GIS Application for Economic Assessment of Direct Disaster Losses

  • Dimitar DimitrovEmail author
  • Georgi PenchevEmail author
  • Ekaterina BogomilovaEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 516)


The article is aimed at presenting the proof-of-concept system for automated direct disaster losses, developed within a scientific project of University of National and World Economy, Sofia. The idea behind the project was to create a system that can be used by customers without expert knowledge on physical or economic modeling of disaster effects. Thus alleviating the initial phases of disaster planning in administration, providing raw picture of disaster threats. The article describes in general conceptual and physical schemes of the system and gives main requirements for data and GIS applications that can be used. The process of developing such system shows that it is a complicated, but possible task. The system is not production ready and it is implemented with one physical model for floods. Nevertheless, the possibility to use and reuse the physical model by automation of main model estimation phases gives opportunity for creation and assessment of different alternatives for disaster prevention and relief.


GIS Disaster losses Disaster modeling 


  1. 1.
    OGC® Standards and Supporting Documents.
  2. 2.
    GRASS GIS – General overview. Accessed 18 Nov 2017
  3. 3.
    GDAL: GDAL - Geospatial Data Abstraction Library. Accessed 18 Nov 2017
  4. 4.
    Pavlenko, A.: artem at mapnik: Mapnik C++/Python GIS Toolkit. Accessed 01 Dec 2017
  5. 5.
    mod_tile - OpenStreetMap Wiki. Accessed 29 Nov 2017
  6. 6.
    R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2017)Google Scholar
  7. 7.
    Chang, W., Cheng, J., Allaire, J.J., Xie, Y., McPherson, J.: Shiny: Web Application Framework for R (2017)Google Scholar
  8. 8.
    Shiny Server. Accessed 18 Nov 2017
  9. 9.
    Nominatim - OpenStreetMap Wiki. Accessed 18 Nov 2017
  10. 10.
    Technical Overview. Accessed 29 Nov 2017
  11. 11.
    Geographical information system of the Commission (GISCO) - Statistics Explained. Accessed 02 Dec 2017
  12. 12., Science for a changing world. Accessed 02 Dec 2017
  13. 13.
    ESDAC - European Commission. Accessed 01 Dec 2017
  14. 14.
    COPERNICUS, Emergency Management Service. Accessed 02 Dec 2017
  15. 15.
    Ferrando, I., Federici, B., Sguerso, D., Marzocchi, R.: The r. inund. fluv tool for flood-prone areas evaluation in GRASS GIS: application to the terminal reach of Magra River. In: Geomatics Workbooks 12 FOSS4G Europe Como (2015)Google Scholar
  16. 16.
    Marzocchi, R., Federici, B., Cannata, M., Cosso, T., Syriou, A.: The contribution of GIS in flood mapping: two approaches using open source grass GIS software, pp. 175–178. IISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2013)Google Scholar
  17. 17.
    Cheng, J., Karambelkar, B., Xie, Y.: leaflet: Create Interactive Web Maps with the JavaScript “Leaflet” Library (2017)Google Scholar
  18. 18.
    Metcalfe, P., Beven, K., Freer, J.: dynatopmodel: Implementation of the Dynamic TOPMODEL Hydrological Model (2018)Google Scholar
  19. 19.
    Bivand, R.: spgrass6: Interface Between GRASS 6 and R (2016)Google Scholar
  20. 20.
    Hazus, Accessed 12 Mar 2017
  21. 21.
    Goteti, G.: hazus: Damage Functions from FEMA’s HAZUS Software for Use in Modeling Financial Losses from Natural Disasters (2014)Google Scholar
  22. 22.
    CORINE Land Cover. Accessed 12 Mar 2017
  23. 23.
    Bivand, R., Rundel, C.: rgeos: Interface to Geometry Engine - Open Source (‘GEOS’) (2017)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.University of National and World EconomySofiaBulgaria

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