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 t...
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2023
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| Online Access: | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16059 |
| _version_ | 1801706010111377408 |
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| author | Maestre-Gongora, Gina Acuña-Castellanos, Camilo Andrés Londoño-Bedoya, Edwar García-García, Sergio |
| author_facet | Maestre-Gongora, Gina Acuña-Castellanos, Camilo Andrés Londoño-Bedoya, Edwar García-García, Sergio |
| author_sort | Maestre-Gongora, Gina |
| collection | OJS |
| description | 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 |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-16059 |
| institution | Revista de Investigación, Desarrollo e Innovación (RIDI) |
| language | spa |
| publishDate | 2023 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-160592023-10-20T12:23:25Z Data analysis of thefts in the city of Medellin from a descriptive approach Análisis de datos sobre los hurtos en la ciudad de Medellín desde un enfoque descriptivo Maestre-Gongora, Gina Acuña-Castellanos, Camilo Andrés Londoño-Bedoya, Edwar García-García, Sergio open data; theft; machine learning; business intelligence datos abiertos; robo; aprendizaje automático; inteligencia de negocios 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 Este artículo tiene por objetivo identificar las tendencias y patrones de hurto en la ciudad de Medellín en el periodo 2014-2020, usando datos abiertos de gobierno. Se utiliza como metodología la inteligencia de negocios para el análisis de datos descriptivo. Se analizan variables como barrios, modalidades, tipo de hurto y se realiza la predicción de la variable modalidad de hurto. Los resultados muestran que históricamente el segundo semestre del año tiene la mayor tendencia de incidencias, donde la mayoría de robos suceden en los lugares públicos con un 60% sin el uso de armas. Se identificó que, debido a la pandemia de COVID, las tendencias históricas presentaron alteraciones notables, pero una vez levantadas las restricciones, estas retomaron las tendencias de alzas en robos en las condiciones de prepandemia. Se concluye que el análisis de datos abiertos brinda información relevante para la toma de decisiones de los ciudadanos Universidad Pedagógica y Tecnológica de Colombia 2023-02-15 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16059 10.19053/20278306.v13.n1.2023.16059 Revista de Investigación, Desarrollo e Innovación; Vol. 13 No. 1 (2023): Enero-Junio; 173-184 Revista de Investigación, Desarrollo e Innovación; Vol. 13 Núm. 1 (2023): Enero-Junio; 173-184 2389-9417 2027-8306 spa https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16059/13097 https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16059/13561 Derechos de autor 2023 Revista de Investigación, Desarrollo e Innovación |
| spellingShingle | open data; theft; machine learning; business intelligence datos abiertos; robo; aprendizaje automático; inteligencia de negocios Maestre-Gongora, Gina Acuña-Castellanos, Camilo Andrés Londoño-Bedoya, Edwar García-García, Sergio Data analysis of thefts in the city of Medellin from a descriptive approach |
| title | Data analysis of thefts in the city of Medellin from a descriptive approach |
| title_alt | Análisis de datos sobre los hurtos en la ciudad de Medellín desde un enfoque descriptivo |
| title_full | Data analysis of thefts in the city of Medellin from a descriptive approach |
| title_fullStr | Data analysis of thefts in the city of Medellin from a descriptive approach |
| title_full_unstemmed | Data analysis of thefts in the city of Medellin from a descriptive approach |
| title_short | Data analysis of thefts in the city of Medellin from a descriptive approach |
| title_sort | data analysis of thefts in the city of medellin from a descriptive approach |
| topic | open data; theft; machine learning; business intelligence datos abiertos; robo; aprendizaje automático; inteligencia de negocios |
| topic_facet | open data; theft; machine learning; business intelligence datos abiertos; robo; aprendizaje automático; inteligencia de negocios |
| url | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/16059 |
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