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dc.contributor.author이동희-
dc.date.accessioned2019-12-02T06:01:27Z-
dc.date.available2019-12-02T06:01:27Z-
dc.date.issued2017-11-
dc.identifier.citation한국경영과학회지, v. 42, no. 4, page. 135-146en_US
dc.identifier.issn1225-1119-
dc.identifier.urihttp://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07285855&language=ko_KR-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/116346-
dc.description.abstractIn this paper, we present a case study for developing a crime prediction model in Korea. This model aims to predict regions with high crime rates compared with other regions. To develop the prediction model, we first selected critical variables related to crime occurrence. Second, we collected data for the selected variables some of which were provided by the city hall of Kyung-gi province in Korea. Lastly, we determined relative weights for the selected variables and completed the prediction model by aggregating the selected variables throughweighted summation. Regions with high crime rates were predicted based on the prediction model in this case study.en_US
dc.description.sponsorship이 논문은 한양대학교 교내연구지원사업으로 연구되었음(HY-2016년도).en_US
dc.language.isoko_KRen_US
dc.publisher한국경영과학회en_US
dc.subjectCrime Predictionen_US
dc.subjectOverlap Analysisen_US
dc.subjectPostal Codeen_US
dc.subjectRegression Analysisen_US
dc.title국가기초구역을 이용한 범죄예측모델 :안양시 사례를 중심으로en_US
dc.title.alternativeDevelopment of Crime Prediction Model : A Case Study in Anyang Cityen_US
dc.typeArticleen_US
dc.relation.no4-
dc.relation.volume42-
dc.identifier.doi10.7737/JKORMS.2017.42.4.135-
dc.relation.page135-146-
dc.relation.journal한국경영과학회지-
dc.contributor.googleauthor강선호-
dc.contributor.googleauthor양진경-
dc.contributor.googleauthor한별-
dc.contributor.googleauthor이동희-
dc.contributor.googleauthorKang, Sun-Ho-
dc.contributor.googleauthorYang, Jin-kyung-
dc.contributor.googleauthorHan, Byeol-
dc.contributor.googleauthorLee, Dong-Hee-
dc.relation.code2017018167-
dc.sector.campusS-
dc.sector.daehakDIVISION OF INDUSTRIAL INFORMATION STUDIES[S]-
dc.sector.departmentDIVISION OF INDUSTRIAL INFORMATION STUDIES-
dc.identifier.piddh-
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