Tracking Hurricane Dorian in GDELT and Twitter

GDELT is a machine coded database of events that uses both foreign and domestic news feeds and contains over a quarter of a billion worldwide event records categorized into three hundred categories. This paper compares the spatial footprint of GDELT event mentions with those of event related geo-tagged tweets for Hurricane Dorian in the South-Eastern United States. Besides examining event related GDELT and Twitter data abundance, the study relates areas of elevated GDELT news and tweeting activities to the locations of the hurricane track over a six-day period, and statistically analyzes distances between daily GDELT event mentions and tweets, and the hurricane center on different days. It assesses the potential role of the geographic coverage of the cone in hurricane prediction maps on the level of event related news and tweeting activities. The study also discusses pros and cons of both data sources for event tracking with regards to data abundance, spatial and temporal resolution, and thematic accuracy.

Perceptions of Hurricane-Track Forecasts in the United States

Hurricanes Isaac (2012), Harvey (2017), and Irma (2017) were storms with different geophysical characteristics and track forecast consistencies. Despite the differences, common themes emerged from the perception of track forecasts from evacuees for each storm. Surveys with a mixture of closed and open-ended responses were conducted during the evacuations of each storm while the storm characteristics and decision-making were fresh in the minds of evacuees. Track perception accuracy for each evacuee was quantified by taking the difference between three metrics: perceived track and official track (PT − OT), perceived track and forecast track (PT − FT), and home location and perceived track (HL − PT). Evacuees from Hurricanes Isaac and Harvey displayed a tendency to perceive hurricane tracks as being closer to their home locations than what was forecast to occur and what actually occurred. The large sample collected for Hurricane Irma provided a chance to statistically verify some of the hypotheses generated from Isaac and Harvey. Results from Hurricane Irma confirmed that evacuees expected a storm to be closer to their home locations after controlling for regional influences. Furthermore, participants with greater previous hurricane experience perceived a track as being closer to their home locations, and participants residing in zip codes corresponding with nonmandatory evacuation zones also perceived tracks as being closer to their home locations. These findings suggest that most evacuees from hurricanes in the United States appear to perceive storms as being closer to their home locations than they are and overestimate wind speeds at their homes, thus overestimating the true danger from landfalling hurricanes in many storms.

The Powerful Influence of Marks: Visual and Knowledge-Driven Processing in Hurricane Track Displays

Given the widespread use of visualizations to communicate hazard risks, forecast visualizations must be as effective to interpret as possible. However, despite incorporating best practices, visualizations can influence viewer judgments in ways that the designers did not anticipate. Visualization designers should understand the full implications of visualization techniques and seek to develop visualizations that account for the complexities in decision-making. The current study explores the influence of visualizations of uncertainty by examining a case in which ensemble hurricane forecast visualizations produce unintended interpretations. We show that people estimate more damage to a location that is overlapped by a track in an ensemble hurricane forecast visualization compared to a location that does not coincide with a track. We find that this effect can be partially reduced by manipulating the number of hurricane paths displayed, suggesting the importance of visual features of a display on decision making. Providing instructions about the information conveyed in the ensemble display also reduced the effect, but importantly, did not eliminate it. These findings illustrate the powerful influence of marks and their encodings on decision-making with visualizations.

Will Global Warming Make Hurricane Forecasting More Difficult?

Hurricane track forecasts have improved steadily over the past few decades, yet forecasting hurricane intensity remains challenging. Of special concern are the rare instances of tropical cyclones that intensify rapidly just before landfall, catching forecasters and populations off guard, thereby risking large casualties. Here, we review two historical examples of such events and use scaling arguments and models to show that rapid intensification just before landfall is likely to become increasingly frequent and severe as the globe warms.