Skip to main content
SpringerLink
Log in

Perspectives on global dynamic exposure modelling for geo-risk assessment

  • Review Article
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

The need for a global approach to natural hazard and risk assessment is becoming increasingly apparent to the disaster risk reduction community. Different natural (e.g. earthquakes, tsunamis, tornadoes) and anthropogenic (e.g. industrial accidents) hazards threaten millions of people every day all over the world. Yet, while hazards can be so different from each other, the exposed assets are mostly the same: populations, buildings, infrastructure and the environment. Exposure should be regarded as a dynamic process, as best exemplified by rapid urbanization, depopulation of rural areas and all of the changes associated with the actual evolution of the settlements themselves. The challenge is thus to find innovative, efficient methods to collect, organize, store and communicate exposure data on a global scale, while also accounting for its inherent spatio-temporal dynamics. The aim of this paper is to assess the challenge of implementing an exposure model at a global scale, suitable for different geo-hazards within a dynamic and scalable framework. In this context, emerging technologies, from remote sensing to crowd-sourcing, are assessed for their usability in exposure modelling and a road map is laid out towards a global exposure model that will continuously evolve over time by the continuous input and updating of data, including the consideration of uncertainties. Such an exposure model would lay the basis for global vulnerability and risk assessments by providing reliable, standardized information on the exposed assets across a range of different hazards.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source EM-DAT (Guha-Saphir et al. 2015)

Fig. 2

Similar content being viewed by others

Notes

  1. www.preventionweb.net/english/hyogo/gar/2015.

  2. www.ecapra.org.

  3. www.globalquakemodel.org.

  4. www.fema.gov/hazus.

  5. https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/overview.

  6. www.openstreetmap.org/.

  7. sedac.ciesin.columbia.edu/data/collection/grump-v1.

  8. ghslsys.jrc.ec.europa.eu/.

  9. ec.europa.eu/jrc/en/institutes/ipsc.

  10. www.dlr.de/dlr.

  11. www.isotc211.org/.

References

  • Abraham T, Roddick JF (1999) Survey of spatio-temporal databases. GeoInformatica 3:61–99. doi:10.1023/A:1009800916313

    Article  Google Scholar 

  • Alexander D (2006) Globalization of disaster: trends, problems and dilemmas. J Int Aff-Columbia Univ 59:1

    Google Scholar 

  • Allen TI, Wald DJ, Earle PS et al (2009) An Atlas of ShakeMaps and population exposure catalog for earthquake loss modeling. Bull Earthq Eng 7:701–718. doi:10.1007/s10518-009-9120-y

    Article  Google Scholar 

  • Aoki H, Matsuoka M, Yamazaki F (1999) Backscattering characteristics of airborne SAR images for seismic vulnerability assessment in urban areas. In: Proceedings of the 20th Asian Conference on Remote Sensing, vol 1, pp 115–120

  • Applied Technology Council (ATC) (1985) ATC-13: earthquake damage evaluation data for California. Washington

  • Applied Technology Council (ATC) BSSC, Agency USFEM (1997) NEHRP guidelines for the seismic rehabilitation of buildings. Federal Emergency Management Agency

  • Aubrecht C, Özceylan D, Steinnocher K, Freire S (2013) Multi-level geospatial modeling of human exposure patterns and vulnerability indicators. Nat Hazards 68(1):147–164

    Article  Google Scholar 

  • Balk D, Pozzi F, Yetman G, Deichmann U, Nelson A (2005) The distribution of people and the dimension of place: methodologies to improve the global estimation of urban extents. In: International society for photogrammetry and remote sensing, Proceedings of the urban remote sensing conference, pp 14–16

  • Bautista MLP, Bautista B, Narag IC et al (2014) Enhancing risk analysis capacities for flood, tropical cyclone severe wind and earthquake for the greater metro manila area component 2—exposure information development. Philippine Institute of Volcanology and Seismology, Geoscience Australia

  • Bevington J, Eguchi RT, Huyck CK et al (2012) Exposure data development for the global earthquake model. In: 15th world  conference on earthquake engineering, Lisboa, 24–28 Sep 2012

  • Bhaduri B, Bright E, Coleman P, Urban ML (2007) LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal 69:103–117. doi:10.1007/s10708-007-9105-9

    Article  Google Scholar 

  • Bilham R (2009) The seismic future of cities. Bull Earthq Eng 7(4):839–887

    Article  Google Scholar 

  • Bisch P, Carcalho E, Degee H et al (2012) Eurocode 8: seismic design of buildings worked examples. Joint Research Centre European Union, Luxembourg

    Google Scholar 

  • Blanco-Vogt A, Schanze J (2014) Assessment of the physical flood susceptibility of buildings on a large scale—conceptual and methodological frameworks. Nat Hazards Earth Syst Sci 14:2105–2117. doi:10.5194/nhess-14-2105-2014

    Article  Google Scholar 

  • Bossard M, Feranec J, Otahel J (2000) Corine land cover technical guide—Addendum 2000. Technical report, No 40. Copenhagen (EEA). http://www.eea.europa.eu/publications/tech40add/

  • Broglia M, Corbane C, Carrion D et al (2010) Validation protocol for emergency response geo-information products. JRC

  • Broughton V (2006) The need for a faceted classification as the basis of all methods of information retrieval. Aslib Proc 58:49–72. doi:10.1108/00012530610648671

    Article  Google Scholar 

  • Brzev S, Scawthorn C, Charleson AW, Jaiswal K (2012) GEM basic building taxonomy, version 1.0. GEM Ontology and Taxonomy Global Component project

  • Brzev S, Scawthorn C, Charleson AW et al (2013) GEM Building Taxonomy Version 2.0. GEM Technical Report 2013-02 V1.0.0, Global Earthquake Model

  • Cardona OD, Ordaz MG, Reinoso E et al (2012) CAPRA: Comprehensive approach to probabilistic risk assessment: international initiative for risk management effectiveness. In: Proceedings of the 15th World Conference on Earthquake Engineering, Lisbon

  • Chapman K (2012) Community mapping for exposure in Indonesia. Humanitarian OpenStreetMap Team, Washington

    Google Scholar 

  • Coburn AW, Spence (1992) Factors determining human casualty levels in earthquakes: mortality prediction in building collapse. In: Proceedings of the First International Forum on Earthquake-Related Casualties, Madrid, 1992

  • Coburn A, Spence RJS (2002) Earthquake protection. Wiley, Chichester

    Book  Google Scholar 

  • Coleman DJ, Georgiadou Y, Labonte J (2009) Volunteered Geographic Information: the nature and motivation of producers. Int J Spat Data Infrastruct Res 4:332–358

    Google Scholar 

  • De Bono A, Chatenoux B (2014) A global exposure model for GAR 2015. Input Paper prepared for the Global Assessment Report on Disaster Risk Reduction 2015. UNEP-GRID, Geneva

  • Dell’Acqua F, Gamba P, Jaiswal K (2012) Spatial aspects of building and population exposure data and their implications for global earthquake exposure modeling. Nat Hazards 68:1291–1309. doi:10.1007/s11069-012-0241-2

    Article  Google Scholar 

  • Dilley M (2005) Natural disaster hotspots a global risk analysis. World Bank, Washington

    Book  Google Scholar 

  • Doxsey-Whitfield E et al (2015) Taking advantage of the improved availability of census data: a first look at the gridded population of the world, version 4. Pap Appl Geogr 1:226–234

    Article  Google Scholar 

  • Dunford MA, Power L, Cook (2014) National exposure information system (NEXIS) building exposure—statistical area level 1 (SA1). Geoscience Australia, Canberra. doi:10.4225/25/5420C7F537B15

    Google Scholar 

  • Eguchi RT, Huyck CK, Ghosh S, Adams BJ (2008) The application of remote sensing technologies for disaster management. In: The 14th world conference on earthquake engineering, Beijing, China, 12–17 Oct 2008

  • Esch T, Thiel M, Schenk A et al (2010) Delineation of urban footprints from TerraSAR-X Data by analyzing speckle characteristics and intensity information. IEEE Trans Geosci Remote Sens 48:905–916. doi:10.1109/TGRS.2009.2037144

    Article  Google Scholar 

  • FEMA (2003) Multi-hazard loss estimation methodology. Federal Emergency Management Agency, Washington

    Google Scholar 

  • FEMA 154 (2002) Rapid visual screening of buildings for potential seismic hazards: a handbook, 2nd edn. ATC, Washington

    Google Scholar 

  • Fritz S, McCallum I, Schill C et al (2009) Geo-Wiki.Org: the use of crowdsourcing to improve global land cover. Remote Sens 1:345–354. doi:10.3390/rs1030345

    Article  Google Scholar 

  • Gahegan M, Ehlers M (2000) A framework for the modelling of uncertainty between remote sensing and geographic information systems. ISPRS J Photogramm Remote Sens 55:176–188

    Article  Google Scholar 

  • GAR-2015 (2015) Global risk analysis platform (CapraViewer). http://risk.preventionweb.net/capraviewer. Accessed 22 June 22 2015

  • Geiß C, Taubenböck H (2013) Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap. Nat Hazards 68:7–48. doi:10.1007/s11069-012-0322

    Article  Google Scholar 

  • Geiß C, Taubenböck H, Tyagunov S, Tisch A, Post J, Lakes T (2014) Assessment of seismic vulnerability from space. Earthq Spectra. doi:10.1193/121812EQS350M

    Google Scholar 

  • Girres J-F, Touya G (2010) Quality Assessment of the French OpenStreetMap Dataset. Trans GIS 14:435–459. doi:10.1111/j.1467-9671.2010.01203.x

    Article  Google Scholar 

  • Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquis 5:199–220

    Article  Google Scholar 

  • Grünthal G, Musson RMW, Schwarz J, Stucchi M (1998) European Macroseismic Scale 1998 (EMS-98). European Seismological Commission

  • Guha-Saphir D (2015) The human cost of natural disasters. A global perspective. Centre for Research on the Epidemiology of Disasters CRED

  • Guha-Saphir D, Hoyois P, Below R (2015) Annual disaster statistical review 2014: the numbers and trends. Centre for Research on the Epidemiology of Disasters (CRED)

  • Haklay M (2010) How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environ Plan 37:682–703. doi:10.1068/b35097

    Article  Google Scholar 

  • Hallegatte S, Przyluski V (2010) The economics of natural disasters. Concepts and methods. Policy research working paper no. 5507. The World Bank Sustainable Development Network

  • Harrower M (2003) Representing uncertainty: does it help people make better decisions. UCGIS Workshop: Geospatial Visualization and Knowledge Discovery Workshop, National Conference Center, Landsdowne, VA

  • Hecht R, Kunze C, Hahmann S (2013) Measuring completeness of building footprints in OpenStreetMap over Space and Time. ISPRS Int J Geo-Inf 2:1066–1091. doi:10.3390/ijgi2041066

    Article  Google Scholar 

  • Hoffmann E, Chamie M (1999) International statistical classification: basic principles. Statistical Commission Thirtieth session New York, New York, 1–5 March 1999

  • International Organization for Standardization (2005) International classification for standards: ICS. International Organization for Standardization, Genève

    Google Scholar 

  • Jaiswal KS, Wald DJ, Porter K (2010) A global building inventory for earthquake loss assessment and risk management. Earthq Spectra 26(3):731–748

    Article  Google Scholar 

  • Kresse W, Fadaie K (2004) ISO standards for geographic information. Springer, Berlin

    Book  Google Scholar 

  • Liu W, Suzuki K, Yamazaki F (2015) Height estimation for high-rise buildings based on InSAR analysis. In: Urban remote sensing event (JURSE), 2015 Joint, pp 1–4

  • Lu X, Wetter E, Bharti N et al (2013) Approaching the limit of predictability in human mobility. Nat—Sci Rep. doi:10.1038/srep02923

    Google Scholar 

  • Ludwig I, Voss A, Krause-Traudes M (2011) A comparison of the street networks of Navteq and OSM in Germany. In: Geertman S, Reinhardt W, Toppen F (eds) Advancing geoinformation science for a changing world, vol 1. Springer, Berlin, pp 65–84

    Chapter  Google Scholar 

  • MacEachren AM, Robinson A, Hopper S et al (2005) Visualizing geospatial information uncertainty: what we know and what we need to know. Cartogr Geogr Inf Sci 32:139–160

    Article  Google Scholar 

  • Marconcini M, Marmanis D, Esch T (2014) A novel method for building height estimation using TanDEM-X data. In: IGARRS conference, Quebec. doi:10.1109/IGARSS.2014.6947569

  • Mayaux P, Eva H, Gallego J et al (2006) Validation of the global land cover 2000 map. IEEE Trans Geosci Remote Sens 44:1728–1739. doi:10.1109/TGRS.2006.864370

    Article  Google Scholar 

  • Mondal P, Tatem AJ (2012) Uncertainties in measuring populations potentially impacted by sea level rise and coastal flooding. PLoS One 7:e48191. doi:10.1371/journal.pone.0048191

    Article  Google Scholar 

  • Müller A, Reiter J, Weiland U (2011) Assessment of urban vulnerability towards floods using an indicator-based approach—a case study for Santiago de Chile. Nat Hazards Earth Syst Sci 11:2107–2123. doi:10.5194/nhess-11-2107-2011

    Article  Google Scholar 

  • NRC (2010) National building code of Canada. Government of Canada, Ottawa

    Google Scholar 

  • Pagani M, Monelli D, Weatherill G, Danciu L, Crowley H, Silva V, Henshaw P, Butler L, Nastasi M, Panzeri L, Simionato M, Vigano D (2014) OpenQuake engine: an open hazard (and risk) software for the global earthquake model. Seismol Res Lett 85(3):692–702

    Article  Google Scholar 

  • Paredaens J, Van den Bussche J, Van Gucht D (1994) Towards a theory of spatial database queries (extended abstract). In: Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems. ACM, New York, pp 279–288

  • Peduzzi P, Dao H, Herold C, Mouton F (2009) Assessing global exposure and vulnerability towards natural hazards: the Disaster Risk Index. Nat Hazards Earth Syst Sci 9:1149–1159

    Article  Google Scholar 

  • Pelekis N, Theodoulidis B, Kopanakis I, Theodoridis Y (2004) Literature review of spatio-temporal database models. Knowl Eng Rev 19:235–274. doi:10.1017/S026988890400013X

    Article  Google Scholar 

  • Pesaresi M, Huadong G, Blaes X, Ehrlich D, Ferri S, Gueguen L, Halkia M, Kauffmann M, Kemper T, Lu L, Marin-Herrera MA, Ouzounis GK, Scavazzon M, Soille P, Syrris V, Zanchetta L (2013) A global human settlement layer from optical HR/VHR RS data: concept and first results. IEEE J Sel Top Appl Earth Obs Remote Sens 6(5):2102–2131

    Article  Google Scholar 

  • Peuquet DJ (2001) Making space for time: issues in space-time data representation. GeoInformatica 5:11–32. doi:10.1023/A:1011455820644

    Article  Google Scholar 

  • Pittore M, Wieland M (2013) Toward a rapid probabilistic seismic vulnerability assessment using satellite and ground-based remote sensing. Nat Hazards. doi:10.1007/s11069-012-0475-z

    Google Scholar 

  • Porter K (2005) Taxonomy of nonstructural building components. Pacific Earthquake Engineering Research (PEER) Center, Berkeley

    Google Scholar 

  • Potere D, Schneider A, Angel S, Civco D (2009) Mapping urban areas on a global scale: which of the eight maps now available is more accurate? Int J Remote Sens 30:6531–6558. doi:10.1080/01431160903121134

    Article  Google Scholar 

  • Sarabandi P, Kiremidjian A, Eguchi R, Adams B (2008) Building inventory compilation for disaster management: application of remote sensing and statistical modeling. Technical Report MCEER-08-0025

  • Sellis TK (1999) Research Issues in spatio-temporal database systems. In: Proceedings of the 6th international symposium on advances in spatial databases. Springer, London, pp 5–11

  • Shi W (2008) Towards uncertainty-based geographic information science–theories of modelling uncertainties in spatial analyses. Adv Spatio-Temporal Anal 5:29

    Google Scholar 

  • Snodgrass RT (1992) Temporal databases. In: Frank AU, Campari I, Formentini U (eds) Theories and methods of spatio-temporal reasoning in geographic space. Springer, Berlin Heidelberg, pp 22–64

    Chapter  Google Scholar 

  • Sorichetta A, Hornby GM, Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020. Scientific Data 2:150045

    Article  Google Scholar 

  • Strahler AH, Boschetti L, Foody GM et al (2006) Global land cover validation: recommendations for evaluation and accuracy assessment of global land cover maps. European Communities, Luxembourg 51

    Google Scholar 

  • Taubenböck H, Roth A, Dech S (2007) Linking structural urban characteristics derived from high resolution satellite data to population distribution. In: Coors V, Rumor M, Fendel E, Zlatanova S (eds) Urban and regional data management. Taylor & Francis Group, London, pp 35–45

    Google Scholar 

  • Taubenböck H, Esch T, Felbier A et al (2012) Monitoring urbanization in mega cities from space. Remote Sens Environ 117:162–176. doi:10.1016/j.rse.2011.09.01

  • Tobler W, Deichmann U, Gottsegen J, Maloy K (1997) World population in a grid of spherical quadrilaterals. Int J Popul Geogr 3:203–225

    Article  Google Scholar 

  • UNEP (2000) PREVIEW global risk data platform. http://preview.grid.unep.ch/. Accessed 26 Nov 2013

  • UNISDR (2009) UNISDR terminology on disaster risk reduction, United Nations International Strategy for Disaster Reduction, UNISDR-20-2009-Geneva, p 35

  • UNISDR GAR-13 (2013) GAR global risk assessment: data, sources and methods. UNISDR, Geneva

    Google Scholar 

  • United Nations, Department of Economic and Social Affairs, Population Division (2014). World urbanization prospects: the 2014 revision, CD-ROM edition. http://esa.un.org/unpd/wup/CD-ROM/. Accessed 15 June 2015

  • United Nations, Statistical Division (2008) Principles and recommendations for population and housing censuses. Department of Economic and Social Affairs, Statistics Division. Statistical Papers ST/ESA/STAT/SER.M/67/Rev.2 United Nations Publications, New York

  • Wald DJ, Earle PS, Allen TI et al (2008) Development of the US Geological Survey’s PAGER system (Prompt Assessment of Global Earthquakes for Response). 14th World Conference on Earthquake Engineering

  • Wesolowski A, Buckee CO, Pindolia DK et al (2013) The use of census migration data to approximate human movement patterns across temporal scales. PLoS One 8:e52971. doi:10.1371/journal.pone.0052971

    Article  Google Scholar 

  • Wieland M, Pittore M (2014) Performance evaluation of machine learning algorithms for urban pattern recognition from multi-spectral satellite images. Remote Sens 6(4):2912–2939. doi:10.3390/rs6042912

    Article  Google Scholar 

  • Wieland M, Pittore M, Parolai S et al (2012a) Estimating building inventory for rapid seismic vulnerability assessment: towards an integrated approach based on multi-source imaging. Soil Dyn Earthq Eng 36:70–83

    Article  Google Scholar 

  • Wieland M, Pittore M, Parolai S, Zschau J (2012b). Remote sensing and omnidirectional imaging for efficient building inventory data capturing: application within the Earthquake Model Central Asia. In: Proceedings of the IEEE IGARSS 2012, Munich, pp 3010–3013

  • Wieland M, Pittore M, Parolai S, Begaliev U, Yasunov P, Tyagunov S, Moldobekov B, Saidiy S, Ilyasov I, Abakanov T (2015) A multiscale exposure model for seismic risk assessment in Central Asia. Seismol Res Lett 86(1):210–222

    Article  Google Scholar 

  • Womble JA, Ghosh S, Adams BJ, Friedland CJ (2006) Advanced damage detection for Hurricane Katrina: integrating remote sensing with VIEWS field reconnaissance. MCEER-06-SPO2, Buffalo

  • World Bank (2012) Urban risk assessments: an approach for understanding disaster and climate risk in cities. ISBN: 978-0-8213-8962-1, doi:10.1596/978-0-8213-8962-1

  • World Bank (2013) World development report 2014: Risk and opportunity—managing risk for development. The World Bank, 2013. http://siteresources.worldbank.org/EXTNWDR2013/Resources/8258024-1352909193861/8936935-1356011448215/8986901-1380046989056/WDR-2014_Complete_Report.pdf. Accessed 28 June 2015

  • World Bank (2014) Open data for resilience initiative: field guide. Global Facility for Disaster Risk Reduction (GFDRR), World Bank, 2014. https://www.gfdrr.org/sites/gfdrr/files/publication/opendri_fg_web_20140629b_0.pdf. Accessed 27 June 2015

  • Wyss M, Tolis S, Rosset P, Pacchiani F (2013) Approximate model for worldwide building stock in three size categories of settlements. Background Paper prepared for the Global Assessment Report on Disaster Risk Reduction 2013. Geneva, Switzerland

  • Zielstra D, Zipf A (2010) Quantitative studies on the data quality of OpenStreetMap in Germany. In: Proceedings of the sixth international conference on geographic information science, GIScience, Zurich, pp 20–26

Download references

Acknowledgments

The research reported upon in this work was supported by funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No 312972 “Framework to integrate Space-based and in situ sENSing for dynamic vUlnerability and recover Monitoring”, the Global Earthquake Model (GEM), and the Earthquake Model Central Asia (EMCA) projects. The present paper is based on a contribution to the Global Assessment Report 2015. The authors would like to thank Anne Himmelfarb and Alanna Simpson (World Bank) for their insightful suggestions. The paper greatly benefited from the constructive comments of the two anonymous reviewers.

Author information

Authors and Affiliations

  1. Centre for Early Warning Systems, GFZ German Research Centre for Geosciences, Helmholtzstraße 7, 14467, Potsdam, Germany

    Massimiliano Pittore, Marc Wieland & Kevin Fleming

Authors
  1. Massimiliano Pittore
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marc Wieland
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kevin Fleming
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Massimiliano Pittore.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pittore, M., Wieland, M. & Fleming, K. Perspectives on global dynamic exposure modelling for geo-risk assessment. Nat Hazards 86 (Suppl 1), 7–30 (2017). https://doi.org/10.1007/s11069-016-2437-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-016-2437-3

Keywords

Search

Navigation