Data analysis of thefts in the city of Medellin from a descriptive approach
Abstract
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
Keywords
open data;, theft;, machine learning;, business intelligence
Author Biography
Gina Maestre-Gongora
Ingeniera de Sistemas, Doctora en Ingeniería de Sistemas y Computación
Camilo Andrés Acuña-Castellanos
Ingeniero de Software
Edwar Londoño-Bedoya
Ingeniero de Software
Sergio García-García
Ingeniero de Software
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