Machine learning model for the classification of municipalities by illicit crops in Colombia from 2010 to 2020
For the United Nations Office on Drugs and Crime (UNODC), Colombia is one of the top countries where drug trafficking and crime jeopardize security, peace and development opportunities of citizens. Initially, an account of the history of coca crops in Colombia will be developed. Starting with the pe...
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| Format: | Article |
| Language: | English |
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Universidad de la Costa
2023-01-01
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| Series: | Inge-Cuc |
| Subjects: | |
| Online Access: | https://revistascientificas.cuc.edu.co/ingecuc/article/view/4639 |
| _version_ | 1797668255681216512 |
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| author | Andrés Eduardo Narváez Figueroa Gustavo Cáceres Castellanos Juan Sebastián González Sanabria |
| author_facet | Andrés Eduardo Narváez Figueroa Gustavo Cáceres Castellanos Juan Sebastián González Sanabria |
| author_sort | Andrés Eduardo Narváez Figueroa |
| collection | DOAJ |
| description | For the United Nations Office on Drugs and Crime (UNODC), Colombia is one of the top countries where drug trafficking and crime jeopardize security, peace and development opportunities of citizens. Initially, an account of the history of coca crops in Colombia will be developed. Starting with the period known as “bonanza marimbera” in the 60s, it will be described how the country transformed from a marijuana producer to being one of the main cocaine producers in the world. Multiple sources of information are crossed, such as the number of hectares of coca per municipality, seizures, destroyed laboratories, manual eradication and fumigation monitored by the national institutions, crossed with socio-economic variables and performance of the municipalities that have coca crops in Colombia in the period from 2010 to 2020. Data mining algorithms were used to identify correlations and patterns that allowed the classification of municipalities with coca and starting from the classification it was possible to analyze the scenarios of each category found, to find scenarios that shed light on the dynamics of the territories that suffer from this scourge. |
| first_indexed | 2024-03-11T20:26:31Z |
| format | Article |
| id | doaj.art-a9d8d91cb1634d41acc1b9ca2f3765b8 |
| institution | Directory Open Access Journal |
| issn | 0122-6517 2382-4700 |
| language | English |
| last_indexed | 2024-03-11T20:26:31Z |
| publishDate | 2023-01-01 |
| publisher | Universidad de la Costa |
| record_format | Article |
| series | Inge-Cuc |
| spelling | doaj.art-a9d8d91cb1634d41acc1b9ca2f3765b82023-10-02T14:10:30ZengUniversidad de la CostaInge-Cuc0122-65172382-47002023-01-0119110.17981/ingecuc.19.1.2023.053659Machine learning model for the classification of municipalities by illicit crops in Colombia from 2010 to 2020Andrés Eduardo Narváez FigueroaGustavo Cáceres CastellanosJuan Sebastián González SanabriaFor the United Nations Office on Drugs and Crime (UNODC), Colombia is one of the top countries where drug trafficking and crime jeopardize security, peace and development opportunities of citizens. Initially, an account of the history of coca crops in Colombia will be developed. Starting with the period known as “bonanza marimbera” in the 60s, it will be described how the country transformed from a marijuana producer to being one of the main cocaine producers in the world. Multiple sources of information are crossed, such as the number of hectares of coca per municipality, seizures, destroyed laboratories, manual eradication and fumigation monitored by the national institutions, crossed with socio-economic variables and performance of the municipalities that have coca crops in Colombia in the period from 2010 to 2020. Data mining algorithms were used to identify correlations and patterns that allowed the classification of municipalities with coca and starting from the classification it was possible to analyze the scenarios of each category found, to find scenarios that shed light on the dynamics of the territories that suffer from this scourge.https://revistascientificas.cuc.edu.co/ingecuc/article/view/4639unsupervised classificationillicit cropsdata miningfight against drugscocainecolombia |
| spellingShingle | Andrés Eduardo Narváez Figueroa Gustavo Cáceres Castellanos Juan Sebastián González Sanabria Machine learning model for the classification of municipalities by illicit crops in Colombia from 2010 to 2020 Inge-Cuc unsupervised classification illicit crops data mining fight against drugs cocaine colombia |
| title | Machine learning model for the classification of municipalities by illicit crops in Colombia from 2010 to 2020 |
| title_full | Machine learning model for the classification of municipalities by illicit crops in Colombia from 2010 to 2020 |
| title_fullStr | Machine learning model for the classification of municipalities by illicit crops in Colombia from 2010 to 2020 |
| title_full_unstemmed | Machine learning model for the classification of municipalities by illicit crops in Colombia from 2010 to 2020 |
| title_short | Machine learning model for the classification of municipalities by illicit crops in Colombia from 2010 to 2020 |
| title_sort | machine learning model for the classification of municipalities by illicit crops in colombia from 2010 to 2020 |
| topic | unsupervised classification illicit crops data mining fight against drugs cocaine colombia |
| url | https://revistascientificas.cuc.edu.co/ingecuc/article/view/4639 |
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