Examinando por Materia "Spatial distribution"

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    Influencia de la distribución espacial de modelos de amenaza y exposición en la evaluación del riesgo sísmico urbano
    (Universidad EAFIT, 2025) Betancur Calle, Juan Felipe; Acevedo Jaramillo, Ana Beatriz
    Urban seismic risk assessment, which involves estimating the consequences that earthquakes may generate in a given location, is associated with numerous uncertainties. These uncertainties affect the results and may lead to underestimations or overestimations of risk metrics, including economic and human losses. For researchers, policymakers, and financial entities, obtaining the most accurate results while minimizing errors is crucial. Seismic risk assessment requires a model of the local seismic hazard (intensities that earthquakes can generate), a description of the assets of interest in the area (exposure model), and knowledge of the susceptibility of exposed elements to damage (vulnerability model). The spatial distribution of hazard and exposure models influences risk results, as it determines the ground motion intensities that exposed elements will experience. This spatial distribution, known as aggregation, refers to grouping multiple assets within a single location for risk assessment. Consequently, all assets within the same aggregation unit are subjected to the same level of seismic hazard, altering the actual distances to seismic sources. Thus, model aggregation impacts the estimation of consequences such as economic losses and human casualties. This study evaluates the influence of the spatial distribution of hazard and exposure models on urban seismic risk assessment, applied to three cities in Antioquia: Medellín, Bello, and Girardota. The evaluation was conducted through a sensitivity analysis of seismic risk results, considering different aggregation units and comparing economic loss metrics, fatalities, and the number of buildings with complete damage using OpenQuake. The results indicate that the level of aggregation in the exposure model and the method used to select soil characteristics significantly influence the obtained estimates. A higher resolution in exposure models and a detailed soil classification model provide more accurate estimates. Notably, a differentiated soil classification model shows acceptable results even when the exposure model has very low resolution.