The spatio-temporal properties of Twitter users during the Sandy Hurricane
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
The wide-scale deployment of networked communication and sensing devices, e.g. phones and tablets, provides a previously unimaginable amount of information about people's environment and movements. These devices often have access to high accuracy localization technology, such as GPS and Wi-Fi/cell tower localization. Users of these devices also frequently participate in global social networks, for instance Twitter, Facebook and Google+. The information obtained from social media in a catastrophic event is unique and cannot be found anywhere else in the information space, they may even have the geographical knowledge of the influenced areas, which can be high importance for those outside of the area. This role is highlighted in the occurrence of hurricane sandy on 2012. Geo- tagged social media messages expose user’s locations and subsequent movements, providing near-instantaneous data about how people are responding to a disaster event. The need for up-to- date information is paramount for the authorities so they can organize the most efficacious response. They need to know what issues are affecting people on the ground, where people are located and whether they can/will evacuate. This project will analyze gigabytes of data collected during the Sandy Hurricane of 2012 on the American East Coast. Millions of geo-tagged tweets from hundreds of thousands of users were collected and offer a unique insight into how Twitter activity increased during the hurricane in the area of the event and the movement pattern of the people changed during the hurricane. These reactions and movements of people during the Hurricane Sandy help the process of evaluation so responders can have a more robust situational awareness of the disaster.
Place, publisher, year, edition, pages
2015. , 48 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-252092OAI: oai:DiVA.org:uu-252092DiVA: diva2:808852
Masters Programme in Embedded Systems
Rohner, ChristianYi, Wang