Using Non-authoritative Sources During Emergencies in Urban Areas - Computational Approaches for Urban Environments - page 328

non-authoritative data including social media, news, tweets, and mobile phone
data. Specifically, two applications are presented for transportation infrastructure
assessment and emergency evacuation.

Keywords Infrastructure assessment ￿ Evacuation ￿ Remote sensing ￿ Inundation
modeling ￿ Social media ￿ Geospatial analysis ￿ Big data

14.1 Introduction

Never in the history of humankind have we known so much about our planet. Never
in the history of humankind have we had such easy access to data. Never in the
history of humankind has our civilization been so much at risk.
Hazards pose a constant threat to the development and sustainment of our infras-
tructure and our society. Hazards can be natural, anthropogenic, or technological.
They are, respectively, events that naturally occur, events resulting from human
activities or accidents, or the catastrophic collapse of infrastructure, such as roads,
communication networks, or power grids, which are needed for our society to
function.
A single catastrophic event can claim thousands of lives; cause billions of dollars
of damage; trigger an economic depression that might directly or indirectly affect
the entire world; destroy natural landmarks; cause tsunamis, floods, and landslides;
render a large territory uninhabitable; and destabilize the military and political
balance in a region (Cutter 1993 ; Alexander 2002 ; Wisner et al. 2004 ). Such
potential catastrophic consequences are due to the emergence of megacities and
the proliferation of nuclear power plants and nuclear waste storage facilities, high
dams, and other facilities whose destruction poses an unacceptable risk of global
reach (Freudenburg et al. 2008 ;Casti 2012 ). Thus, the study of natural hazards and
of the processes that govern their occurrence has become a fundamental challenge
for the survival of our civilization.
Advances in our ability to observe the Earth and its environment through the
use of air-, space-, and ground-based sensors has led to the collection of massive
amounts of dynamic and geographically distributed spatiotemporal data. Numerical
models are initialized with these high-resolution observations to forecast the future
or to simulate the past, generating simulations that can be several orders of
magnitude larger than the initial observations. Remote sensing data from air- and
space-borne platforms have also become the de facto standard for providing high-
resolution information for the assessment, relief, and mitigation of damaged areas
during and after emergencies caused by natural, human-made, and technological
disasters (Jensen and Cowen 1999 ; Voigt et al. 2007 ). However, due to limitations
in orbital revisit time, sensor characteristics, and the presence of clouds, there may
be gaps in these remote sensing data.
This chapter presents applications for data collected from non-authoritative
sources to fill the gaps in remote sensing data during disasters and emergencies.
Non-authoritative sources include data volunteered by citizens (also known as
volunteered geographic information or VGI (Goodchild 2007 ; Sui and Goodchild