Detection of Homicide Trends in Colombia Using Machine Learning
The number of violent homicides in Latin America has grown considerably in recent decades, due to the expansion and rise of organized criminal groups in rural and urban areas of the main cities of countries such as Mexico, Colombia and Venezuela. Given their high homicide rate as a consequence of th...
| Main Authors: | , , |
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2019
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11740 |
| _version_ | 1801706088924446720 |
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| author | Ordoñez-Eraso, Hugo Armando Pardo-Calvache, César Jesús Cobos-Lozada, Carlos Alberto |
| author_facet | Ordoñez-Eraso, Hugo Armando Pardo-Calvache, César Jesús Cobos-Lozada, Carlos Alberto |
| author_sort | Ordoñez-Eraso, Hugo Armando |
| collection | OJS |
| description | The number of violent homicides in Latin America has grown considerably in recent decades, due to the expansion and rise of organized criminal groups in rural and urban areas of the main cities of countries such as Mexico, Colombia and Venezuela. Given their high homicide rate as a consequence of the high crime rate, these countries have been classified among the most violent in the world. According to data reported by the Crime Observatory, the National Police and the Attorney General's Office of Colombia, in 2019 there were 1,032 murders in Bogotá. This data shows a homicide rate of 14.3 per 100,000 inhabitants. From this, it is estimated that between 1960 and 2019, around 226,215 homicides were generated, which is, on average, 3,834 deaths per year. In this work a random forest-based machine learning model is presented, which allows predicting violent homicide (VH) trends in Colombia for the next 5 years. The objective of the model is to serve as an instrument to facilitate decision-making in organizations such as the Prosecutor’s Office and the National Police. The model was evaluated with a dataset obtained from the Criminal, Contraventional and Operational Statistical Information System (SIEDCO in Spanish) of the Prosecutor's Office, which has 2,662,402 records of crimes committed in Colombia from 1960 to 2019. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-11740 |
| institution | Revista Facultad de Ingeniería |
| language | spa |
| publishDate | 2019 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-117402021-07-13T02:24:57Z Detection of Homicide Trends in Colombia Using Machine Learning Detección de tendencias de homicidios en Colombia usando Machine Learning Ordoñez-Eraso, Hugo Armando Pardo-Calvache, César Jesús Cobos-Lozada, Carlos Alberto data mining homicide machine learning random forest homicidio machine learning minería de datos random forest The number of violent homicides in Latin America has grown considerably in recent decades, due to the expansion and rise of organized criminal groups in rural and urban areas of the main cities of countries such as Mexico, Colombia and Venezuela. Given their high homicide rate as a consequence of the high crime rate, these countries have been classified among the most violent in the world. According to data reported by the Crime Observatory, the National Police and the Attorney General's Office of Colombia, in 2019 there were 1,032 murders in Bogotá. This data shows a homicide rate of 14.3 per 100,000 inhabitants. From this, it is estimated that between 1960 and 2019, around 226,215 homicides were generated, which is, on average, 3,834 deaths per year. In this work a random forest-based machine learning model is presented, which allows predicting violent homicide (VH) trends in Colombia for the next 5 years. The objective of the model is to serve as an instrument to facilitate decision-making in organizations such as the Prosecutor’s Office and the National Police. The model was evaluated with a dataset obtained from the Criminal, Contraventional and Operational Statistical Information System (SIEDCO in Spanish) of the Prosecutor's Office, which has 2,662,402 records of crimes committed in Colombia from 1960 to 2019. En las últimas décadas, el número de homicidios violentos en América Latina ha crecido considerablemente debido a la ampliación y auge de grupos criminales organizados en zonas rurales y urbanas de las principales ciudades de países como México, Colombia y Venezuela. Con base en el alto índice de homicidio de estos países, consecuencia de la alta criminalidad, éstos han sido clasificados dentro de los más violentos a nivel mundial. Según datos reportados por el Observatorio del Delito de la Policía Nacional y la Fiscalía General de la Nación de Colombia, en 2019 se presentaron 1.032 asesinatos en Bogotá. Estos datos arrojan una tasa de 14,3 homicidios por cada 100.000 habitantes. A partir de esto, se estima que entre 1960 y 2019 se han generado alrededor de 226.215 homicidios, unas 3,834 muertes por año, en promedio. En este trabajo se presenta un modelo de machine learning basado en random forest, el cual permite predecir las tendencias de homicidio violento (HV) en Colombia para los próximos 5 años. El proyecto tiene como objetivo servir de instrumento para facilitar la toma de decisiones en organismos como la Fiscalía General de la Nación y la Policía Nacional. El modelo fue evaluado con un conjunto de datos generado a partir del Sistema de Información Estadístico Delincuencial, Contravencional y Operativo (SIEDCO) de la Fiscalía, el cual cuenta con 2.662.402 registros de delitos realizados en Colombia desde el año 1960 hasta 2019. Universidad Pedagógica y Tecnológica de Colombia 2019-10-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf application/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11740 10.19053/01211129.v29.n54.2020.11740 Revista Facultad de Ingeniería; Vol. 29 No. 54 (2020): Continuos Publication; e11740 Revista Facultad de Ingeniería; Vol. 29 Núm. 54 (2020): Publicación Continua; e11740 2357-5328 0121-1129 spa https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11740/9603 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11740/10006 Copyright (c) 2020 Hugo-Armando Ordoñez-Eraso; César-Jesús Pardo-Calvache; Carlos-Alberto Cobos-Lozada |
| spellingShingle | data mining homicide machine learning random forest homicidio machine learning minería de datos random forest Ordoñez-Eraso, Hugo Armando Pardo-Calvache, César Jesús Cobos-Lozada, Carlos Alberto Detection of Homicide Trends in Colombia Using Machine Learning |
| title | Detection of Homicide Trends in Colombia Using Machine Learning |
| title_alt | Detección de tendencias de homicidios en Colombia usando Machine Learning |
| title_full | Detection of Homicide Trends in Colombia Using Machine Learning |
| title_fullStr | Detection of Homicide Trends in Colombia Using Machine Learning |
| title_full_unstemmed | Detection of Homicide Trends in Colombia Using Machine Learning |
| title_short | Detection of Homicide Trends in Colombia Using Machine Learning |
| title_sort | detection of homicide trends in colombia using machine learning |
| topic | data mining homicide machine learning random forest homicidio machine learning minería de datos random forest |
| topic_facet | data mining homicide machine learning random forest homicidio machine learning minería de datos random forest |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/11740 |
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