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Applications of Big Spatial Data: Health

Encyclopedia of Big Data Technologies
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Definitions

The term “big spatial data” encompasses all types of big data with the addition of geographic reference information, typically a location associated with a point in space (e.g., latitude, longitude, and altitude coordinates), an area (e.g., a country, a district, or a census enumeration zone), a line or curve (e.g., a river or a road), or a pixel (e.g., high-resolution satellite images or a biomedical imaging scan). When applied to questions of health, big spatial data can aid in attempts to understand geographic variations in the risks and rates of disease (e.g., is risk here greater than risk there?), to identify local factors driving geographic variations in risks and rates (e.g., does local nutritional status impact local childhood mortality?), and to evaluate the impact of local health policies (e.g., district-specific adjustments to insurance reimbursements).

In addition to defining big spatial data, it is also important to define what is meant by “health.” The World...

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References

  • Brownstein JS, Freifeld CC, Madoff LC (2009) Digital disease detection: harnessing the web for public health surveillance. N Engl J Med 360:2153–2157

    Article  Google Scholar 

  • Estrin D, Sim I (2010) Open mHealth architecture: an engine for health care innovation. Science 330:759–760

    Article  Google Scholar 

  • Goodchild MF (1992) Geographic information science. Int J Geogr Inf Syst 6:31–45

    Article  Google Scholar 

  • Kindig D, Stoddart G (2003) What is population health? Am J Public Health 93:380–383

    Article  Google Scholar 

  • Kitron U (1998) Landscape ecology and epidemiology of vector-borne diseases: tools for spatial analysis. J Med Entomol 35:435–445

    Article  Google Scholar 

  • Krieger N (2001) Theories for social epidemiology in the 21st century: an ecosocial perspective. Int J Epidemiol 30:668–677

    Article  Google Scholar 

  • Lazar D, Kennedy R, King G, Vespignani A (2014) The parable of Google Flu: traps in big data analysis. Science 343:1203–1205

    Article  Google Scholar 

  • Liu Y, Sarnat JA, Kilaru V, Jacob DJ, Koutrakis P (2005) Estimating ground-level PM2.5 in the Eastern United States using satellite remote sensing. Environ Sci Technol 39:3269–3278

    Article  Google Scholar 

  • Mandel JC, Kreda DA, Mandl KD, Kohane IS, Romoni RB (2016) SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc 23:899–908

    Article  Google Scholar 

  • Miller GM, Jones DP (2014) The nature of nurture: refining the definition of the exposome. Toxicol Sci 137:1–2

    Article  Google Scholar 

  • Murdoch TB, Detsky AS (2013) The inevitable application of big data to heath care. J Am Med Assoc 309:1351–1352

    Article  Google Scholar 

  • Murray CJL, Lopez AD (1997) Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study. Lancet 349:1498–1504

    Article  Google Scholar 

  • Nilsen W, Kumar S, Shar A, Varoquiers C, Wiley T, Riley WT, Pavel M, Atienza AA (2012) Advancing the science of mHealth. J Health Commun 17(supplement 1):5–10

    Article  Google Scholar 

  • Shaddick G, Thomas ML, Green A, Brauer M, van Donkelaar A, Burnett R, Chang HH, Cohen A, van Dingenen R, Dora C, Gumy S, Liu Y, Martin R, Waller LA, West J, Zidek JV, Pruss-Ustun A (2017) Data integration model for air quality: a hierarchical approach to the global estimation of exposures to air pollution. J R Stat Soc Ser C 67:231–253

    Article  MathSciNet  Google Scholar 

  • Sui D, Elwood S, Goodchild M (eds) (2013) Crowdsourcing geographic knowledge: volunteered geographic information in theory and practice. Springer, Dondrecht

    Google Scholar 

  • Vazquez-Prokopec GM, Stoddard ST, Paz-Soldan V, Morrison AC, Elder JP, Kochel TJ, Scott TW, Kitron U (2009) Usefulness of commercially available GPS data-loggers for tracking human movement and exposure to dengue virus. Int J Health Geogr 8:68. https://doi.org/10.1186/1476-072X-8-68

    Article  Google Scholar 

  • Waller LA, Gotway CA (2004) Applied spatial statistics for public health data. Wiley, Hoboken

    Book  Google Scholar 

  • Wild CP (2005) Complementing the genome with the “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomark Prev 14:1847–1850

    Article  Google Scholar 

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Correspondence to Lance A. Waller .

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Waller, L.A. (2018). Applications of Big Spatial Data: Health. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_72-1

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  • DOI: https://doi.org/10.1007/978-3-319-63962-8_72-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63962-8

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Chapter history

  1. Latest

    Applications of Big Spatial Data: Health
    Published:
    16 September 2022

    DOI: https://doi.org/10.1007/978-3-319-63962-8_72-2

  2. Original

    Applications of Big Spatial Data: Health
    Published:
    25 April 2018

    DOI: https://doi.org/10.1007/978-3-319-63962-8_72-1