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Fig. 3 Predictive capacity of Hurricane Sandy’s digital traces.

Published byArnfinn Elias Knudsen Modified over 5 years ago

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Presentation on theme: "Fig. 3 Predictive capacity of Hurricane Sandy’s digital traces."— Presentation transcript:

1 Fig. 3 Predictive capacity of Hurricane Sandy’s digital traces.
Predictive capacity of Hurricane Sandy’s digital traces. The horizontal axis is an offset (in hours) with respect to the time of hurricane landfall (00:00 UTC on 30 October 2012). (A) The number of messages as a function of time (labeled on the secondary y axis on the right) and the number of “active” (with at least one message posted) ZCTAs (labeled on the primary y axis on the left). (B) Evolution of the rank correlation coefficients between the normalized per-capita activity (number of original messages divided by the population of a corresponding ZCTA) and per-capita damage (composed of FEMA individual assistance grants and Sandy-related insurance claims). In addition, the dashed trend shows Kendall rank correlations between average sentiment and per-capita damage. The correlation increases from the prelandfall stage to the postlandfall stage of the hurricane, with a drop on the day of hurricane landfall. We conclude that the postdisaster stage, or persistent activity on the topic in the immediate aftermath of an event, is a good predictor of damage inflicted locally. The strength of the average sentiment of tweets does not seem to be a good predictor, at least at this level of spatial granularity (ZCTA resolution). Yury Kryvasheyeu et al. Sci Adv 2016;2:e Copyright © 2016, The Authors

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