, Volume 82, Issue 2, pp 293–310 | Cite as

Towards a spatial data infrastructure for technological disasters: an approach for the road transportation of hazardous materials

  • Janaina Bezzerra Silva
  • Mariana A. Giannotti
  • Ana Paula C. Larocca
  • José Alberto Quintanilha


Spatial data have been used for the environmental monitoring of the consequences of accidents that involve the transportation of hazardous chemical products. This spatial data infrastructure (SDI), which was created for the sharing and use of spatial data, is limited by the absence of policies to support its establishment. The main objective of this study was to explore the use of social network analysis (SNA) as a tool to identify spatial data sharing between organizations involved in the management of accidents related to road transport of hazardous materials (RTHM). In addition, to discuss the existing policies and institutional agreements, and to initiate a conceptual SDI framework for RTHM sector. In this context, the institutions that are involved with RTHM were identified and information concerning their interest in the use and sharing of spatial data via a SDI was collected through interviews and consolidated. The interviews were at 39 institutions with representative employees. The interview data were tabulated and entered into the UCINET software (2000 version) to calculate metrics of centrality. From the SNA, the flow of data among the participating institutions was identified through the visual representation of the spatial data sharing and use networks. Subsequently, the existing institutional agreements for spatial data sharing were analyzed and discussed. The compiled results enabled the proposal of a conceptual SDI framework to support the management of disasters involving RTHM, based on the application of SNA theory, and the development of a methodology that supports the analysis of interactions among the various actors of an SDI. The purpose is to facilitate the formulation of policies for the sharing of spatial data for decision-making and preventive disaster management. The results indicate that the 39 institutions share spatial data, but this sharing is not always predetermined by formal agreements. Furthermore, there is a strong demand, by the institutions involved in the management of RTHM accidents, regarding legal mechanisms governing the sharing of data for the purpose of producing maps that help to describe actions of preparedness, prevention, management and immediate relief involving RTHM incidents. Finally, it was possible to propose a conceptual framework with data that is considered essential for creating an SDI for RTHM.


SDI SNA GIS RTHM RTHM policy articulation Technological disasters 



Authors thanks to the Brazilian National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq) for student scholarship and PQ2 scholarship, to United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UNSPIDER), to all of the public and private institutions that participated in this study by answering the questionnaire, and to UNSPIDER in Bonn (Germany).

Compliance with ethical standards

This article does not contain any studies with human or animal subjects.


  1. Ajmar, A., Perez, F., & Terzo, O. (2008). WFP spatial data infrastructure (SDI) implementation in support of emergence management. In: XXI congress of the international society for photogrammetry and remote sensing.Google Scholar
  2. Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71.CrossRefGoogle Scholar
  3. Borgatti, S. P. & Everett, M. G. (2006). A graph-theoretic perspective on centrality, Social Networks, In Press, Corrected Proof, Available online 20 Jan 2006.Google Scholar
  4. Butler, D. (2006). Mashups mix data global service. Nature, 439, 6–7.Google Scholar
  5. BRAZIL, Estado de São Paulo [State of São Paulo]. Protocolo unificado de atendimento a emergências químicas no Estado de São Paulo [Unified treatment protocol for chemical emergencies in the State of São Paulo]. São Paulo. Accessed 10 Oct 2012.Google Scholar
  6. BRAZIL, Estado de São Paulo [State of São Paulo]. Resolução CC-3, de 9-1-2004. Diário Oficial do Estado de São Paulo. Casa Civil. Governo de São Paulo. Centro de Documentação e Arquivo—CDA [Resolution CC—3, of 1-9-2004. Official Gazette of the State of São Paulo. Civil House. Government of São Paulo. Documentation and Archive Center].Google Scholar
  7. Bruzewicz, A. J. (2003). Remote sensing imagery for emergency management in geographical dimension of terrorism. Transportation Research Board of The National Academies, Routledge, nº 2003-01-0126, 87–89.Google Scholar
  8. Bubbico, R., Di Cave, S., & Mazzarotta, B. (2004). Risk analysis for road and rail transport of hazardous materials: A GIS approach. Journal of Loss Prevention in the Process Industries, 17(6), 483–488.CrossRefGoogle Scholar
  9. Bubbico, R., Di Cave, S., & Mazzarotta, B. (2006). Risk management of road and rail transport of hazardous materials in Sicily. Journal of Loss Prevention in the Process Industries, Amsterdan, 19, 32–38.CrossRefGoogle Scholar
  10. Chemical Emergency report of State Agency – CETESB. (2010). Companhia Ambiental do Estado de São Paulo. Relatório de Emergências Químicas Atendidas pela CETESB em [State of São Paulo]. Disponível em
  11. Davis, J. R., Clodoveu, A., & Fonseca, F. (2011). National spatial data infrastructure: The case of Brazil. Washington, DC: infoDev/World Bank. Available at
  12. Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.CrossRefGoogle Scholar
  13. Goodchild, M. F. (2007). Citzens as voluntary sensors: Spatial data infrastructure in the world of web 2.0. International Journal of Spatial Data Infrastructures Research, 2, 24–32.Google Scholar
  14. Groeve, T., Stollberg, B., Vernaccini, L., & Doherty, B. (2010). Mash-up or spatial data infrastructure: Appropriate mapping tools for international situation rooms. In Proceedings of Gi4DM annual conference. Torino, Italy: ISPRS.Google Scholar
  15. Hubbell, C. H. (1965). An input-output approach to clique identification. Sociometry, 28, 377–399.Google Scholar
  16. Katz, L. (1953). A new index derived from sociometric data analysis. Psychometrika, 18, 39–43.Google Scholar
  17. Mansourian, A., Rajabifard, A., Valdan Zoej, M. J., & Williamson, I. (2006). Using SDI and web-based system to facilitate disaster management. Computer and Geosciences, 32, 303–315.CrossRefGoogle Scholar
  18. Milazzo, M. F., Lisi, R., Maschio, G., Antonioni, G., Bonvicini, S., & Spadoni, G. (2002). HazMat transport through Messina town: From risk analysis suggestions for improving territorial safety. Journal of Loss Prevention in the Process Industries, 15(5), 347–356.CrossRefGoogle Scholar
  19. Milazzo, M. F., Lisi, R., Maschio, G., Antonioni, G., & Spadoni, G. (2010). A study of land transport of dangerous substances in Eastern sicily. Journal of Loss Prevention in the Process Industries, 23(3), 393–403.CrossRefGoogle Scholar
  20. Molina, M., & Bayarri, S. A. (2011). Multinational SDI—based system to facilitate disaster risk management in the Andean Community. Computers and Geosciences, 37(9), 1501–1510.CrossRefGoogle Scholar
  21. Omran, E. E., & Van Etten, J. (2007). Spatial data sharing: Applying social network analysis to study individual and collective behavior. International Journal of Geographical Information Science, 21(6), 699–714.CrossRefGoogle Scholar
  22. Paudya, D. R., McDougall, K., & Apan, A. (2012). Spatially enabling government, industry and citizens. Abbas Rajabifard and David Coleman (Eds).Google Scholar
  23. Pinho. (2012). Análise das Redes de Localidades Ribeirinhas Amazônicas no Tecido Urbano Estendido: Uma contribuição Metodológica [Analysis of Amazonian Riparian Networks in the Extended Urban Fabric: A Methodological contribution]. Instituto Nacional de Pesquisas Espaciais—INPE [National Institute of Spatial Research].Google Scholar
  24. Scott, J. (2013). Social network analysis, A handbook (3rd ed.). London: Sage.Google Scholar
  25. Snoeren, G., Zlatanova, S., Crompvoets, J., & Scholten, H. (2007). Spatial data infrastructure for emergency management: the view of the users. Article presented in the third Symposium on Gi4DM, Toronto.Google Scholar
  26. Taylor, P. J., Catalano, G., & Walker, D. R. F. (2002). Exploratory analysis of the world city network. Urban Studies, 39(13), 2377–2394.Google Scholar
  27. Tena-Chollet, F., Tixier, J., Dusserre, G., & Mangin, J. F. (2013). Development of a spatial risk assessment tool for the transportation of hydrocarbons: Methodology and implementation in a geographical information system. Environmental Modelling and Software, 46, 61–74.CrossRefGoogle Scholar
  28. Van Oort, P., Hazeu, G., Kramer, H., Begt, A., & Rip, F. (2010). Social network in spatial data infrastructures. GeoJournal, 75(1), 105–118.CrossRefGoogle Scholar
  29. Vandenbroucke, D., Crompvoets, J., Vancauwenberghe, G., Dessers, E. & Orshoven, J., (2009). A network perspective on spatial data infrastructures: application to the sub-national SDI of Flanders (Belgium). Transactions in GIS 13(105–122)Google Scholar
  30. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). New York: Cambridge University Press.Google Scholar
  31. Zhang, J., Hodgson, J., & Erkut, E. (2000). Using GIS to assess the risks of hazardous materials transport in networks. European Journal of Operational Research, 121(2), 316–329.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Janaina Bezzerra Silva
    • 1
  • Mariana A. Giannotti
    • 1
  • Ana Paula C. Larocca
    • 1
    • 2
  • José Alberto Quintanilha
    • 1
  1. 1.Polytechnic SchoolUniversity of Sao PauloSao PauloBrazil
  2. 2.Sao Carlos Engineering SchoolUniversity of Sao PauloSao CarlosBrazil

Personalised recommendations