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Automatic Event Detection in Smart Cities Using Big Data Analytics

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 224)


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.


  • Smart cities
  • Big data
  • High performance computing
  • Social media analysis
  • Machine learning

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  • DOI: 10.1007/978-3-319-94180-6_13
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The authors acknowledge with thanks the technical and financial support from the Deanship of Scientific Research (DSR) at the King Abdulaziz University (KAU), Jeddah, Saudi Arabia, under the grant number G-661-611-38. The experiments reported in this paper were performed on the Aziz supercomputer at King AbdulAziz University.

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Correspondence to Sugimiyanto Suma .

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Suma, S., Mehmood, R., Albeshri, A. (2018). Automatic Event Detection in Smart Cities Using Big Data Analytics. In: Mehmood, R., Bhaduri, B., Katib, I., Chlamtac, I. (eds) Smart Societies, Infrastructure, Technologies and Applications. SCITA 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 224. Springer, Cham.

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