Guatemala faces acute disaster vulnerability due to the combined effects of geological hazards, climatic variability, and socio-economic inequality. This study applies a mixed-method design, combining descriptive interpretation of disaster trends with quantitative statistical modeling to analyze disaster patterns and socio-economic drivers at national and municipal levels. National-scale disaster events (n = 171, 1902–2024) were obtained from EM-DAT, while municipal records (n = 11,256, 1988–2015) came from DesInventar. The analysis employed multiple linear regression, Poisson regression, and Random Forest models to identify predictors of disaster occurrence. Results indicate that total population (β = 0.00084, p < 0.001), road distance (β = −0.037, p < 0.001 in linear; β = 0.0016, p < 0.001 in Poisson), and Human Development Index (β = 118.2, p < 0.001) were consistently significant. Municipalities with higher population density and infrastructure, mostly urban areas, report more disasters, reflecting both greater exposure and improved reporting. Earthquakes and floods are the most lethal hazards, while hydrometeorological events show strong links to El Niño–Southern Oscillation variability, aggravating food insecurity and economic losses, particularly in rural and indigenous communities dependent on rainfed subsistence agriculture. The findings highlight the need for differentiated data-driven disaster risk reduction strategies that address both urban exposure and rural vulnerability. Key recommendations include strengthening early warning systems, improving infrastructure resilience, incorporating traditional knowledge into risk assessments, and integrating socio-economic indicators into national DRR planning to enhance Guatemala’s preparedness and resilience.
Quesada-Román et al. (2026) studied this question.