Automatic Event Detection in Smart Cities Using Big Data Analytics

Big data technologies enable smart city systems in sensing the city at micro-levels, making intelligent decisions, and taking appropriate actions, all within stringent time bounds. Social media have revolutionized our societies and is gradually becoming a key pulse of smart societies by sensing the information about the people and their spatio-temporal experiences around the living spaces. In this paper, we use Twitter for the detection of spatio-temporal events in London. Specifically, we use big data and machine learning platforms including Spark, and Tableau, to study twitter data about London. Moreover, we use the Google Maps Geocoding API to locate the tweeters and make additional analysis. We find and locate congestion around London and empirically demonstrate that events can be detected automatically by analyzing data. We detect the occurrence of multiple events including the London Notting Hill Carnival 2017 event, both their locations and times, without any prior knowledge of the event. The results presented in the paper have been obtained by analyzing over three million tweets.