Predictive models in pandemic times and their impact on the analysis of crime

Main Article Content

Gabriel Alejandro Silva-Atencio
https://orcid.org/0000-0002-4881-181X
Mauricio Vladimir Umaña-Ramírez
https://orcid.org/0000-0002-0733-5183

Abstract

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

Article Details

How to Cite
Silva-Atencio, G. A., & Umaña-Ramírez, M. V. (2023). Predictive models in pandemic times and their impact on the analysis of crime. Journal of Applied Research and Technology, 21(3), 484–495. https://doi.org/10.22201/icat.24486736e.2023.21.3.1944
Section
Articles