Big Data Solutions to Interpreting Complex Systems in the Environment

  • Hongmei ChiEmail author
  • Sharmini Pitter
  • Nan Li
  • Haiyan Tian
Part of the Studies in Big Data book series (SBD, volume 26)


The amount of relevant published data sets available in the environmental sciences is rapidly increasing in recent years. For example, the National Oceanic and Atmospheric Administration (NOAA) has published vast data resources and tremendous volumes of high quality environmental data. Analyzing those data sets poses unprecedented challenges and opportunities to environmental scientists. The goal of this chapter is to present a practical investigation of big data tools that can be used to analyze environmental data sets and provide environmental information to decision makers in the political and non-profit spheres. Throughout this chapter, we will provide examples of the uses of big data analysis in assessing environmental impact and change in real-time in hopes of initiating discussion towards benchmarking key features and considerations of big data techniques.


Data analysis RapidMiner Hurricane dataset Environmental microbiology Environmental datasets Remote sensing data Sensor network 


  1. Barrett, T., Troup, D. B., Wilhite, S. E., Ledoux, P., Rudnev, D., Evangelista, C., et al. (2009). NCBI GEO: Archive for high-throughput functional genomic data. Nucleic Acids Research, 37(suppl 1), D885–D890.Google Scholar
  2. Basha, E. A., Ravela, S., & Rus, D. (2008). Model-based monitoring for early warning flood detection. In Proceedings of the 6th ACM conference on embedded network sensor systems (pp. 295–308). ACM.Google Scholar
  3. Belasen, A. R., & Polachek, S. W. (2009). How disasters affect local labor markets the effects of hurricanes in Florida. Journal of Human Resources, 44(1), 251–276.CrossRefGoogle Scholar
  4. Bjarnadottir, S., Li, Y., & Stewart, M. G. (2011). A probabilistic-based framework for impact and adaptation assessment of climate change on hurricane damage risks and costs. Structural Safety, 33(3), 173–185.CrossRefGoogle Scholar
  5. Blake, E. S., Rappaport, E. N., & Landsea, C. W. (2007). The deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2006 (and other frequently requested hurricane facts) (p. 43). NOAA/National Weather Service, National Centers for Environmental Prediction, National Hurricane Center.Google Scholar
  6. Bossak, B. H., et al. (2014). Coastal Georgia is not immune: Hurricane history, 1851–2012. Southeastern Geographer, 54(3), 323–333.CrossRefGoogle Scholar
  7. Boyd, D., & Crawford, K. (2012). Critical questions for big data. Information, Communication and Society, 15(5), 662–679.CrossRefGoogle Scholar
  8. Cock, P. J., et al. (2015). NCBI BLAST+ integrated into galaxy. Gigascience, 4, 39.CrossRefGoogle Scholar
  9. Denman, S. E., Martinez Fernandez, G., Shinkai, T., Mitsumori, M., & McSweeney, C. S. (2015). Metagenomic analysis of the rumen microbial community following inhibition of methane formation by a halogenated methane analog. Frontiers in Microbiology, 6, 1087.Google Scholar
  10. Earth Observing System Data and Information System (EOSDIS) (2009). Earth Observing System ClearingHOuse (ECHO) /Reverb, Version 10.X [online application]. Greenbelt, MD: EOSDIS, Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA). URL:
  11. Federhen, S. (2012). The NCBI Taxonomy database. Nucleic Acids Research, 40, D136–D143.CrossRefGoogle Scholar
  12. Fichant, G., Basse, M. J., & Quentin, Y. (2006). ABCdb: An online resource for ABC transporter repertories from sequenced archaeal and bacterial genomes. FEMS Microbiology Letters, 256, 333–339.CrossRefGoogle Scholar
  13. Frazier, T. G., Wood, N., Yarnal, B., & Bauer, D. H. (2010). Influence of potential sea level rise on societal vulnerability to hurricane storm-surge hazards, Sarasota County, Florida. Applied Geography, 30(4), 490–505.Google Scholar
  14. Greenwood, P. L., Valencia, P., Overs, L., Paull, D. R., & Purvis, I. W. (2014). New ways of measuring intake, efficiency and behaviour of grazing livestock. Animal Production Science, 54(10), 1796–1804.CrossRefGoogle Scholar
  15. Guo, J., Peng, Y., Fan, L., Zhang, L., Ni, B. J., Kartal, B., et al. (2016). Metagenomic analysis of anammox communities in three different microbial aggregates. Environmental Microbiology, 18(9), 2979–2993.Google Scholar
  16. Hampton, S. E., Strasser, C. A., Tewksbury, J. J., Gram, W. K., Budden, A. E., Batcheller, A. L., etal. (2013). Big data and the future of ecology. Frontiers in Ecology and the Environment, 11(3), 156–162.Google Scholar
  17. Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of Big Data research. Big Data Research, 2(2), 59–64.Google Scholar
  18. Kelly, K. (2007). What is the quantified self. The Quantified Self, 5, 2007.Google Scholar
  19. Khedo, K. K., Perseedoss, R., & Mungur, A. (2010a). A wireless sensor network air pollution monitoring system. Preprint arXiv:1005.1737.Google Scholar
  20. Khedo, K. K., Perseedoss, R., Mungur, A., & Mauritius. (2010b). A wireless sensor network air pollution monitoring system. International Journal of Wireless and Mobile Networks, 2(2), 31–45.CrossRefGoogle Scholar
  21. Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 2053951714528481.CrossRefGoogle Scholar
  22. Kodama, Y., Shumway, M., & Leinonen, R. (2012). The International Nucleotide Sequence Database Collaboration. The sequence read archive: explosive growth of sequencing data. Nucleic Acids Research, 40, D54–D56.CrossRefGoogle Scholar
  23. Landsea, C. W., & Franklin, J. L. (2013). Atlantic hurricane database uncertainty and presentation of a new database format. Monthly Weather Review, 141(10), 3576–3592.CrossRefGoogle Scholar
  24. Landsea, C. W., et al. (2004). The Atlantic hurricane database re-analysis project: Documentation for the 1851–1910 alterations and additions to the HURDAT database. In Hurricanes and typhoons: Past, present and future (pp. 177–221).Google Scholar
  25. Lehmann, R. J., Reiche, R., & Schiefer, G. (2012). Future internet and the agri-food sector: State-of-the-art in literature and research. Computers and Electronics in Agriculture, 89, 158–174.CrossRefGoogle Scholar
  26. Li, N., Chen, H., & Williams, H. N. (2015). Genome-wide comparative analysis of ABC systems in the Bdellovibrio-and-like organisms. Gene, 562, 132–137.CrossRefGoogle Scholar
  27. Lo, B. P., Thiemjarus, S., King, R., & Yang, G. Z. (2005). Body sensor network–a wireless sensor platform for pervasive healthcare monitoring. In 3rd international conference on pervasive computing, Munich, Germany, May 8–13 (pp. 77–80). Google Scholar
  28. Ma, Y., Wu, H., Wang, L., Huang, B., Ranjan, R., Zomaya, A., & Jie, W. (2015). Remote sensing big data computing: challenges and opportunities. Future Generation Computer Systems, 51, 47–60.CrossRefGoogle Scholar
  29. Malmstadt, J., Scheitlin, K., & Elsner, J. (2009). Florida hurricanes and damage costs. Southeastern Geographer, 49(2), 108–131.CrossRefGoogle Scholar
  30. NHC Data Archive. Retrieved from <>, June 7, 2016.
  31. Nielsen, J., & Lee, S. Y. (2012). Systems biology: The ‘new biotechnology’. Current Opinion in Biotechnology, 23, 583–584.CrossRefGoogle Scholar
  32. Powell, M. D., Houston, S. H., Amat, L. R., & Morisseau-Leroy, N. (1998). The HRD real-time hurricane wind analysis system. Journal of Wind Engineering and Industrial Aerodynamics, 77, 53–64.Google Scholar
  33. Powell, M. D., Uhlhorn, E. W., & Kepert, J. D. (2009). Estimating maximum surface winds from hurricane reconnaissance measurements. Weather and Forecasting, 24(3), 868–883.CrossRefGoogle Scholar
  34. Pruitt, K. D., Tatusova, T., & Maglott, D. R. (2007). NCBI reference sequences (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Research, 35, D61–D65.CrossRefGoogle Scholar
  35. Ravi, M., & Subramaniam, P. (2014). Wireless sensor network and its security—A survey. International Journal of Science and Research (IJSR), 3, 12.Google Scholar
  36. Saffir, H. S. (1973). Hurricane wind and storm surge, and the hurricane impact scale (p. (423). The Military Engineer: Alexandria, VA.Google Scholar
  37. Schott, T., Landsea, C., Hafele, G., Lorens, J., Taylor, A., Thurm, H., et al. (2012). The Saffir-Simpson hurricane wind scale. National Hurricane Center. National Weather Service. Coordinación General de Protección Civil de Tamaulipas. National Oceanic and Atmospheric Administration (NOAA) factsheet. URL: Scholar
  38. Shiffrin, R. M. (2016). Drawing causal inference from Big Data. Proceedings of the National Academy of Sciences, 113(27), 7308–7309.CrossRefGoogle Scholar
  39. Simpson, R. H., & Saffir, H. (1974). The hurricane disaster potential scale. Weatherwise, 27(8), 169.Google Scholar
  40. South Florida Regional Climate Compact (SFRCCC) 2012. Analysis of the vulnerability of Southeast Florida to sea-level rise. Available online: Accessed 14 August 2016.
  41. Swan, M. (2013). The quantified self: Fundamental disruption in big data science and biological discovery. Big Data, 1(2), 85–99.CrossRefGoogle Scholar
  42. Ulrichs, M., Cannon, T., Newsham, A., Naess, L. O., & Marshall, M. (2015). Climate change and food security vulnerability assessment. Toolkit for assessing community-level potential for adaptation to climate change. Available online: Accessed 15 August 2016.
  43. Wdowinski, S., Bray, R., Kirtman, B. P., & Wu, Z., et al. (2016). Increasing flooding hazard in coastal communities due to rising sea level: Case study of Miami Beach, Florida. Ocean and Coastal Management, 126, 1–8.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Hongmei Chi
    • 1
    Email author
  • Sharmini Pitter
    • 1
  • Nan Li
    • 2
  • Haiyan Tian
    • 3
  1. 1.Florida A&M UniversityTallahasseeUSA
  2. 2.Guangxi Teachers Education UniversityNanningChina
  3. 3.University of Southern MississippiHattiesburgUSA

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