{"id":"https://openalex.org/W2920360943","doi":"https://doi.org/10.1109/isc2.2018.8656935","title":"Identification of the Social Duality: Street Criminality and High Vehicle Traffic in Lima City by Using Artificial Intelligence Through the Fisher-Snedecor Statistics and Shannon\u2019s Entropy","display_name":"Identification of the Social Duality: Street Criminality and High Vehicle Traffic in Lima City by Using Artificial Intelligence Through the Fisher-Snedecor Statistics and Shannon\u2019s Entropy","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2920360943","doi":"https://doi.org/10.1109/isc2.2018.8656935","mag":"2920360943"},"language":"en","primary_location":{"id":"doi:10.1109/isc2.2018.8656935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isc2.2018.8656935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Smart Cities Conference (ISC2)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5049466348","display_name":"Huber Nieto\u2013Chaupis","orcid":"https://orcid.org/0000-0001-6072-608X"},"institutions":[{"id":"https://openalex.org/I4210104876","display_name":"Universidad de Ciencias y Humanidades","ror":"https://ror.org/01dm2pd27","country_code":"PE","type":"education","lineage":["https://openalex.org/I4210104876"]}],"countries":["PE"],"is_corresponding":true,"raw_author_name":"Huber Nieto-Chaupis","raw_affiliation_strings":["Center of Research eHealth, Universidad de Ciencias y Humanidades, Lima, Peru"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center of Research eHealth, Universidad de Ciencias y Humanidades, Lima, Peru","institution_ids":["https://openalex.org/I4210104876"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5049466348"],"corresponding_institution_ids":["https://openalex.org/I4210104876"],"apc_list":null,"apc_paid":null,"fwci":2.007,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.86956522,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.9833999872207642,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.9833999872207642,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9650999903678894,"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"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.958899974822998,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5782383680343628},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5553325414657593},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.433369904756546},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34584105014801025}],"concepts":[{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5782383680343628},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5553325414657593},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.433369904756546},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34584105014801025},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isc2.2018.8656935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isc2.2018.8656935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Smart Cities Conference (ISC2)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W121858502","https://openalex.org/W1589380646","https://openalex.org/W1986127618","https://openalex.org/W2070722739","https://openalex.org/W2113878454","https://openalex.org/W2138615112","https://openalex.org/W2138629625","https://openalex.org/W2241951013","https://openalex.org/W2301635754","https://openalex.org/W2543124608","https://openalex.org/W2588311706","https://openalex.org/W2601909147","https://openalex.org/W2622175443","https://openalex.org/W2787025334","https://openalex.org/W6604922607","https://openalex.org/W6680970901","https://openalex.org/W6728842626"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"When":[0,79],"Shannon's":[1,63],"entropy":[2,64],"and":[3,33,62,91,128,136],"Fisher-Snedecor":[4],"statistics":[5],"are":[6,82],"working":[7],"together,":[8],"this":[9,40,54],"can":[10],"enter":[11],"into":[12],"a":[13,44],"scheme":[14,46],"of":[15,26,72,87,97,111,113,122],"artificial":[16,143],"intelligence":[17,144],"to":[18,47,69,74,126,147],"tackle":[19],"social":[20,51,77,130],"problems":[21],"such":[22,132],"as":[23,119,133],"the":[24,95,103,120,123],"identification":[25],"worrisome":[27],"spatial":[28],"points":[29],"where":[30],"street":[31,134],"criminality":[32,135],"vehicle's":[34],"chaos":[35],"is":[36,117],"happening":[37],"sharply.":[38],"In":[39],"paper":[41],"we":[42,56,84],"construct":[43],"computational":[45,80],"anticipate":[48],"these":[49,76],"abnormal":[50],"events.":[52,78],"For":[53,94],"end":[55],"use":[57],"Google-earth":[58],"maps.":[59],"The":[60],"Fischer-Snedecor":[61],"mathematical":[65],"machinery":[66],"have":[67],"served":[68],"build":[70],"schemes":[71],"probabilities":[73],"identify":[75],"simulations":[81],"done":[83],"perform":[85],"matching":[86],"output":[88],"`s":[89],"simulation":[90],"official":[92],"data.":[93],"case":[96],"Lima":[98],"city":[99],"our":[100],"modeling":[101],"matches":[102],"one":[104],"from":[105],"real":[106],"data":[107],"with":[108],"an":[109],"accuracy":[110],"order":[112],"85%.":[114],"This":[115],"result":[116],"translated":[118],"capability":[121],"stochastic":[124,148],"models":[125],"analyze":[127],"measure":[129],"abnormalities":[131],"vehicle":[137],"traffic":[138],"in":[139,145],"large":[140],"cities":[141],"using":[142],"conjunction":[146],"formalisms.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}