Holistics 3.0 for health

David John Lary, Steven Woolf, Fazlay Faruque, James P. LePage

Research output: Contribution to journalArticle

9 Scopus citations

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. c 2014 by the authors; licensee MDPI, Basel, Switzerland.

Original languageEnglish (US)
Pages (from-to)1023-1038
Number of pages16
JournalISPRS International Journal of Geo-Information
Volume3
Issue number3
DOIs
StatePublished - Sep 2014

Keywords

  • Big Data
  • Data-driven decisions
  • Geospatial
  • Health
  • Holistics 3.0
  • Machine learning
  • Remote sensing

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)

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