Data extrapolation in social sensing for disaster response

"This thesis complements the large body of social sensing literature by developing means for augmenting sensing data with inference results that ""fill-in"" missing pieces. Unlike trend-extrapolation methods, we focus on prediction in disaster scenarios where disruptive trend changes occur. A set of prediction heuristics (and a standard trend extrapolation algorithm) are compared that use either predominantly-spatial or predominantly-temporal correlations for data extrapolation purposes. The evaluation shows that none of them do well consistently. This is because monitored system state, in the aftermath of disasters, alternates between periods of relative calm and periods of disruptive change (e.g., aftershocks). A good prediction algorithm, therefore, needs to intelligently combine time-based data extrapolation during periods of calm, and spatial data extrapolation during periods of change. The thesis develops such an algorithm. The algorithm is tested using data collected during the New York City crisis in the aftermath of Hurricane Sandy in November 2012. Results show that consistently good predictions are achieved. The work is unique in addressing the bi-modal nature of damage propagation in complex systems subjected to stress, and offers a simple solution to the problem."

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Gu, Siyu

Contributors dc:contributor

• Abdelzaher, Tarek F.

• Social Sensing
• Disaster Response
• Data Extrapolation

Statement dc:rights

• Copyright 2014 Siyu Gu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/49761

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/49761