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ISPRS Int. J. Geo-Inf. 2014, 3(3), 1023-1038; doi:10.3390/ijgi3031023

Holistics 3.0 for Health

1
Hanson Center for Space Science, University of Texas at Dallas, Richardson, TX 75080, USA
2
Center on Society and Health, Virginia Commonwealth University, Richmond, VA 23298, USA
3
GIS & Remote Sensing Program, University of Mississippi Medical Center, MS 39216, USA
4
ACOS Research and Development, VA North Texas Health Care System, Dallas, TX 75216, USA
*
Author to whom correspondence should be addressed.
Received: 21 February 2014 / Revised: 1 July 2014 / Accepted: 10 July 2014 / Published: 24 July 2014
(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)
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Abstract

Human 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. View Full-Text
Keywords: geospatial; machine learning; Big Data; health; remote sensing; Holistics 3.0; data-driven decisions geospatial; machine learning; Big Data; health; remote sensing; Holistics 3.0; data-driven decisions
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Lary, D.J.; Woolf, S.; Faruque, F.; LePage, J.P. Holistics 3.0 for Health. ISPRS Int. J. Geo-Inf. 2014, 3, 1023-1038.

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