Using publicly available data for predicting socioeconomic values in urban context

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dc.catalogadorvdr
dc.contributor.advisorReutter de la Maza, Juan
dc.contributor.authorOjeda Aguila, Maximiliano
dc.contributor.otherPontificia Universidad Católica de Chile. Escuela de Ingeniería
dc.date.accessioned2025-02-27T15:28:29Z
dc.date.available2025-02-27T15:28:29Z
dc.date.issued2024
dc.descriptionTesis (Master of Science in Engineering)--Pontificia Universidad Católica de Chile, 2024
dc.description.abstractUrban transportation networks are recognized for their pivotal role in forecasting city indicators and facilitating efficient planning and management. However, despite the increase of methodologies and models harnessing machine learning advancement to forecast these values, challenges persist in scenarios where direct demographic or economic data are limited or unavailable. In this work, we propose an approach to infer socioeconomic information in an urban context without relying on traditional, official data sources, but rather focusing on publicly available data relating to the digital footprints of the cities’ inhabitants. We leverage Graph Neural Network (GNN) models to capture the spatial relationships inherent in map data while integrating perceptual features extracted from images to enhance predictive accuracy. Our results demonstrate that the combination of these data sources enables a GNN to achieve robust performance in predicting socioeconomic indicators, particularly in settings where traditional demographic and economic data may be sparse or unavailable. Through our analysis, we show that while perceptual features alone offer substantial predictive power, the inclusion of map topology through GNN models provides crucial context, leading to better generalization and more reliable predictions across different urban areas.
dc.description.funderInstituto Milenio Fundamento de los Datos (IMFD)
dc.fechaingreso.objetodigital2025-02-27
dc.format.extentxi, 47 páginas
dc.fuente.origenSRIA
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/102274
dc.information.autorucEscuela de Ingeniería; Reutter de la Maza, Juan; 0000-0002-2186-0312; 126898
dc.information.autorucEscuela de Ingeniería; Ojeda Aguila, Maximiliano; 0009-0005-0379-0743; 1246333
dc.language.isoen
dc.nota.accesoContenido completo
dc.rightsacceso abierto
dc.subjectGraph neural networks
dc.subjectUrban transportation
dc.subjectPerceptual values
dc.subjectSocioeconomic forecasting
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.subject.ods11 Sustainable cities and communities
dc.subject.odspa11 Ciudades y comunidades sostenibles
dc.titleUsing publicly available data for predicting socioeconomic values in urban context
dc.typetesis de maestría
sipa.codpersvinculados126898
sipa.codpersvinculados1246333
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3.01 Tesis magíster