Applications of Big Spatial Data: Health
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...
- 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
- Sui D, Elwood S, Goodchild M (eds) (2013) Crowdsourcing geographic knowledge: volunteered geographic information in theory and practice. Springer, DondrechtGoogle 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 CrossRefGoogle Scholar