Abstract
Social networks are a valuable source of information to support the detection and monitoring of targeted events, such as rainfall episodes. Since the emergence of Web 2.0, several studies have explored the relationship between social network messages and authoritative data in the context of disaster management. However, these studies fail to address the problem of the temporal validity of social network data. This problem is important for establishing the correlation between social network activity and the different phases of rainfall events in real-time, which thus can be useful for detecting and monitoring extreme rainfall events. In light of this, this paper adopts a temporal approach for analyzing the cross-correlation between rainfall gauge data and rainfall-related Twitter messages by means of temporal units and their lag-time. This approach was evaluated by conducting a case study in the city of São Paulo, Brazil, using a dataset of rainfall data provided by the Brazilian National Disaster Monitoring and Early Warning Center. The results provided evidence that the rainfall gauge time-series and the rainfall-related tweets are not synchronized, but they are linked to a lag-time that ranges from −10 to +10 min. Furthermore, our temporal approach is thus able to pave the way for detecting patterns of rainfall in real-time based on social network messages.
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- 1.
We took the geometry of the city from the Global Administrative Areas (GADM).
- 2.
CEMADEN website is available at www.cemaden.gov.br.
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Acknowledgements
This research was partially funded by the Engineering and Physical Sciences Research Council (EPSRC) through the Global Challenges Research Fund. The authors would like to express their thanks for the financial support provided by the Coordination for higher Education Staff Development (CAPES, Grant No. 88887.091744/2014- 01). S. C. Andrade would like to thank the Araucária Foundation of Supports Scientific and Technological Development in the State of Paraná (FAPPR) and State Secretariat of Science, Technology and Higher Education of Paraná (SETI) for their financial support. C. Restrepo-Estrada is grateful for the financial support from CAPES-PROEX.
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de Andrade, S.C., Restrepo-Estrada, C., Delbem, A.C.B., Mendiondo, E.M., de Albuquerque, J.P. (2017). Mining Rainfall Spatio-Temporal Patterns in Twitter: A Temporal Approach. In: Bregt, A., Sarjakoski, T., van Lammeren, R., Rip, F. (eds) Societal Geo-innovation. AGILE 2017. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-56759-4_2
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DOI: https://doi.org/10.1007/978-3-319-56759-4_2
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