13],"to":[14,84,101,138,190,198],"detect":[15],"locations":[16],"where":[17,47],"the":[18,23,179,192],"concentration":[19],"inside":[22],"is":[25,34,60],"much":[26],"higher":[27],"than":[28],"outside.":[29],"Statistically":[30],"an":[35,143],"important":[36],"task":[37],"application":[39],"domains":[40],"such":[41],"as":[42],"epidemiology,":[43],"ecology,":[44],"criminology,":[45],"etc.":[46],"it":[48],"may":[49],"reveal":[50],"interesting":[51],"information":[52],"for":[53,70,122,158,165],"domain":[54],"experts.":[55],"However,":[56],"challenging":[61],"because":[62],"lack":[65],"generic":[68,152],"technique":[69,133],"different":[71,166],"patterns":[73],"(i.e.":[74],"shapes)":[75],"and":[76,87,118,182],"thus":[77],"large":[79],"number":[80],"candidate":[82],"hotspots":[83],"be":[85,108,136,163],"enumerated":[86],"tested.":[88],"Previous":[89],"techniques":[92,121],"focus":[93],"on":[94,178,186],"specific":[95],"shapes":[96],"(e.g.":[97],"circles,":[98],"rectangles,":[99],"ellipses)":[100],"identify":[102],"areas,":[104],"but":[105],"they":[106],"cannot":[107],"used":[109,137,164],"interchangeably":[110],"which":[111],"cause":[112],"vast":[114],"variety":[115],"complicated":[117],"sometimes":[119],"confusing":[120],"each":[123],"individual":[124],"pattern.":[126],"For":[127],"example,":[128],"circular":[130,199],"can":[134,162],"not":[135],"discover":[139],"rectangular":[141],"or":[142],"elliptical":[144],"hotspot.":[145],"In":[146],"this":[147],"paper,":[148],"we":[149],"propose":[150],"dual":[153],"grid":[154],"based":[155],"pruning":[156],"approach":[157,197],"that":[161],"patterns.":[168],"We":[169],"also":[170],"present":[171],"cost":[173],"analysis,":[174],"simplified":[176],"experiment":[177],"dataset":[180,189],"size":[181],"case":[184],"study":[185],"synthetic":[188],"show":[191],"applicability":[193],"our":[195],"proposed":[196],"hotspots.":[200]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
