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“Hassle Mapping”: Using social media to monitor disruption from extreme weather, Computer Science – PhD (Funded)

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  • Full or part time
    Dr H Williams
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences, is inviting applications for a fully-funded PhD studentship to commence in September 2019 or as soon as possible thereafter. For eligible students the studentship will cover UK/EU/International tuition fees plus an annual tax-free stipend of at least £15,009 for 3.5 years full-time, or pro rata for part-time study. The student would be based in the Computer Science Department in the College of Engineering, Mathematics and Physical Sciences at the Streatham Campus in Exeter.

Extreme weather can cause disruption to the normal activities of individuals, businesses and public service organisations, including closures, transport delays, power outages, traffic accidents and many other negative impacts. Such disruption is often reported on social media, either by affected individuals or by organisations seeking to alert the public. This project will use “social sensing” to harvest social media content on disruption and use it to understand: (a) the baseline level of disruption in the UK on a typical day, and (b) how disruption is increased by windstorms and other extreme weather events.

Social sensing is the systematic analysis of large volumes of social media data, often in real time, to give information about real-world events. We have previously demonstrated the ability of social sensing to detect and locate wildfires (Boulton et al, 2016; https://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/viewPaper/13204), floods (Arthur et al, 2018; https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189327) and pollen (Cowie et al, 2018; https://www.mdpi.com/1424-8220/18/12/4434). The methodology involves machine learning, text mining, statistics and geospatial analysis. During this project the student will develop skills in these areas and a variety of other data science techniques.

Disruption from windstorms and other extreme weather has a huge effect on the UK economy and the well-being of citizens, but the total impact is hard to quantify. Outputs from this project will benefit groups such as the emergency services, utility companies, transport organisations and the civil authorities.

The studentship will be awarded on the basis of merit for 3.5 years of full-time study to commence in September 2019. The studentship is associated with the NERC BigFoot project but is funded by the University of Exeter.


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