Examinando por Materia "Aprendizaje no supervisado"
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Publicación Detección de fraude en reclamaciones de hogar bajo un enfoque de aprendizaje no supervisado : un caso de estudio(Universidad EAFIT, 2022) Acevedo Maya, Sergio; Almonacid Hurtado, Paula MaríaPublicación Entropy-based graph construction methods for unsupervised data structure detection(Universidad EAFIT, 2021) Ariza Jiménez, Leandro Fabio; Quintero Montoya, Olga Lucía; Pinel Peláez, Nicolás; Centro de Excelencia y apropiación en Big Data y Data Analytics (Alianza CAOBA); Universidad EAFITPublicación Estimación de precio de oferta para una planta hidroeléctrica de baja regulación en la bolsa de energía(Universidad EAFIT, 2021) Mosquera Galvis, Liceth Cristina; Quintero Montoya,Olga Lucia; CelsiaÍtem Morfología urbana y patrones de movilidad : un análisis topológico y espacial de redes(Universidad EAFIT, 2025) Riascos Goyes, Juan Fernando ; Ospina Zapata, Juan Pablo; Guarín-Zapata, NicolásUrban morphology has long been recognized as a factor shaping human mobility, yet comparative and formal classifications of urban form across metropolitan areas remain limited. Building on theoretical principles of urban structure and advances in unsupervised learning, we systematically classified the built environment of nine U.S. metropolitan areas using structural indicators such as density, connectivity, and spatial configuration. The resulting morphological types were linked to mobility patterns through descriptive statistics, marginal effects estimation, and post hoc statistical testing. Here we show that distinct urban forms are systematically associated with different mobility behaviors, such as reticular morphologies being linked to significantly higher public transport use and reduced car dependence, while organic forms are associated with increased car usage, and substantial declines in public transport and active mobility. These effects are statistically robust, highlighting that the spatial configuration of urban areas plays a fundamental role in shaping transportation choices. Our findings extend previous work by offering a reproducible framework for classifying urban form and demonstrate the added value of morphological analysis in comparative urban research. These results suggest that urban form should be treated as a key variable in mobility planning and provide empirical support for incorporating spatial typologies into sustainable urban policy design.