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Information Systems Frontiers

, Volume 20, Issue 5, pp 993–1011 | Cite as

CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing

  • Marco Avvenuti
  • Stefano Cresci
  • Fabio Del Vigna
  • Tiziano Fagni
  • Maurizio Tesconi
Article

Abstract

Natural disasters, as well as human-made disasters, can have a deep impact on wide geographic areas, and emergency responders can benefit from the early estimation of emergency consequences. This work presents CrisMap, a Big Data crisis mapping system capable of quickly collecting and analyzing social media data. CrisMap extracts potential crisis-related actionable information from tweets by adopting a classification technique based on word embeddings and by exploiting a combination of readily-available semantic annotators to geoparse tweets. The enriched tweets are then visualized in customizable, Web-based dashboards, also leveraging ad-hoc quantitative visualizations like choropleth maps. The maps produced by our system help to estimate the impact of the emergency in its early phases, to identify areas that have been severely struck, and to acquire a greater situational awareness. We extensively benchmark the performance of our system on two Italian natural disasters by validating our maps against authoritative data. Finally, we perform a qualitative case-study on a recent devastating earthquake occurred in Central Italy.

Keywords

Crisis mapping Word embeddings Geoparsing Online social networks Social media Big data 

Notes

Acknowledgments

This research is supported in part by the EU H2020 Program under the scheme INFRAIA-1-2014-2015: Research Infrastructures grant agreement #654024 SoBigData: Social Mining & Big Data Ecosystem, and by the MIUR (Ministero dell’Istruzione, dell’Universita‘ e della Ricerca) and Regione Toscana (Tuscany, Italy) funding the SmartNews: Social sensing for Breaking News project: PAR-FAS 2007-2013. Open image in new window

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information EngineeringUniversity of PisaPisaItaly
  2. 2.Institute of Informatics and Telematics (IIT)National Research Council (CNR)PisaItaly

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