Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Applications of Big Spatial Data: Health

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


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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  1. Brownstein JS, Freifeld CC, Madoff LC (2009) Digital disease detection: harnessing the web for public health surveillance. N Engl J Med 360:2153–2157CrossRefGoogle Scholar
  2. Estrin D, Sim I (2010) Open mHealth architecture: an engine for health care innovation. Science 330:759–760CrossRefGoogle Scholar
  3. Goodchild MF (1992) Geographic information science. Int J Geogr Inf Syst 6:31–45CrossRefGoogle Scholar
  4. Kindig D, Stoddart G (2003) What is population health? Am J Public Health 93:380–383CrossRefGoogle Scholar
  5. Kitron U (1998) Landscape ecology and epidemiology of vector-borne diseases: tools for spatial analysis. J Med Entomol 35:435–445CrossRefGoogle Scholar
  6. Krieger N (2001) Theories for social epidemiology in the 21st century: an ecosocial perspective. Int J Epidemiol 30:668–677CrossRefGoogle Scholar
  7. Lazar D, Kennedy R, King G, Vespignani A (2014) The parable of Google Flu: traps in big data analysis. Science 343:1203–1205CrossRefGoogle Scholar
  8. 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 39:3269–3278CrossRefGoogle Scholar
  9. 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 23:899–908CrossRefGoogle Scholar
  10. Miller GM, Jones DP (2014) The nature of nurture: refining the definition of the exposome. Toxicol Sci 137:1–2CrossRefGoogle Scholar
  11. Murdoch TB, Detsky AS (2013) The inevitable application of big data to heath care. J Am Med Assoc 309:1351–1352CrossRefGoogle Scholar
  12. Murray CJL, Lopez AD (1997) Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study. Lancet 349:1498–1504CrossRefGoogle Scholar
  13. 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):5–10CrossRefGoogle Scholar
  14. 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 67:231–253MathSciNetCrossRefGoogle Scholar
  15. Sui D, Elwood S, Goodchild M (eds) (2013) Crowdsourcing geographic knowledge: volunteered geographic information in theory and practice. Springer, DondrechtGoogle Scholar
  16. 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 CrossRefGoogle Scholar
  17. Waller LA, Gotway CA (2004) Applied spatial statistics for public health data. Wiley, HobokenCrossRefGoogle Scholar
  18. Wild CP (2005) Complementing the genome with the “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomark Prev 14:1847–1850CrossRefGoogle Scholar

Authors and Affiliations

  1. 1.Department of Biostatistics and BioinformaticsRollins School of Public Health, Emory UniversityAtlantaUSA

Section editors and affiliations

  • Timos Sellis
    • 1
  • Aamir Cheema
  1. 1.Data Science Research InstituteSwinburne University of TechnologyMelbourneAustralia