Abstract
“Social sensing” is a form of crowd-sourcing that involves systematic analysis of digital communications to detect real-world events. Here we consider the use of social sensing for observing natural hazards. In particular, we present a case study that uses data from a popular social media platform (Twitter) to detect and locate flood events in the UK. In order to improve data quality we apply a number of filters (timezone, simple text filters and a naive Bayes ‘relevance’ filter) to the data. We then use place names in the user profile and message text to infer the location of the tweets. These two steps remove most of the irrelevant tweets and yield orders of magnitude more located tweets than we have by relying on geo-tagged data. We demonstrate that high resolution social sensing of floods is feasible and we can produce high-quality historical and real-time maps of floods using Twitter.
Highlights
Natural hazards such as floods, wildfires, storms and other extreme weather events cause substantial disruption to human activity and are predicted to increase in frequency and severity as the climate changes [1, 2]
Since we suppose that the algorithm developed here will be applied to detect floods in a specific area we utilise the user timezone associated with each tweet as a simple filter prior to location inference, rather than as part of a generalised location inference procedure
Since we are trying to locate floods, when tuning parameters and other design options, we focus on the overall accuracy of flood event detection, rather than the accuracy of location inference on a single tweet, as the metric for improvement
Summary
Natural hazards such as floods, wildfires, storms and other extreme weather events cause substantial disruption to human activity and are predicted to increase in frequency and severity as the climate changes [1, 2]. Rapid high-resolution observations of unfolding hazard events can inform decision-making and management responses. Observations from meteorological and climatological instrumentation typically suffer from data sparsity, time delays and high costs [2]. The human impacts associated with natural hazards are hard to measure using sensor platforms designed to observe the hazards themselves. Crowd-sourcing has been proposed as a possible solution to the need for better observations of environmental phenomena and associated impacts, utilising a variety of methods including citizen observations, web technologies, distributed sensor networks, and smart devices [2]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
