ABSTRACT
The study of disaster events and their impact in the urban space has been traditionally conducted through manual collections and analysis of surveys, questionnaires and authority documents. While there have been increasingly rich troves of human behavioral data related to the events of interest, the ability to obtain hindsight following a disaster event has not been scaled up. In this paper, we propose a novel approach for analyzing events called PairFac. PairFac utilizes discriminant tensor analysis to automatically discover the impact of a major event from rich human behavioral data. Our method aims to (i) uncover the persistent patterns across multiple interrelated aspects of urban behavior (e.g., when, where and what citizens do in a city) and at the same time (ii) identify the salient changes following a potentially impactful event. We show the effectiveness of PairFac in comparison with previous methods through extensive experiments. We also demonstrate the advantages of our approach through case studies with real-world traffic sensor data and social media streams surrounding the 2015 terrorist attacks in Paris. Our work has both methodological contributions in studying the impact of an external stimulus on a system as well as practical implications in the area of disaster event analysis and assessment.
- Evrim Acar et al. "Scalable Tensor Factorizations with Missing Data." In: SIAM 2011.Google Scholar
- Harshavardhan Achrekar et al. "Predicting flu trends using twitter data". In: INFOCOM WKSHPS 2011.Google Scholar
- Keith M Ashman, Christina M Bird, and Steven E Zepf. "Detecting bimodality in astronomical datasets". In: arXiv preprint astro-ph/9408030 (1994).Google Scholar
- James P Bagrow, Dashun Wang, and Albert-Laszlo Barabasi. "Collective response of human populations to large-scale emergencies". In: PloS one (2011).Google Scholar
- Amir Beck and Marc Teboulle. "A fast iterative shrinkage- thresholding algorithm for linear inverse problems". In: SIAM journal on imaging sciences (2009). Google ScholarDigital Library
- Nicholas Diakopoulos, Mor Naaman, and Funda Kivran-Swaine. "Diamonds in the rough: Social media visual analytics for journalistic inquiry". In: VAST 2010.Google ScholarCross Ref
- Sunil Kumar Gupta et al. "Nonnegative shared subspace learning and its application to social media retrieval". In: ACM SIGKDD 2010. Google ScholarDigital Library
- Sunil Kumar Gupta et al. "Regularized nonnegative shared subspace learning". In: Data mining and knowledge discovery (2013). Google ScholarDigital Library
- R.A. Harshman. "Foundations of the PARAFAC procedure: Models and conditions for an" explanatory" multimodal factor analysis". In: (1970).Google Scholar
- Andreas Kaltenbrunner et al. "Urban cycles and mo- bility patterns: Exploring and predicting trends in a bicycle-based public transport system". In: Pervasive and Mobile Computing (2010). Google ScholarDigital Library
- Hannah Kim et al. "Simultaneous Discovery of Common and Discriminative Topics via Joint Nonnegative Matrix Factorization". In: ACM SIGKDD 2015. Google ScholarDigital Library
- Tamara Gibson Kolda. Multilinear operators for higher- order decompositions.Google Scholar
- Yu-Ru Lin and Drew Margolin. "The ripple of fear, sympathy and solidarity during the Boston bombings". In: EPJ Data Science (2014).Google Scholar
- Wei Liu et al. "Mining labelled tensors by discovering both their common and discriminative subspaces". In: SIAM 2013.Google Scholar
- Michael Mathioudakis and Nick Koudas. "Twittermonitor: trend detection over the twitter stream". In: ACM SIGMOD 2010. Google ScholarDigital Library
- Sharad Mehrotra et al. "Technological Challenges in Emergency Response". In: IEEE Intelligent Systems (2013). Google ScholarDigital Library
- William E Schlenger et al. "Psychological reactions to terrorist attacks: findings from the National Study of Americans? Reactions to September 11". In: Jama (2002).Google ScholarCross Ref
- Xuan Song et al. "Intelligent system for human behavior analysis and reasoning following large-scale disasters". In: IEEE Intelligent Systems (2013). Google ScholarDigital Library
- Andranik Tumasjan et al. "Predicting elections with twitter: What 140 characters reveal about political sentiment." In: ICWSM (2010).Google Scholar
- Direction de la Voirie et des déplacements - Service des Déplacements. Données trafic issues des capteurs permanents. http://opendata.paris.fr/explore/dataset/comptages-routiers-permanents/.Google Scholar
- Qi Wang and John E Taylor. "Quantifying human mobility perturbation and resilience in Hurricane Sandy". In: PLoS one (2014).Google Scholar
- Xiaofeng Wang, Matthew S. Gerber, and Donald E. Brown. "Automatic crime prediction using events extracted from twitter posts". In: SBP. 2012. Google ScholarDigital Library
- Yangyang Xu. "Alternating proximal gradient method for nonnegative matrix factorization". In: arXiv preprint arXiv:1112.5407v1 (2011).Google Scholar
- Yangyang Xu and Wotao Yin. "A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion". In: SIAM Journal on imaging sciences (2013).Google Scholar
- Dingqi Yang, Daqing Zhang, and Bingqing Qu. "Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks". In: ACM TIST (2016). Google ScholarDigital Library
- Fuzheng Zhang et al. "Sensing the pulse of urban refueling behavior: A perspective from taxi mobility". In: ACM TIST (2015). Google ScholarDigital Library
Index Terms
- PairFac: Event Analytics through Discriminant Tensor Factorization
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