Hafner, ChristianDujardin, SébastienPaulus, RobertRobertPaulus2025-05-142025-05-142025-05-142024https://hdl.handle.net/2078.2/39673This thesis explores spatio-temporal modeling techniques to analyze social media user metrics, using Meta's data as a proxy for population distribution in the Philippines. The study examines various approaches, including econometric models, kriging, and Bayesian modeling for Gaussian processes, to address spatial and temporal auto-correlations in the data. Kernel-based methods are utilized both in spatio-temporal modeling, particularly through Gaussian processes, and in the development of a dynamic Crisis Sensitivity Index (CSI) during typhoon events. Overall, the thesis demonstrates the effectiveness of these advanced techniques in handling complex spatio-temporal datasets for population analysis and crisis impact assessment.Spatio-temporal analysisSpace-timeTime seriesKrigingGaussian processesClusteringKernelCrisisSocial media dataBayesiantext::thesis::master thesisthesis:48828