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

Author

Listed:
  • Marco Avvenuti

    (University of Pisa)

  • Stefano Cresci

    (National Research Council (CNR))

  • Fabio Del Vigna

    (National Research Council (CNR))

  • Tiziano Fagni

    (National Research Council (CNR))

  • Maurizio Tesconi

    (National Research Council (CNR))

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.

Suggested Citation

  • Marco Avvenuti & Stefano Cresci & Fabio Del Vigna & Tiziano Fagni & Maurizio Tesconi, 2018. "CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing," Information Systems Frontiers, Springer, vol. 20(5), pages 993-1011, October.
    • Handle: RePEc:spr:infosf:v:20:y:2018:i:5:d:10.1007_s10796-018-9833-z
    DOI: 10.1007/s10796-018-9833-z
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    Citations

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    Cited by:

    1. Rafael Prieto Curiel & Stefano Cresci & Cristina Ioana Muntean & Steven Richard Bishop, 2020. "Crime and its fear in social media," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-12, December.
    2. Stefan Stieglitz & Christian Meske & Björn Ross & Milad Mirbabaie, 2020. "Going Back in Time to Predict the Future - The Complex Role of the Data Collection Period in Social Media Analytics," Information Systems Frontiers, Springer, vol. 22(2), pages 395-409, April.
    3. Jennifer Fromm & Kaan Eyilmez & Melina Baßfeld & Tim A. Majchrzak & Stefan Stieglitz, 2023. "Social Media Data in an Augmented Reality System for Situation Awareness Support in Emergency Control Rooms," Information Systems Frontiers, Springer, vol. 25(1), pages 303-326, February.
    4. Pavel Petrov, 2023. "Practical approach for modifying existing geocoding system from equal angular to equal area," Economics and computer science, Publishing house "Knowledge and business" Varna, issue 2, pages 43-65.
    5. Sajjad Ahadzadeh & Mohammad Reza Malek, 2021. "Earthquake Damage Assessment Based on User Generated Data in Social Networks," Sustainability, MDPI, vol. 13(9), pages 1-19, April.
    6. Saptarshi Ghosh & Kripabandhu Ghosh & Debasis Ganguly & Tanmoy Chakraborty & Gareth J. F. Jones & Marie-Francine Moens & Muhammad Imran, 2018. "Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends," Information Systems Frontiers, Springer, vol. 20(5), pages 901-907, October.

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