Joint Source Selection and Data Extrapolation in Social Sensing for Disaster Response

TL;DR

This paper develops a novel approach combining source selection and data extrapolation techniques to improve social sensing predictions during disaster scenarios, demonstrated with Hurricane Sandy data.

Contribution

It introduces a joint source selection and extrapolation algorithm that enhances inference quality in social sensing for disaster response, addressing the bi-modal damage propagation challenge.

Findings

Consistently accurate predictions during Hurricane Sandy crisis

Enhanced inference quality through combined source selection and extrapolation

Addresses bi-modal damage propagation in complex systems

Abstract

This paper complements the large body of social sensing literature by developing means for augmenting sensing data with inference results that "fill-in" missing pieces. It specifically explores the synergy between (i) inference techniques used for filling-in missing pieces and (ii) source selection techniques used to determine which pieces to retrieve in order to improve inference results. We focus on prediction in disaster scenarios, where disruptive trend changes occur. We first discuss our previous conference study that compared a set of prediction heuristics and developed a hybrid prediction algorithm. We then enhance the prediction scheme by considering algorithms for sensor selection that improve inference quality. Our proposed source selection and extrapolation algorithms are tested using data collected during the New York City crisis in the aftermath of Hurricane Sandy in November 2012. The evaluation results show that consistently good predictions are achieved. The work is notable for addressing the bi-modal nature of damage propagation in complex systems subjected to stress, where periods of calm are interspersed with periods of severe change. It is novel in offering a new solution to the problem that jointly leverages source selection and extrapolation components thereby improving the results.