Using Non-authoritative Sources During Emergencies in Urban Areas - Computational Approaches for Urban Environments - page 349

14.4 Conclusions

During disasters and emergencies in urban areas, timely and accurate information
of road networks, infrastructure conditions, and locations of citizens is crucial.
But this information can be limited, incomplete, lengthy to acquire, or out of
date. Although not necessarily created or posted with the intent of being used
for scientific research, non-authoritative data can be harvested during disasters
and emergencies to provide timely, on-the-ground information. Although often
viewed with uncertainty because of concerns related to, for example, producer
anonymity and lack of authoritative verification, these data often provide relevant
information that may be difficult for authorities to collect. Non-authoritative sources
also provide an additional layer of subjective information which can indicate the
severity of potentially dangerous events, as well as how citizens are reacting to the
developing danger or coping with damage resulting from a disaster. By presenting
varied applications, such as damage assessments and emergency evacuations, the
practicality of non-authoritative sources and how they can add value to official data
is demonstrated. It is hoped that one day this research will help save lives.

Acknowledgements Work performed under this project has been partially supported by the Office
of the Assistant Secretary for Research and Technology, US Department of Transportation award
# RITARS-12-H-GMU (GMU #202717) and also partially funded by the Office of Naval Research
(ONR) award #N00014-14-1-0208 (PSU #171570) and the Office of Naval Research (ONR)
award #N00014-14-1-0208 (PSU #171570). DISCLAIMER: The views, opinions, findings and
conclusions reflected in this presentation are the responsibility of the authors only and do not
represent the official policy or position of the USDOT/OST-R, or any State or other entity.

References

Alexander DE (2002) Principles of emergency planning and management. Oxford University Press,
Oxford/New York
Atkinson PM, Tatnall A (1997) Introduction neural networks in remote sensing. Int J Remote Sens
18(4):699-709
Benediktsson JA, Swain PH, Ersoy OK (1990) Neural network approaches versus statistical
methods in classification of multisource remote sensing data. IEEE Trans Geosci Remote Sens
28(4):540-552
Berenbrok C, Mason RR, Blanchard SF (2009) Mapping Hurricane Rita inland storm tide. J Food
Risk Manag 2:76-82
Booij N, Ris RC, Holthuijsen LH (1999) A third generation wave model for coastal regions: model
description and validation. J Geophys Res 104:7649-7666
Casti JL (2012) X-events: the collapse of everything. HarperCollins, New York
Chen C, Liu H, Beardsley R (2003) An unstructured grid, finite-volume, three-dimensional,
primitive equations ocean model: application to coastal ocean and estuaries. J Atmos Ocean
Technol 20:159-186
Cutter SL (1993) Living with risk: the geography of technological hazards. Edward Arnold,
London