{"id":"https://openalex.org/W2911630801","doi":"https://doi.org/10.1145/3282866.3282870","title":"Analysis of Multifactorial Social Unrest Events with Spatio-Temporal k-Dimensional Tree-based DBSCAN","display_name":"Analysis of Multifactorial Social Unrest Events with Spatio-Temporal k-Dimensional Tree-based DBSCAN","publication_year":2018,"publication_date":"2018-11-06","ids":{"openalex":"https://openalex.org/W2911630801","doi":"https://doi.org/10.1145/3282866.3282870","mag":"2911630801"},"language":"en","primary_location":{"id":"doi:10.1145/3282866.3282870","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3282866.3282870","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3282866.3282870?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News","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/10.1145/3282866.3282870?download=true","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055403059","display_name":"Sudeep Basnet","orcid":null},"institutions":[{"id":"https://openalex.org/I114395901","display_name":"University of Nebraska\u2013Lincoln","ror":"https://ror.org/043mer456","country_code":"US","type":"education","lineage":["https://openalex.org/I114395901"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sudeep Basnet","raw_affiliation_strings":["Univ. of Nebraska, Lincoln, NE"],"affiliations":[{"raw_affiliation_string":"Univ. of Nebraska, Lincoln, NE","institution_ids":["https://openalex.org/I114395901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089919340","display_name":"Leen\u2010Kiat Soh","orcid":"https://orcid.org/0000-0002-6094-4666"},"institutions":[{"id":"https://openalex.org/I114395901","display_name":"University of Nebraska\u2013Lincoln","ror":"https://ror.org/043mer456","country_code":"US","type":"education","lineage":["https://openalex.org/I114395901"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leen-Kiat Soh","raw_affiliation_strings":["Univ. of Nebraska, Lincoln, NE"],"affiliations":[{"raw_affiliation_string":"Univ. of Nebraska, Lincoln, NE","institution_ids":["https://openalex.org/I114395901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056599026","display_name":"Ashok Samal","orcid":"https://orcid.org/0000-0002-4559-9454"},"institutions":[{"id":"https://openalex.org/I114395901","display_name":"University of Nebraska\u2013Lincoln","ror":"https://ror.org/043mer456","country_code":"US","type":"education","lineage":["https://openalex.org/I114395901"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ashok Samal","raw_affiliation_strings":["Univ. of Nebraska, Lincoln, NE"],"affiliations":[{"raw_affiliation_string":"Univ. of Nebraska, Lincoln, NE","institution_ids":["https://openalex.org/I114395901"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015659456","display_name":"Deepti Joshi","orcid":"https://orcid.org/0000-0003-3274-5907"},"institutions":[{"id":"https://openalex.org/I91036609","display_name":"Citadel","ror":"https://ror.org/01vwr6t80","country_code":"US","type":"education","lineage":["https://openalex.org/I91036609"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deepti Joshi","raw_affiliation_strings":["The Citadel, Charleston, SC"],"affiliations":[{"raw_affiliation_string":"The Citadel, Charleston, SC","institution_ids":["https://openalex.org/I91036609"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5055403059"],"corresponding_institution_ids":["https://openalex.org/I114395901"],"apc_list":null,"apc_paid":null,"fwci":0.367,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.76167009,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"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":0.9980999827384949,"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":0.9980999827384949,"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/T11106","display_name":"Data Management and Algorithms","score":0.9977999925613403,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9929999709129333,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7065557241439819},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.6934711337089539},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6597323417663574},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6343884468078613},{"id":"https://openalex.org/keywords/dbscan","display_name":"DBSCAN","score":0.6209430694580078},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5236499309539795},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.4333209693431854},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3786527216434479},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.36911141872406006},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28259336948394775},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.267467200756073},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.26420000195503235},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.23255088925361633},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.13706550002098083}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7065557241439819},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.6934711337089539},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6597323417663574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6343884468078613},{"id":"https://openalex.org/C46576248","wikidata":"https://www.wikidata.org/wiki/Q1114630","display_name":"DBSCAN","level":5,"score":0.6209430694580078},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5236499309539795},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.4333209693431854},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3786527216434479},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.36911141872406006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28259336948394775},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.267467200756073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26420000195503235},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.23255088925361633},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.13706550002098083},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3282866.3282870","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3282866.3282870","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3282866.3282870?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3282866.3282870","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3282866.3282870","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3282866.3282870?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2911630801.pdf","grobid_xml":"https://content.openalex.org/works/W2911630801.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W3989084","https://openalex.org/W74920699","https://openalex.org/W1493454437","https://openalex.org/W1546510067","https://openalex.org/W1673310716","https://openalex.org/W1971022913","https://openalex.org/W1975716168","https://openalex.org/W1996510517","https://openalex.org/W2023706795","https://openalex.org/W2048569019","https://openalex.org/W2057313398","https://openalex.org/W2084639365","https://openalex.org/W2095897464","https://openalex.org/W2137367494","https://openalex.org/W2146994701","https://openalex.org/W2153218475","https://openalex.org/W2154002592","https://openalex.org/W2160642098","https://openalex.org/W2165558283","https://openalex.org/W2524727588","https://openalex.org/W2553798436","https://openalex.org/W2611422198","https://openalex.org/W2612186099","https://openalex.org/W2780205489","https://openalex.org/W2914874661","https://openalex.org/W2999729612","https://openalex.org/W4231029117","https://openalex.org/W4251756538","https://openalex.org/W6682378837","https://openalex.org/W6842476337","https://openalex.org/W6903624869","https://openalex.org/W6996824252"],"related_works":["https://openalex.org/W3163639875","https://openalex.org/W4367313141","https://openalex.org/W2004086023","https://openalex.org/W3176449234","https://openalex.org/W2353158678","https://openalex.org/W2767235736","https://openalex.org/W4225278791","https://openalex.org/W2045002201","https://openalex.org/W2971352445","https://openalex.org/W4322502698"],"abstract_inverted_index":{"Clustering":[0],"geospatial":[1,37,103,137],"event":[2,19,73,92,174,235],"data":[3],"requires":[4],"defining":[5],"a":[6,59,98,106,146,158,183,189],"distance":[7,147],"function":[8,148],"between":[9,149],"events":[10,27,220],"as":[11,13,29],"well":[12,121],"representing":[14],"neighborhood":[15,107],"characteristics":[16],"where":[17],"an":[18,72,109],"occurred":[20],"in":[21,33,87,105,231,237],"numerical":[22],"or":[23],"categorical":[24],"values.":[25],"For":[26],"such":[28,91],"social":[30,60,242],"unrest":[31,61,219,243],"events,":[32],"addition":[34],"to":[35,45,74,96,114,119,217,233,239],"the":[36,56,69,116,128,143,153,169,180,215,228,248],"coordinates":[38],"and":[39,63,111,135,139,198,223,250],"time":[40],"stamps,":[41],"other":[42],"factors":[43,52,65,225],"needed":[44],"understand":[46],"how":[47,95,113,241],"they":[48],"evolve":[49],"include":[50],"socioeconomic":[51,222],"that":[53,66,167],"fuel,":[54],"say,":[55,68],"emergence":[57],"of":[58,71,90,101,145,252],"event,":[62,110,197],"infrastructural":[64,224],"facilitate,":[67],"propagation":[70],"nearby":[75,102],"regions.":[76],"In":[77],"this":[78],"paper,":[79],"we":[80,131,156],"focus":[81],"on":[82],"addressing":[83],"two":[84,133],"main":[85],"challenges":[86],"spatiotemporal":[88],"clustering":[89,117,165,176],"data:":[93],"(1)":[94],"derive":[97],"numeric":[99],"representation":[100],"objects":[104],"for":[108,122,172,195,227],"(2)":[112],"improve":[115],"process":[118],"scale":[120],"very":[123],"large":[124],"datasets.":[125],"To":[126,151],"address":[127,152],"first":[129,178],"challenge,":[130,155],"propose":[132,157],"metrics---proximity":[134],"density---of":[136],"objects,":[138],"incorporate":[140],"them":[141],"into":[142,182],"definition":[144],"events.":[150],"second":[154],"novel":[159],"Spatio-Temporal":[160],"k-Dimensional":[161],"Tree-based":[162],"DBSCAN":[163,202],"(ST-KDT-DBSCAN)":[164],"approach":[166],"restricts":[168],"search":[170],"radius":[171],"each":[173,196,205],"during":[175],"by":[177],"organizing":[179],"dataset":[181],"k-dimensional":[184],"tree":[185],"structure,":[186],"subsequently":[187],"creating":[188],"Fixed-Radius":[190],"Near":[191],"Neighbor":[192],"(FRNN)":[193],"object":[194,208],"then":[199],"carrying":[200],"out":[201],"considering":[203],"only":[204],"event's":[206],"FRNN":[207],"when":[209],"computing":[210],"reachability.":[211],"We":[212],"have":[213],"applied":[214],"solutions":[216],"29,371":[218],"with":[221],"recorded":[226],"year":[229],"2014":[230],"India,":[232],"identify":[234],"episodes":[236],"order":[238],"analyze":[240],"evolves.":[244],"Our":[245],"results":[246],"show":[247],"viability":[249],"scalability":[251],"our":[253],"solutions.":[254]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
