Darling, MathewWilson, ThomasBradley, BrendonOrchiston, CarolineAdams, Ben2019-09-242019-09-242019http://hdl.handle.net/10092/17311Underpinning strong disaster risk reduction initiatives are representative disaster risk assessments of communities, regions or nations. Often risk modelling focuses on understanding the physical hazard and its spatial extent; however, it often draws on old or static population datasets. We consider geospatial and big data methods to understanding fluctuations in populations, to ultimately better inform disaster risk assessments. This is particularly relevant in areas of both significant fluctuations in population movement (through tourism), and high disaster risk. We consider the case study of the Alpine Fault in the South Island of New Zealand. The initial findings of this research draw on the case study of Rakiura, Stewart Island, where the total fluctuations in the population are known (though passenger movements through the Foveaux Strait), and we compare these to more novel indicators; such as infrastructure load, social media data, and visitor counter networks. We then consider how such indicators are applicable at a regional national scale to understand fluctuations in population movement, to better inform disaster risk modelling.enCC-BY 4.0 InternationalConference Contributions - Other