Examinando por Materia "PCA"

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    Análisis de discurso basado en modelos grandes de lenguaje
    (Universidad EAFIT, 2024) Jiménez Jaimes, Edgar Leandro; Montoya Múnera, Edwin Nelson
    This thesis explores the implementation of natural language processing techniques and large language models (LLMs) to support discourse analysis tasks in the context of the "Tenemos que hablar Colombia" program. Techniques such as topic modeling, sentiment analysis, clustering, visualization, and the creation of a conversational assistant based on Retrieval Augmented Generation (RAG) have been addressed using advanced text modeling, vector embeddings, and prompt engineering approaches. A text classification model focused on predicting the label of the verbal indicator variable, assigned manually by the interviewer, is also presented, although this model is not directly applied to discourse analysis. This work adds to the studies of the " Tenemos que hablar Colombia " program, where other authors have contributed through computational linguistics analysis and machine learning techniques. Using advanced NLP techniques, we have sought to improve the interpretation of text data and its application in discourse analysis. The results have shown improvements in the accuracy of data classification and analysis through the techniques explored, providing a better understanding of citizen perceptions.
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    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, Nicolás
    Urban 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.
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