Big Data Solutions to Interpreting Complex Systems in the Environment
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.
KeywordsData analysis RapidMiner Hurricane dataset R Environmental microbiology Environmental datasets Remote sensing data Sensor network
- 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
- 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
- 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
- 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
- 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: http://reverb.earthdata.nasa.gov.
- 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
- 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
- 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
- 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
- Kelly, K. (2007). What is the quantified self. The Quantified Self, 5, 2007.Google Scholar
- Khedo, K. K., Perseedoss, R., & Mungur, A. (2010a). A wireless sensor network air pollution monitoring system. Preprint arXiv:1005.1737.Google Scholar
- 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
- 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
- NHC Data Archive. Retrieved from <http://www.nhc.noaa.gov/data/hurdat/hurdat2-1851-2015-070616.txt>, June 7, 2016.
- 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
- Ravi, M., & Subramaniam, P. (2014). Wireless sensor network and its security—A survey. International Journal of Science and Research (IJSR), 3, 12.Google Scholar
- Saffir, H. S. (1973). Hurricane wind and storm surge, and the hurricane impact scale (p. (423). The Military Engineer: Alexandria, VA.Google Scholar
- 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: http://www.nhc.noaa.gov/pdf/sshws.pdf.Google Scholar
- Simpson, R. H., & Saffir, H. (1974). The hurricane disaster potential scale. Weatherwise, 27(8), 169.Google Scholar
- South Florida Regional Climate Compact (SFRCCC) 2012. Analysis of the vulnerability of Southeast Florida to sea-level rise. Available online: http://www.southeastfloridaclimatecompact.org/wp-content/uploads/2014/09/regional-climate-action-plan-final-ada-compliant.pdf. Accessed 14 August 2016.
- 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: https://cgspace.cgiar.org/rest/bitstreams/55087/retrieve. Accessed 15 August 2016.
- 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