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

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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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