Abstract
Abstract. Social media could be very useful source of data for a people interested in disasters, since it can provide them with on-site information. Posted georeferenced messages and images can help to understand the situation of the area affected by the event. Considering this type of resource as a real-time crowdsource of crisis information, the spatial distribution of geolocated posts related to an event can represent an early indicator of the severity of impact. The aim of this paper is to explore the spatial distribution of Twitter posts related to hurricane Michael, occurred in the USA in 2018 and to analyse their potential in providing a fast insight about the event impact. Kernel density estimation has been applied to explore the spatial distribution of Twitter posts, after which Hot Spot analysis has been performed in order to analyse the spatiotemporal distribution of the data. Hot Spot analysis has shown to be the most comprehensive analysis, detecting the area of high impact. The Kernel density map has shown to be useful as well.
Highlights
Social media have shown a significant contribution in disaster relief
Focusing on the tweets that carry a geographic reference, a “picture” of what is happening in a specific place could be made mapping the posts over the area of interest. This data source can be considered as a real-time crowdsource of crisis information, the spatial distribution of geolocated tweets related to an event can represent an early indicator of the severity of impact
The aim of this paper is to explore the spatial distribution of Twitter posts related to a disaster and to analyse their potential in providing a fast insight regarding its impact
Summary
Social media have shown a significant contribution in disaster relief. They could be very valuable sources of the on-site disasterrelated information shared by the affected people. Focusing on the tweets that carry a geographic reference, a “picture” of what is happening in a specific place could be made mapping the posts over the area of interest This data source can be considered as a real-time crowdsource of crisis information, the spatial distribution of geolocated tweets related to an event can represent an early indicator of the severity of impact. This raises a question: would it be possible to understand the affected zones and approximate level of impact by mapping and processing spatially the posts?
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