Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 74, Issue -, Pages 238-246Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2015.06.003
Keywords
Integrated modelling; Environmental sensors; Population health; Environmental health; Big data
Categories
Funding
- NIH [R21ES024715]
- Minnesota Discovery, Research and InnoVation Economy (MnDRIVE)
- Institute on the Environment Discovery Grant funding at the University of Minnesota Twin-Cities
- UK Natural Environment Research Council through National Capability funding
- Natural Environment Research Council [NE/I007490/1, ceh020007] Funding Source: researchfish
- NERC [ceh020007, NE/I007490/1] Funding Source: UKRI
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Sensors are becoming ubiquitous in everyday life, generating data at an unprecedented rate and scale. However, models that assess impacts of human activities on environmental and human health, have typically been developed in contexts where data scarcity is the norm. Models are essential tools to understand processes, identify relationships, associations and causality, formalize stakeholder mental models, and to quantify the effects of prevention and interventions. They can help to explain data, as well as inform the deployment and location of sensors by identifying hotspots and areas of interest where data collection May achieve the best results. We identify a paradigm shift in how the integration of models and sensors can contribute to harnessing 'Big Data' and, more importantly, make the vital step from 'Big Data' to 'Big Information'. In this paper, we illustrate current developments and identify key research needs using human and environmental health challenges as an example. Crown Copyright (C) 2015 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
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