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Title Mobility Pattern Analysis For Power Restoration Activities Using Geo-Tagged Tweets
ID_Doc 37366
Authors Kar B.; Ethridge J.
Year 2019
Published Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019
DOI http://dx.doi.org/10.1145/3356395.3365547
Abstract In this study, we analyzed mobility patterns of at-risk populations affected by an extreme event using geo-tagged tweets to geo-target power restoration efforts. Unlike other studies that have used tweets to facilitate emergency management activities, we used 1.5 million geo-tagged tweets generated during Hurricane Sandy (2012) to determine the mobility patterns and geospatial distribution of impacted populations who experienced power outage before, during and after the hurricane. We implemented a three-step analytical framework to: (i) analyze tweet contents with visual methods, including dendrograms, word clouds to identify common keywords pertaining to power outage; (ii) identify target users whose tweets contained information about power outages; and (iii) create a user-tweet locations matrix and an origin-destination matrix to examine clusters of target users and their mobility patterns. Preliminary results indicate that potential clusters were present in and around New York city, Philadelphia, Washington D.C. and Baltimore, which were used as potential evacuation destination cities after hurricane Sandy. The travel pattern and destination information can be used to (i) mobilize restoration efforts by utility companies and (ii) address resource allocation needs both in impacted and destination cities. Future work will focus on analyzing potential destinations for different origins and travel-time to identify evacuation routing patterns. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Author Keywords Crisis informatics; Mobility pattern analysis; Natural language processing; Power restoration; Situational awareness


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