  Natural hazard risk management is a demanding interdisciplinary task. It
requires domain knowledge, integration of robust computational methods, and
effective use of complex datasets. However, existing solutions tend to focus on
specific aspects, data, or methods, limiting their impact and applicability.
Here, we present a general data-driven framework to support risk assessment and
policy making illustrating its usage in the context of fire hazard by
integrating three unique datasets of fire behavior, street network, and census
data for the whole state of California. We show that integrating spatial
complexity by including a fire behavior layer and a socio-demographic layer
changes the universal function observed in previous optimization frameworks
that only work with the accessibility of facilities. These results open avenues
for the future development of flexible interdisciplinary frameworks in natural
hazards management using complex large-scale data.
