INSPIRE Knowledge Base


Generalization of HAzard/RIsk predictive Mapping Standards for disaster management
Mission and Objectives: 
The following three issues have been identified as the focus of the proposed activity. Issue 1: Risks are increasing and intermixed. They need to be dealt with in their spatial dimension with multi-risk approaches. Issue 2: There is no standard risk assessment and risk mapping technique today. Issue 3: There is no practice today to validate the results of risk predictions and the risk maps. This is a crucial operational gap in land-use planning and disaster preparedness. GHARIMS aims to address point 12 of the INSPIRE Initiative (Annex III): natural risk zones. This will be done by: (i) proposing data requirements for different types of risk; (ii) providing advice on risk assessment techniques and on how to integrate them; (iii) guiding on risk mapping aspects and map validation; (iv) establishing links with decision support procedures and with the users of risk maps; (v) strengthening linkages between risk management, land-use planning, and emergency management, to satisfy the risk management cycle (identification, analysis, mitigation and follow-up) in a holistic operational approach. During the past 10 years tremendous efforts have been made by the disaster science community to develop and apply new techniques for hazard prediction and risk mapping mainly but not exclusively in areas affected by mass movements. Several EC Projects have led to firm know-how in spatial data analysis and GIS (see the Mandate box below). Various academic institutions, industrial partners and local administrations have participated in the experimentation of GIS and Decision Support Systems, DSS, for the solution of environmental issues in areas subjected to hazardous processes in different parts of Europe (Italy, Spain, Portugal, France, Austria, Poland, Belgium and Germany). Times are now ripe to establish mapping standards to communicate the different levels of predicted natural and technological hazard in general, to introduce vulnerabilities and socioeconomic values in the risk assessment equation, and to represent risk in ways appropriate to decision-makers. A core issue in risk mapping is the one of providing measures of significance of prediction results when the predictions are generated from spatial databases. The spatial databases usually contain map information on rock units, land-cover classes, topographic elevations and derived attributes (slope, aspect, etc.), socioeconomic data layers (population density, infrastructures, assets, etc.), and the distribution in space and in time of clearly identified hazardous events. In prediction modeling multi-layered databases are transformed into an aggregation of functional values to obtain an index of propensity of the land to host hazardous processes. If the information in the database is sufficiently representative of the typical conditions in which the hazardous events originated in space and in time, the problem then, is to confirm the validity of the results of some models over other ones, or of particular experiments that use more significant data. After a first hazard prediction is obtained using all data available, the validation of hazard prediction results requires the partitioning of the distribution of hazardous occurrences in time intervals, or spatial subsets of parts of the study area or of random half occurrence subsets. One subset of occurrences is used to obtain a second hazard prediction using the same model used in a first prediction, and the other subset of occurrences is used to validate the prediction result (the prediction map) by counting the number and measuring the area of the validation occurrences falling within each prediction class, ranked in descending order. This generates a prediction-rate curve, which represent the goodness of the prediction.
Formal Mandate: 
The following EC projects have led to the present INSPIRE proposal. (1) Geomorphology and Environmental Impact Assessment, HC&M, ERBCHRXCT 930311, 93-96; (2) NEWTECH, New Technologies for Landslide Hazard Assessment and Management in Europe, Environment, ENV-CT96-0248, 1996-1998; (3) GETS, TMR, FMRX-CT98-0162, 99-02; (4) ELANEM, Quantitative indicators and indices of environmental quality; a Euro-Latin American Network for Environmental Assessment and Monitoring, INCO, ERBIBC18CT98-0290; and (5) ALARM, Assessment of LAndslide Risk and Mitigation in mountain areas, EVG1-2001-00018, 01-04). All these activities were conceived under the patronage of CERG, the European Centre on Geomorphologic Hazards (, within the framework of the Council of Europe’s partial Agreement on the prevention, protection against major natural and technical disasters and organization of relief ( An important link will be maintained with active EC projects such as ORCHESTRA, Developing a Unified Open Architecture for Risk Management Applications(, and ARMONIA, Applied multi Risk Mapping of Natural Hazards for Impact Assessment (; ), and PSPE, Participatory Spatial Planning in Europe, INTERREG III C ( ) Applications have demonstrated to portray the desirable relations between hazard prediction and disaster management. A software approach in the ALARM Network provides a fundamental method for decision-making ( This approach can now be extended to other areas of natural and technological risks and in particular to multi-risk situations to obtain standardized risk maps. The needs of spatial data for emergency management are bound to be very different from the needs of spatial data for risk management, especially if the risk assessment is not available! Risk maps are to be the starting points for emergency management. The spatial information laboratory, SPINlab ( ) carries out research and education on spatial and geo-information at the Vrije Universiteit Amsterdam. At the lab issues are being addressed such as land-use management, climate change, health-care and mobile field work from the perspective of spatial information. Themes are combined such as transport and natural risk management with innovative technologies such as wireless location services, web mapping, 3D visualization and ubiquitous computing. The aim of the lab is to stimulate innovation in education through distance learning, digital learning, wireless and mobile learning. The SPINlab is an interfaculty centre of the Faculty of Earth and Life Sciences and the Faculty of Economics and Business Administration.
Workshops in connection with various European research projects and Working Groups meeting within ISPRS (Working Group VI/8, on Spatial Data Integration for Emergency Services:
Geological Survey of Canada; GEODAN Holding B.V., Amsterdam, Netherlands; DISAT, University of Milano-Bicocca, Milan, Italy; SPINlab, Vrije Universiteit, Amsterdam, Netherlands; DICITYMAC, Universidad de Cantabria, Santander, Spain.
References: Zlatanova S., Fabbri A.G., and Li J., 2005, Geo-information for disaster management; large scale 3D needed by urban areas. GIM International, March 2005, v. 19, issue 3, p. 10-13. Bonachea J., Bruschi V.M., Remondo J., Alberto Gonzalez-Diez A., Salas L., Bertens J., Cendrero A., Otero C., Giusti C., Fabbri A., Gonzalez-Lastra J.R., and Aramburu J.R., 2005, An approach for quantifying geomorphological impacts for EIA of transportation infrastructures: a case study in northern Spain. Geomorphology, v. 66, p. 95-117. Chung, C. F., and Fabbri, A. G., 2005, Spatial prediction models and their validations with then occurrences of future landslides for risk analysis. Procs. International Conference on Landslide and Risk Management, Vancouver, B.C., Canada, May 31-June 4, 2005, in press. Chung, C.F., Fabbri, A. G., Jang, D. H., and Scholten, H. J., Risk assessment using spatial prediction model for natural disaster preparedness. In, van Oosterom P., Zlatanova S., and Fendel E.M., 2005, Geo-information for Disaster Management. Berlin, Springer, p. 619-640. Procs. of Gi4DM, The First Symposium on Geo-information for Disaster Management, Delft, Netherlands, March 21-23, 2005. Chung C.F. and Fabbri A.G., 2004, Systematic procedures of landslide hazard mapping for risk assessment using spatial prediction models. In, Glade T., Anderson M.G., and Crozier M.J., eds., Landslide Hazard and Risk. New York, John Wiley & Sons, p. 139-174. Fabbri, A.G., Chung, C.F., and Jang D.H., 2004, A software approach to spatial predictions of natural hazards and consequent risks. In, Brebbia C.A., ed., Risk Analysis IV. Southampton, Boston, WIT Press, p. 289-305. Chung, C.F. and Fabbri, A.G., 2003, On some weak points of quantitative landslide hazard zonation. Proc. IAMG 2003, Portsmouth, U.K., September 7-12, 2003. Geneletti, D., Beinat, E., Chung, C.F., Fabbri, A.G., and Scholten, H.J., 2003, Accounting for uncertainty factors in biodiversity impact assessment: lessons from a case study. Environmental Impact Assessment Review, v. 23, p. 471-487. Chung, C.F. and Fabbri, A.G., 2003, Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, v. 30, p. 451-472. Remondo, J., Gonzalez, A., Diaz de Teran, J.R., Cendrero, A, Fabbri, A.G., and Chung, C.F., 2003, Validation of landslide susceptibility maps: examples and applications from a case study in Northern Spain. Natural Hazards,v. 30, p. 437-449. Fabbri, A.G., Chung, C.F. and Cendrero A., and Remondo, J., 2003, Is prediction of future landslides possible with a GIS? Natural Hazards, v. 30, p. 487-499. Chung, C.F, Kojima,H., and Fabbri, A.G., 2002, Stability analysis of prediction models for landslide hazard mapping. In, R.J. Allison, ed., Applied Geomorphology: Theory and Practice. New York, John Wiley and Sons, Ltd., p. 3-19. Cendrero, A., Frances, E., Latrubesse, E.M., Prado, R., Fabbri A., Panizza, M., Cantu, M.P., Hurtado, M., Gimenez, J.E., Martinez, O., Cabral, M., Tecchi, R.A., Hamity, V., Ferman, J.L., Quintana, C., Ceccioni, A., Recatalá, L., Bayer, M. and Aquino, S., 2002, Projecto Relesa-Elanem: uma Nova Proposta Metodológica de Índices e Indicadores para Avaliação da Qualidade Ambiental. Revista Brasileira de Geomorfologia, Ano 3, No 1, p. 33-47. Chung, C.F, Fabbri, A.G., and Chi, K.H., 2002, A strategy for sustainable development of non-renewable resources using spatial prediction models. In, Fabbri, A.G., Gaal, G., and McCammon, R.B., eds., Deposit and Geoenvironmental Models for Resource Exploitation and Environmental Security. Dordrecht, Kluwer Academic Publishers, p. 101-118. Fabbri, A.G., Chung, C.F., Napolitano, P., Remondo, J. and Zezere, J.L., 2002, Prediction rate functions of landslide susceptibility applied in the Iberian Peninsula. In, Brebbia C.A., ed., Risk Analysis III. Southampton, Boston, WIT Press, p. 703-718.

Which role(s) do you foresee for the SDIC in INSPIRE development

  • submit reference material as input to the Drafting Teams
  • yes
  • allocate experts to Drafting Teams
  • yes
  • participate in the review process
  • yes
  • implement pilot projects to test/revise/develop the draft Implementing Rules
  • yes
  • contribute to awareness raising and training
  • yes
    Geographic Domain
    areas affected by hazardous natural processes
    Societal Sector
    Research and education in close collaboration with privat sector and local administrations
    Specific Expertise
    Specific Expertise: 
    Previous Experience relevant for INSPIRE development
    Construction of hazard prediction and risk assessment maps using spatial data analysis and statistical methods (see references in the comment box). Experience in several EC projects (see Mandate box and references in Comment box).
    Environmental application domains
    Spatial planning, landslide prevention and management, avalanches prevention and management
    Primary Business
    Environmental management, consulting/technical services, surveying and mapping using Geographic Information Systems and Spatial Decision Support Systems.
    INSPIRE Data Themes