dor_id: 4150069
506.#.#.a: Público
590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares
510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation
561.#.#.u: https://www.icat.unam.mx/
650.#.4.x: Ingenierías
336.#.#.b: article
336.#.#.3: Artículo de Investigación
336.#.#.a: Artículo
351.#.#.6: https://jart.icat.unam.mx/index.php/jart
351.#.#.b: Journal of Applied Research and Technology
351.#.#.a: Artículos
harvesting_group: RevistasUNAM
270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx
590.#.#.c: Open Journal Systems (OJS)
270.#.#.d: MX
270.1.#.d: México
590.#.#.b: Concentrador
883.#.#.u: https://revistas.unam.mx/catalogo/
883.#.#.a: Revistas UNAM
590.#.#.a: Coordinación de Difusión Cultural
883.#.#.1: https://www.publicaciones.unam.mx/
883.#.#.q: Dirección General de Publicaciones y Fomento Editorial
850.#.#.a: Universidad Nacional Autónoma de México
856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/1944/1008
100.1.#.a: Silva Atencio, Gabriel Alejandro; Umaña Ramírez, Mauricio Vladimir
524.#.#.a: Silva Atencio, Gabriel Alejandro, et al. (2023). Predictive models in pandemic times and their impact on the analysis of crime. Journal of Applied Research and Technology; Vol. 21 Núm. 3, 2023; 484-495. Recuperado de https://repositorio.unam.mx/contenidos/4150069
245.1.0.a: Predictive models in pandemic times and their impact on the analysis of crime
502.#.#.c: Universidad Nacional Autónoma de México
561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM
264.#.0.c: 2023
264.#.1.c: 2023-06-29
653.#.#.a: crime prediction; preventive patrolling; police statistics; crime-fighting
506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx
884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/1944
001.#.#.#: 074.oai:ojs2.localhost:article/1944
041.#.7.h: eng
520.3.#.a: Through the descriptive analysis on the Open Data of the Costa Rican Judicial Power, alarming results are reflected in the number of complaints imposed in the Judicial Investigation Organism (OIJ), exceeding fifty thousand complaints in 2019. Based on those numbers, the objective for this research is to generate a data analysis model that allows to potentiate these statistics and to indicate in advance the regions with the most remarkable propensity to suffer crimes in the next five years, to promote the proactivity of both the citizen and the police to be alerted and to avoid upcoming crimes. Statistical prediction models are used to prove mathematical methods applicable to the data obtained and their behavior during 2015-2019. The analysis reflects the need to apply the simple linear regression algorithm to the developed solution available to all Costa Ricans on the Tableau Public website. The results show pessimistic predictions for the country, especially in the Greater Metropolitan Area (GAM); the behavior of crimes will significantly impact this area, which indicates the need to establish police strengthening programs improvements in education and employment to counter the potential crimes projected for the next five years
773.1.#.t: Journal of Applied Research and Technology; Vol. 21 Núm. 3 (2023); 484-495
773.1.#.o: https://jart.icat.unam.mx/index.php/jart
022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423
310.#.#.a: Bimestral
300.#.#.a: Páginas: 484-495
264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM
doi: https://doi.org/10.22201/icat.24486736e.2023.21.3.1944
harvesting_date: 2023-11-08 13:10:00.0
856.#.0.q: application/pdf
file_creation_date: 2023-06-27 21:38:09.0
file_modification_date: 2023-06-27 21:38:09.0
file_creator: Yolanda G.G.
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license_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es
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