Geospatial Clustering Analysis on Drug Abuse Emergencies

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dc.contributor.authorLee, Jinha
dc.contributor.authorChoi, Jung Im
dc.contributor.authorYeh, Arthur
dc.contributor.authorLan, Qizhen
dc.contributor.authorKang, Hyojung
dc.date.accessioned2021-12-24T18:11:53Z
dc.date.available2021-12-24T18:11:53Z
dc.date.issued2022-01-04
dc.description.abstractThe epidemic of drug abuse is a serious public health issue in the U.S. The number of overdose deaths involving prescription opioids and illicit drugs has continuously increased over the last few years. This study aims to develop a geospatial model that identifies geospatial clusters in terms of socioeconomic and demographic characteristics with an unsupervised machine learning algorithm. Then, we suggest the most important features affecting heroin overdose both negatively and positively. The findings of this study may inform policymakers about strategies to mitigate the drug overdose crisis.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.697
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/80036
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectLocation Intelligence Research in System Sciences
dc.subjectdrug overdose
dc.subjectgeospatial clusters
dc.subjectk-means algorithms
dc.subjectunsupervised machine learning
dc.titleGeospatial Clustering Analysis on Drug Abuse Emergencies
dc.type.dcmitext

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