Delito de hurto a personas en Colombia: una perspectiva
| dc.contributor.advisor | Granados Erazo, Oscar Mauricio | |
| dc.creator | Prieto Roncancio, Javier Dario | |
| dc.creator | Guacaneme Medina, Erika Margareth | |
| dc.creator | Rubio Ropero, Mauricio Gerardo | |
| dc.date.accessioned | 2026-01-13T21:14:08Z | |
| dc.date.created | 2025-12-05 | |
| dc.description.abstract | El hurto a personas es uno de los delitos de mayor impacto en la percepción de seguridad ciudadana y la calidad de vida en entornos urbanos. Tradicionalmente, la respuesta a este flagelo ha sido predominantemente reactiva, enfocada en la investigación posterior al hecho. Sin embargo, la irrupción y madurez de la analítica de datos y los datos masivos han transformado este paradigma, permitiendo a las fuerzas de seguridad y autoridades diseñar estrategias de prevención proactiva basadas en la evidencia. La analítica de datos se define, en este contexto, como el proceso de examinar conjuntos de datos masivos y variados (estructurados y no estructurados) para descubrir patrones, tendencias, correlaciones y otra información valiosa que ayude a la toma de decisiones. En la lucha contra el hurto a personas, su uso se centra en dos pilares fundamentales: Primero, una policía predictiva y Focalización Espacial: Utilizando algoritmos de aprendizaje automático, se analizan grandes volúmenes de datos históricos delictivos (lugar, hora, modus operandi, clima, variables socioeconómicas) para construir modelos predictivos. Estos modelos tienen como objetivo principal identificar con alta precisión los "puntos calientes" y los momentos de mayor riesgo, lo que permite a las autoridades focalizar el patrullaje y los recursos de vigilancia de manera inteligente y dinámica, disuadiendo la ocurrencia del hurto antes de que suceda. La analítica proporciona una visión detallada y en tiempo real del comportamiento del hurto (ej. el aumento de casos en días y horas específicas, la prevalencia de ciertos tipos de armas o el perfil de las víctimas). Esta capacidad de monitoreo constante es esencial para evaluar la eficacia de las medidas de seguridad implementadas y realizar ajustes estratégicos de forma continua, asegurando que las políticas de prevención sean eficientes y se adapten a las tácticas cambiantes de los delincuentes. La implementación de la analítica de datos ofrece una herramienta para entender la dinámica criminal del hurto y se posiciona como una herramienta para la gestión de la seguridad, transformando los datos brutos en inteligencia accionable para la reducción efectiva de este delito de alto impacto. | |
| dc.description.abstractenglish | Theft from individuals is one of the crimes with the greatest impact on the perception of citizen security and quality of life in urban environments. Traditionally, the response to this scourge has been predominantly reactive, focused on investigation after the fact. However, the emergence and maturation of data analytics and big data have transformed this paradigm, allowing law enforcement and authorities to design proactive, evidence-based prevention strategies. Data analytics is defined, in this context, as the process of examining massive and varied datasets (structured and unstructured) to discover patterns, trends, correlations, and other valuable information that aids decision-making. In the fight against theft from individuals, its use focuses on two fundamental pillars: First, predictive policing and spatial targeting: Using machine learning algorithms, large volumes of historical crime data (location, time, modus operandi, weather, socioeconomic variables) are analyzed to build predictive models. These models aim to identify "hot spots" and times of greatest risk with high precision, allowing authorities to intelligently and dynamically focus patrols and surveillance resources, deterring theft before it occurs. Analytics provides a detailed, real-time view of theft patterns (e.g., increased cases on specific days and times, the prevalence of certain types of weapons, or victim profiles). This constant monitoring capability is essential for evaluating the effectiveness of implemented security measures and making continuous strategic adjustments, ensuring that prevention policies are efficient and adapt to criminals' evolving tactics. Implementing data analytics offers a tool for understanding the criminal dynamics of theft and positions itself as a security management tool, transforming raw data into actionable intelligence for the effective reduction of this high-impact crime. | |
| dc.format.extent | 41 páginas | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12010/38748 | |
| dc.language.iso | es | |
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| dc.subject | Analítica de datos | |
| dc.subject | Hurto a personas | |
| dc.subject | Seguridad ciudadana | |
| dc.subject | Estrategias de prevención | |
| dc.subject.keyword | Data analytics | |
| dc.subject.keyword | Theft from persons | |
| dc.subject.keyword | Citizen security | |
| dc.subject.keyword | Prevention strategies | |
| dc.subject.lemb | Seguridad ciudadana | |
| dc.subject.lemb | Análisis de datos | |
| dc.subject.lemb | Prevención del delito | |
| dc.title | Delito de hurto a personas en Colombia: una perspectiva | |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc |
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