Co-creation and Participatory Design of Big Data Infrastructures on the Field of Human Health Related Climate Services

Chapter
Part of the Studies in Big Data book series (SBD, volume 23)

Abstract

Co-creation of scientific knowledge based on new technologies and big data sources is one of the main challenges for the digital society in the XXI century. Data management and the analysis of patterns among datasets based on machine learning and artificial intelligence has become essential for many sectors nowadays. The development of real time health-related climate services represents an example where abundant structured and unstructured information and transdisciplinary research are needed. The study of the interactions between atmospheric processes and human health through a big data approach can reveal the hidden value of data. The Oxyalert technological platform is presented as an example of a digital biometeorological infrastructure able to forecast, at an individual level, oxygen changes impacts on human health.

Keywords

Co-creation Sustainability Interdisciplinarity Transdisciplinarity Morbidity Climate services Digital divide Big data Apps Oxyalert 

Notes

Acknowledgments

Funding support has been received from the Spanish Minister of Economy and Competiveness through the National Funding Budget applied to the national project CSO2013-46153-R. The Geobiomet Research Group would like to thank this institutional support in the field of Biometeorology.

References

  1. 1.
    Teich, M., Porter, R., Gustafsson, B.: Nature and Society in Historical Context. Cambridge University Press. ISBN 0521498813 (1997)Google Scholar
  2. 2.
    Barona, J.L., Cherry, S. (eds.): Health and Medicine in Rural Europe (1850–1945) Seminari d’Estudis sobre la Ciencia. Universitat de Valencia ISBN 84-370-6334-5Google Scholar
  3. 3.
    IPCC Fourth and Fifth Assessment Report: Climate Change 2007 Working Group II contribution to AR4 “Impacts, Adaptation and Vulnerability” and to AR5 “Impacts Adaptation and Vulnerability”Google Scholar
  4. 4.
    COP21 Climate Action, United Nation Framework Convention on Climate Change. http://www.cop21paris.org/ (2015). Accessed June 2016
  5. 5.
    Drucker, P.: The Age of the Social Transformation. Atlantic Mon, p. 274 (1966)Google Scholar
  6. 6.
    Servon, L.J.: Bridging the Digital Divide, Technology, Community and Public Policy. Blackwell Publishing (2002)Google Scholar
  7. 7.
    Warschauer, M.: Technology and Social Inclusion. Rethinking the digital divide. The MIT Press, Massachusetts (2004)Google Scholar
  8. 8.
    OECD (1996) The Knowledge-Based Society. ParisGoogle Scholar
  9. 9.
  10. 10.
    Gobal Framework for Climate Services http://www.wmo.int/gfcs/. Accessed 5 May 2016
  11. 11.
    Mauser, W., Klepper, G., Rice, M., Schmalzbauer, B.S., Hackmann, H., Leemans, R., Moore, H.: Transdisciplinary global change research: the co-creation of knowledge for sustainability. Curr. Opin. Environ. Sustain. 5(3–4), 420–431 (2013). ISSN 1877-3435CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Tress, B., Tress, G., Fry, G.: Potential and limitations of interdisciplinary and transdisciplinary landscape studies. In: Tress, B., Tress, G., Van der Valk, A., et al. (eds.): Interdisciplinarity and Transdisciplinarity in Landscape Studies: Potential and Limitations. Delta Program, Wageningen, pp. 182–192. Delta Series no. 2 (2003)Google Scholar
  14. 14.
    Tress, B., Tress, G., Fry, G.: Integrative studies on rural landscapes: policy expectations and research practice. Landscape Urban Plan. 70(1/2), 177–191 (2005)CrossRefGoogle Scholar
  15. 15.
    Nonaka, I., Toyama, R.: The theory of the knowledge-creating firm: subjectivity, objectivity and synthesis. Ind. Corp. Change 14(3), 419–436 (2005). doi:10.1093/icc/dth058 CrossRefGoogle Scholar
  16. 16.
    Nonaka, I.: A dynamic theory of knowledge creation. Organ. Sci. 5(1), 1437 (1994)CrossRefGoogle Scholar
  17. 17.
    Nonaka, I., Takeuchi, H.: The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, New York (1997). (1995)Google Scholar
  18. 18.
    Humphreys, P., Samson, A., Roser, T., Cruz-Valdivieso, E.: Co-creation: New Pathways to Value. An Overview, LSE Enterprise, Promise Corporation (2009)Google Scholar
  19. 19.
    Leach, M.: Co-design for relevance and usefulness. Future Earth Blog by Sayer L. http://www.futureearth.org/blog/2014-jul-23/co-design-relevance-and-usefulness-qa-melissa-leach (2014). Accessed 14 May 2016
  20. 20.
  21. 21.
    Fdez-Arroyabe, P.: Climate change, local weather and customized early warning systems based on biometeorological indexes. J. Earth Sci. Eng. 5(2015), 173–181 (2015). doi:10.17265/2159-581X/2015.03.002 Google Scholar
  22. 22.
    Samand, A., Wohlfart, L., Wolf, P.: Hands-on Knowledge Co-creation and Sharing: Practical Methods and Techniques. ISBN 978-951-6350-0 (2007)Google Scholar
  23. 23.
    McMichael, A.J.: Globalization, climate change, and human health. (Review article). The New Engl. J. Med. 2013(368), 1335–1343 (2013). doi:10.1056/NEJMra1109341 CrossRefGoogle Scholar
  24. 24.
    Hewitt, C., Mason, S., Walland, D.: The global framework for climate services. Nat. Clim. Change 2, 831–832 (2012)CrossRefGoogle Scholar
  25. 25.
    Gavidia, V., Talavera, M.: La construcción del concepto de salud. Didáctica de las Ciencias Experimentales y Sociales 26, 161–175 (2012)Google Scholar
  26. 26.
    Laschewski, G., Jendritzky, G.: Effects of the thermal environment on human health: an investigation of 30 years of daily mortality data from SW Germany. Clim. Res. 21, 91–103 (2002)CrossRefGoogle Scholar
  27. 27.
    Schuh, A.: Angewandte medizinische Klimatologie. Grundlagen und Praxis. Sonntag Verlag, Stuttgart (1996)Google Scholar
  28. 28.
    Eis, D., Helm, D., Laußmann, D., Stark, K. (eds.): Klimawandel und Gesundheit—Ein Sachstandsbericht. Robert Koch-Institut, Berlin (2010)Google Scholar
  29. 29.
    Kalkstein, L.S.: A new approach to evaluate the impact of climate upon human mortality. Environ. Health Perspect. 96, 145–150 (1991)CrossRefGoogle Scholar
  30. 30.
    Jendritzky, G.: The atmospheric environment—an introduction. Experientia 49, 733–740 (1993)CrossRefGoogle Scholar
  31. 31.
    Jendritzky, G., Bucher, K., Laschewski, G., Schultz, E., Staiger, H.: Medizinische Klimatologie, en: Handbuch der Balneologie und Medizinischen Klimatologie, ed. Por Gutenbrunnere Hildebrandt. Springer, Heidelberg (1998)Google Scholar
  32. 32.
    Rijssenbeek-Nouwens, L.H., Bel, E.H.: High-altitude treatment: a therapeutic option for patients with severe, refractory asthma? Clin. Exp. Allergy 41, 775–782 (2011)CrossRefGoogle Scholar
  33. 33.
    Schuh, A., Nowak, D.: Klimatherapie im Hochgebirge und im Meeresklima. Evidente Akut- und Langzeiteffekte—ein qualitativer Review. Dtsch. Med. Wochenschr. 136, 135–139 (2011)CrossRefGoogle Scholar
  34. 34.
    Rjissenbeek-Nouwens, L.H., Fieten, K.B., Bron, A.O., Hashimoto, S., Bel, E.H., Weersink, E.J.: High-altitude treatment in atopic and nonatpic patients with severe asthma. Eur. Respir. J. 40, 1320–1321 (2012)CrossRefGoogle Scholar
  35. 35.
    Massimo, T., Blank, C., Strasser, B., Schobersberger, W.: Does climate therapy at moderate altitudes improve pulmonary function in asthma patients? A systematic review. Sleep Breath. 18, 195–206 (2014)CrossRefGoogle Scholar
  36. 36.
    Jancloes, M., Thomson, M., Máñez Costa, M., Corvalan, C., Dinku, T., Lowe, R., Hayden, M., Hewitt, C.: Climate services to improve public health. Int. J. Environ. Res. Public Health 11, 4555–4559 (2014)CrossRefGoogle Scholar
  37. 37.
    Toloo, G., FitzGerald, G., Aitken, P., Verrall, K., Tong, S.: Evaluating the effectiveness of heat warning systems: systematic review of epidemiological evidence. Int. J. Public Health 58, 667–681 (2013)CrossRefGoogle Scholar
  38. 38.
    Connora, S.J., Omumbo, J., Green, C., DaSilva, J., Mantilla, G., Delacollette, C., Hales, S., Rogers, D., Thomson, M.: Health and climate—needs. Proc. Environ. Sci. 1, 27–36 (2010)CrossRefGoogle Scholar
  39. 39.
    WHO: Protecting Health from Climate Change: Global Research Priorities. World Health Organization, Geneva (2009)Google Scholar
  40. 40.
    WHO (2012) Health education: theoretical concepts, effective strategies and core competencies. A foundation document to guide capacity development of health educators. World Health Organization. Regional Office for the Eastern MediterraneanGoogle Scholar
  41. 41.
    Janssen, J., Stoyanov, S., Ferrari, A., Punie, Y., Pannekeet, K., Sloep, P.: Experts’ views on digital competence: Commonalities and differences. Comput. Educ. 68, 473–481 (2013)CrossRefGoogle Scholar
  42. 42.
    Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manage. 35, 137–144 (2015)CrossRefGoogle Scholar
  43. 43.
    Baumann, P., Mazzetti, P., Ungar, J., Barbera, R., Barboni, D., Beccati, A., Bigagli, L., Boldrini, E., Bruno, R., Calanducci, A., Campalani, P., Clements, O., Dumitru, A., Grant, M., Herzig, P., Kakaletris, G., Laxton, J., Koltsida, P., Lipskoch, K., Mahdiraji, A.R., Mantovani, S., Merticariu, V., Messina, A., Misev, D., Natali, S., Nativi, S., Oosthoek, J., Pappalardo, M., Passmore, J., Rossi, A.P., Rundo, F., Sen, M., Sorbera, V., Sullivan, D., Torrisi, M., Trovato, L., Veratelli, M.G., Wagner, S.: Big data analytics for earth sciences: the earth server approach. Int. J. Digit. Earth 9, 1 (2016)CrossRefGoogle Scholar
  44. 44.
    Sagiroglu, S., Sinanc, D.: Big data: a review. In: 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, 2013, pp. 42–47 (2013)Google Scholar
  45. 45.
    Krumholz, H.M.: For A learning health system big data and new knowledge in medicine: the thinking, training, and tools needed. Health Affairs 33(7):1163–1170 (2014)Google Scholar
  46. 46.
    Chatterjee, S., Ghosh, S., Dawn, S., Hore, S., Dey, N.: Optimized forest type classification: a machine intelligence approach. In: Third International Conference on Information System Design and Intelligent Applications, Vishakhapatnam. Springer AISC (In press)Google Scholar
  47. 47.
    Kriti, V.J., Dey, N., Kumar, V.: PCA-PNN and PCA-SVM based CAD systems for breast density classification. In: Hassanien, A.-E., Grosan, C., Fahmy Tolba, M. (ed.) Applications of Intelligent Optimization in Biology and Medicine: Current Trends and Open Problems, pp. 159–180. Springer International Publishing (2016)Google Scholar
  48. 48.
    Thein, H.T.T., Tun, K.M.M.: An approach for breast cancer diagnosis classification using neural network. Adv. Comput. Int. J. (ACIJ) 6, 1 (2015). doi:10.5121/acij.2015.6101 CrossRefGoogle Scholar
  49. 49.
    Samanta, S., Choudhury, A., Dey, N., Ashour, A.S., Balas, V.E.: Quantum inspired evolutionary algorithm for scaling factors optimization during manifold medical information embedding. In: Bhattacharyya, S., Maulik, U., Dutta, P. (eds.) Quantum Inspired Computational intelligence: Research and Applications. Elsevier (2016)Google Scholar
  50. 50.
    Herland, M., Khoshgoftaar, T.M., Wald, R.: A review of data mining using big data in health informatics. J. Big Data 1, 2 (2015)CrossRefGoogle Scholar
  51. 51.
    Lazer, D., Kennedy, R., King, G., Vespignani, A.: The parable of google flu: traps in big data analysis. Science 343, 1203–1205 (2014)CrossRefGoogle Scholar
  52. 52.
    King, G.: Ensuring the data-rich future of the social sciences. Science 331, 719–721 (2011)CrossRefGoogle Scholar
  53. 53.
    Khoury, M.J., Ioannidis, J.P.A.: Big data meets public health. Science 346, 1054 (2014)CrossRefGoogle Scholar
  54. 54.
    Nandi, S., Roy, S., Dansana, J., Ben, W., Karaa, A., Ray, R., Chowdhury, S.R., Chakraborty, S., Dey, N.: Cellular automata based encrypted ECG-hash code generation: an application in inter-human biometric authentication system. Int. J. Comput. Netw. Inf. Secur. 6(11), 1–12 (2014)Google Scholar
  55. 55.
    Biswas, S., Roy, A.B., Ghosh, K., Dey, N.: A biometric authentication based secured ATM banking system. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2, 4 (2012)Google Scholar
  56. 56.
    Bose, S., Madhulika, Acharjee, S., Chowdhury, S.R., Chakraborty, S., Dey, N.: Effect of watermarking in vector quantization based image compression. In: Control, Instrumentation, Communication and Computational Technologies (ICCICCT), International Conference Proceedings, pp. 503–508 (2014)Google Scholar
  57. 57.
    Dey, N., Bose, S., Das, A., Chaudhuri, S.S., Saba, L., Shafique, S., Nicolaides, A., Suri, J.S.: Effect of watermarking on diagnostic preservation of atherosclerotic ultrasound video in stroke telemedicine. J. Med. Syst. 40(4), 1–14 (2016)CrossRefGoogle Scholar
  58. 58.
    Dey, N., Ashour, A.S., Chakraborty, S., Banerjee, S., Gospodinova, E., Gospodinov, M., Hassanien, A.E.: Watermarking in bio-medical signal processing. In: Dey, N., Santhi, V. (eds.) Intelligent Techniques in Signal Processing for Multimedia Security. Springer SCI series, (2016)Google Scholar
  59. 59.
    Pal, K., Dey, N., Samanta, S., Das, A., Chaudhuri, S.S.: A hybrid reversible watermarking technique for color biomedical images. In: Computational Intelligence and Computing Research (ICCIC), 2013 IEEE, International Conference Proceedings, pp. 1–6 (2013)Google Scholar
  60. 60.
    Dey, N., Mukhopadhyay, S., Das, A., Chaudhuri, S.S.: Analysis of P-QRS-T components modified by blind watermarking technique within the electrocardiogram signal for authentication in wireless telecardiology using DWT. IJIGSP 4(7), 33–46 (2012)CrossRefGoogle Scholar
  61. 61.
    Acharjee, S., Ray, R., Chakraborty, S., Nath, S., Dey, N.: Watermarking in motion vector for security enhancement of medical videos. In: Control, Instrumentation, Communication and Computational Technologies (ICCICCT), International Conference Proceedings, pp. 532–537 (2014)Google Scholar
  62. 62.
    EEA: Environment and human health, Joint EEA-JRC report Nr 5 (Report EUR 25933 EN), inf. téc., European Environment Agency (2013)Google Scholar
  63. 63.
    Pick, J.B., Sarkar, A.: The Global Digital Divides Explaining Change. Springer, Progress in IS (2015)Google Scholar
  64. 64.
    Fernández de Arroyabe Hernáez, P.: Virtual divide, Bologna education model and geographic information technologies, GeoFocus 6, 39–51, ISSN: 1578-5157 (2006)Google Scholar
  65. 65.
    Mardikyan, S., Yıldız, E.A., Ordu, M.D., Şimşek, B.: Examining the global digital divide: a cross-country analysis. Commun. IBIMA 2015(592253) (2015). doi:10.5171/2015.592253
  66. 66.
    CEDEFOP: Lifelong Learning: Citizens’ Views in Close Up. Center for the Development of Vocational Training (2004) Google Scholar
  67. 67.
    Levy, H., Janke, A.T., Langa, K.M.: Health literacy and the digital divide among older Americans. J. Gen. Intern. Med. 30, 284–289 (2015)CrossRefGoogle Scholar
  68. 68.
    Fernández de Arróyabe, P.: Climate services and human health: a niche of opportunities for economic growth. Scientific Annals of Alexandru Ioan Cuza Geography Series, University of Iasi, Vol. LIX nº 2, Rumania, ISSN: 1223–5334 (printed version), (online version) eISSN 2284-6379 (2013)Google Scholar
  69. 69.
    Fernández de Arróyabe, P.: Meteorological conditions and human health. In: Carlos Garcia-Legaz Martínez y Francisco Valero Rodriguez (eds.) Adverse Weather in Spain. WCRP Spanish Committee & CCS. ISBN: 978-84-96709-88-1 (2013)Google Scholar
  70. 70.
    App OxyAlert Beta (2014) Geobiomet Research Group UC. https://play.google.com/store/apps/details?id=es.geobiomet.oxyalert&hl=es

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Geography, Urbanism and PlanningUniversity of Cantabria, GEOBIOMET Research GroupPCSpain

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