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dc.contributor.authorGuevara-Maldonado, César-
dc.contributor.authorSantos, Matilde-
dc.date.accessioned2022-06-20T18:22:30Z-
dc.date.available2022-06-20T18:22:30Z-
dc.date.issued2021-
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-030-57805-3_10-
dc.identifier.urihttp://repositorio.uti.edu.ec//handle/123456789/3300-
dc.description.abstractCitizen security is one of the main objectives of any government worldwide. Security entities make multiple efforts to apply the latest technologies in order to prevent any type of criminal offence. The analysis of a database of the National Police of Ecuador has allowed us generating patrol routes to prevent and reduce the crime rate in the city of Quito, Ecuador. The K-means clustering has been used to determine the points of greatest crime concentration and then linear regression is applied for the prediction of crimes within subgroups of data. Those way-points will allow to generate and optimize police patrol routes. The results obtained in the prediction of crimes is greater than 80%.es
dc.language.isoenges
dc.publisherAdvances in Intelligent Systems and Computing. Volume 1267 AISC, Pages 97 - 107. 13th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2020. Burgos. 16 September 2020 through 18 September 2020es
dc.rightsopenAccesses
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es
dc.titleCrime prediction for patrol routes generation using machine learninges
dc.typearticlees
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