Chile is a country exposed to various types of natural hazards, such as earthquakes, tsunamis, volcanic eruptions, floods, and wildfires. These are sometimes extremely severe and cause great damage to society; it is then when we label them as disasters. However, it is said that natural disasters are man-made, because we can adapt our cities and capabilities to tolerate these hazards and prevent them from becoming disasters.
This talk will address how data science can be used to foster disaster science and risk reduction efforts. Our focus will be on applications such as the characterization of seismic hazards, the problem of interdependent infrastructures, the analysis of Web data, and urban planning, which methodologically span statistical modeling, complex network analysis, natural language processing, game theory, among other methods. Finally, we will conclude by providing a landscape of the problems covered in disaster science that can be addressed using data science.