Lary, David J.Woolf, StevenFaruque, FazlayLePage, James P.2017-03-312017-03-312014-07-242014-07-242220-9964http://hdl.handle.net/10735.1/5344Human health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting new era is dawning where we are simultaneously collecting multiple datasets to describe many aspects of health, wellness, human activity, environment and disease. Valuable insights from these datasets can be extracted using massively multivariate computational techniques, such as machine learning, coupled with geospatial techniques. These computational tools help us to understand the topology of the data and provide insights for scientific discovery, decision support and policy formulation. This paper outlines a holistic paradigm called Holistics 3.0 for analyzing health data with a set of examples. Holistics 3.0 combines multiple big datasets set in their geospatial context describing as many areas of a problem as possible with machine learning and causality, to both learn from the data and to construct tools for data-driven decisions.enCC BY 3.0 (Attribution)©2014 The Authorshttp://creativecommons.org/licenses/by/3.0/Geospatial dataMachine learningBig dataRemote sensingHolistics 3.0Data-driven decisionsRemote-sensingAir quality--United StatesSpectroradiometerForest firesModis (Spectroradiometer)Physical geographyArticleLary, David John, Steven Woolf, Fazlay Faruque, and James P. LePage. 2014. "Holistics 3.0 for Health." ISPRS International Journal of Geo-Information 3(3), doi:10.3390/ijgi303102333