Web 2.0 geospatial visual analytics for improved urban flooding situational awareness and assessment

  • Authors:
  • Yong Liu;David Hill;Luigi Marini;Rob Kooper;Alejandro Rodriguez;Jim Myers

  • Affiliations:
  • University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL;University of Illinois at Urbana-Champaign, Urbana, IL

  • Venue:
  • Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
  • Year:
  • 2009

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Abstract

Situational awareness of urban flooding during storm events is important for disaster and emergency management. However, no general purpose tools yet exist for rendering rainfall accumulations in real-time at the resolution of hydrologic units used for modeling. This demonstration will exhibit a novel web 2.0 visual analytical approach for understanding and adaptively managing urban flooding issues. The approach generates a geospatial-temporal map of rainfall within urban hydrologic units (sewer-sheds) in real-time. The polygon-averaged rainfall data is generated using virtual sensors which provide customized real-time data products derived from National Weather Service weather radar data using NCSA's workflow tools. Time-series KML (Keyhole Markup Language) layers are generated, where each KML layer represents a particular slice of the geospatial color-coded sewershed map. Such time-aware KML can be replayed as a movie in the web-based Google Earth environment. This geospatial visual analytic approach can provide decision markers and communities a powerful resource for assessment of neighborhood flooding issues. We will demonstrate our technology using historical and real-time rainfall data in the metropolitan Chicago area to show the effectiveness of such approach. Future work by combining additional ground-truth flooding data will allow us move towards real-time improved decision support for flooding and stormwater management.