International Journal of Biometeorology

, Volume 62, Issue 5, pp 733–740 | Cite as

Digital divide, biometeorological data infrastructures and human vulnerability definition

  • Pablo Fdez-Arroyabe
  • Luis Lecha Estela
  • Falko Schimt
Special Issue: Latin America/Caribbean


The design and implementation of any climate-related health service, nowadays, imply avoiding the digital divide as it means having access and being able to use complex technological devices, massive meteorological data, user’s geographic location and biophysical information. This article presents the co-creation, in detail, of a biometeorological data infrastructure, which is a complex platform formed by multiple components: a mainframe, a biometeorological model called Pronbiomet, a relational database management system, data procedures, communication protocols, different software packages, users, datasets and a mobile application. The system produces four daily world maps of the partial density of the atmospheric oxygen and collects user feedback on their health condition. The infrastructure is shown to be a useful tool to delineate individual vulnerability to meteorological changes as one key factor in the definition of any biometeorological risk. This technological approach to study weather-related health impacts is the initial seed for the definition of biometeorological profiles of persons, and for the future development of customized climate services for users in the near future.


Digital divide Co-creation Vulnerability App Risk 


  1. Castells M (1996) The information age: economy, society and culture. Vol. I. In: The rise of the network society. Blackwell, OxfordGoogle Scholar
  2. ESA (2016) Copernicus Initiative. Accessed on 10 Nov 2016
  3. Fdez-Arroyabe P (2013) Climate services and human health: a niche of opportunities for economic growth. Scientific annals of Alexandru Ioan Cuza. Geography Series 59(2):135–152 (on line versión) 2284-6379 eISNNGoogle Scholar
  4. Fdez-Arroyabe P (2015) Climate change, local weather and customized early warning systems based on biometeorological indexes. J Earth Sci Eng 5(2015):173–181 David publishing. doi: 10.17265/2159-581X/2015.03.002 Google Scholar
  5. Fdez-Arroyabe P, Lecha L (2008) Assessment of two biometeorological early warning systems based on contrast in Northern Spain. Proceedings of the Int. Conference of the Spanish Society of Climatology, pp. 781–92Google Scholar
  6. Freeman C, Louçã F (2002) As time goes by: from the industrial revolutions to the information revolution. Oxford University Press, USA 2002CrossRefGoogle Scholar
  7. Hand E (2010) Citizen science: people power. Nature 466(7307):685–687CrossRefGoogle Scholar
  8. Hewitt C, Mason S, Walland D (2012) The global framework for climate services. Nat Clim Chang 2:831–832CrossRefGoogle Scholar
  9. Irwin A (1997) Citizen science: a study of people, expertise and sustainable development. Sci Technol Hum Values 22(4):525–527CrossRefGoogle Scholar
  10. Janssen J, Stoyanov S, Ferrari A, Punie Y, Pannekeet K, Sloep P (2013) Experts’ views on digital competence: commonalities and differences. Comput Educ 68:473–481CrossRefGoogle Scholar
  11. Kalkstein LS, Jamason PF, Greene JS (1996) The Philadelphia hot weather-health watch/warning system: development and application. Bull Am Meteorol Soc 77(7):56–64CrossRefGoogle Scholar
  12. Kirch W, Menne B, Bertollini R (2005) Extreme weather events and public health responses. Ed. Springer-Verlag, ISBN 3–540–24417-4, 303 ppGoogle Scholar
  13. Kozlovszky M, Batbayar K, Garaguly Z, Karózkai K (2016) Multimodal biophysical data visualization for patient monitoring, 2016 I.E. 11th Int. Symposium on Applied Computational Intelligence and Informatics, (SACI), Timisoara, 2016, pp. 401–406. doi:  10.1109/SACI.2016.7507411
  14. Lecha L, Delgado T (1996) On a regional health watch and warning system. In: Proceedings of the 14th Int. Congress of Biometeorology, Ljubljana, Slovenia, part 2, vol. 3, 94–107Google Scholar
  15. Lecha L, Ortiz PL, Moya A, Estrada A (2008) A new world wide web for the public diffusion of biometeorological and bioclimatic forecasts. In XVIII ISB Congress, Tokyo, Japan; Hum2-P09Google Scholar
  16. Negroponte N (1995) Being digital. Hodder & Stoughton Ltd.Google Scholar
  17. Nonaka I, Takeuchi H (1995) The knowledge creating company: how Japanese companies create the dynamics of innovation. Oxford University Press, New York 1995Google Scholar
  18. Ovcharova VF (1981) Calculation of oxygen content in the air on the basis of meteorological parameters (pressure, temperature and humidity) for forecasting the effects of hypoxic conditions [in Russian]. Prob. Climatoterapia, Fisioterapia y Rehabilitación 2:29–34Google Scholar
  19. Sagiroglu S, Sinanc D (2013) Big data: a review, Collaboration Technologies and Systems (CTS), 2013 International conference on, San Diego, CA, 2013, pp. 42–47Google Scholar
  20. Servon LJ (2002) Bridging the digital divide, technology, community and public policy. Blackwell Publishing, HobokenCrossRefGoogle Scholar
  21. Warschauer M (2004) Technology and social inclusion, rethinking the digital divide. The MIT Press, MassachusettsGoogle Scholar
  22. WMO (2014) Implementation plan of the global framework for climate services. Access 14 May 14 2016.

Copyright information

© ISB 2017

Authors and Affiliations

  1. 1.Geography Department, Geobiomet Research GroupUniversity of CantabriaSantanderSpain
  2. 2.Centro de Estudio Ambientales (CESAM)Santa ClaraCuba

Personalised recommendations