Data analysis of thefts in the city of Medellin from a descriptive approach

This article aims to identify trends and patterns of theft in the city of Medellin in the period 2014-2020, using open government data. The methodology used is business intelligence for descriptive data analysis. Variables such as neighborhoods, modalities, type of theft, and the prediction of the theft modality variable are analyzed. The results show that historically the second half of the year has the highest trend of incidences, where most thefts occur in public places 60% without the use of weapons. It is shown that due to the COVID pandemic, historical trends showed significant changes, but once the restrictions were lifted, they resumed the trends of increases in thefts in pre-pandemic conditions. It is concluded that the use of open data analisys gives information to improve the decision-making of the citizens

• Tipo de recurso: http://purl.org/coar/resource_type/c_6584
• Fecha de publicación: 2023
• Institución: Universidad Pedagógica y Tecnológica de Colombia
• Repositorio: RiUPTC: Repositorio Institucional UPTC
• Idioma: spa
• OAI Identifier: oai:repositorio.uptc.edu.co:001/10407
• Acceso en línea: https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16059; https://repositorio.uptc.edu.co/handle/001/10407
• Palabra clave: open data; theft; machine learning; business intelligence; datos abiertos; robo; aprendizaje automático; inteligencia de negocios
• License: Derechos de autor 2023 Revista de Investigación, Desarrollo e Innovación