Bayesian Modeling and Analysis for Gradients in Spatiotemporal Processes

Details

• Personal Author:
  Banerjee S ; Carlin BP ; Quick H
• Description:
  Stochastic process models are widely employed for analyzing spatiotemporal datasets in various scientific disciplines including, but not limited to, environmental monitoring, ecological systems, forestry, hydrology, meteorology, and public health. After inferring on a spatiotemporal process for a given dataset, inferential interest may turn to estimating rates of change, or gradients, over space and time. This manuscript develops fully model-based inference on spatiotemporal gradients under continuous space, continuous time settings. Our contribution is to offer, within a flexible spatiotemporal process model setting, a framework to estimate arbitrary directional gradients over space at any given timepoint, temporal derivatives at any given spatial location and, finally, mixed spatiotemporal gradients that reflect rapid change in spatial gradients over time and vice-versa. We achieve such inference without compromising on rich and flexible spatiotemporal process models and use nonseparable covariance structures. We illustrate our methodology using a simulated data example and subsequently apply it to a dataset of daily PM2.5 concentrations in California, where the spatiotemporal gradient process reveals the effects of California's unique topography on pollution and detects the aftermath of a devastating series of wildfires. [Description provided by NIOSH]
• Subjects:
  Air Pollution Occupational Health Particulate Matter Safety
• Keywords:
  Air Pollution Author Keywords: Gaussian Process Exposure Assessment Gradients Markov Chain Monte Carlo Models Particulates Statistical Analysis
• ISSN:
  0006-341X
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; Georgia ; Minnesota ; OSHA Region 4 ; OSHA Region 5 ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  71
• Issue:
  3
• NIOSHTIC Number:
  nn:20055683
• Citation:
  Biometrics 2015 Sep; 71(3):575-584
• Contact Point Address:
  Sudipto Banerjee, Department of Biostatistics, University of California, Los Angeles, California 90095-1772, U.S.A.
• Email:
  sudipto@ucla.edu
• Federal Fiscal Year:
  2015
• Performing Organization:
  University of California, Los Angeles
• Peer Reviewed:
  True
• Start Date:
  20130901
• Source Full Name:
  Biometrics
• End Date:
  20170831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:377cb36e3e0a91d4e2034d9f8535d89be916e53b2cab2ddeacf78c8d7a3afb5b9d07f2af78cbe5ee5093dcdcb70853e7fb60f8515c2316a6a02ac63369d23cb6
• Download URL:
  https://stacks.cdc.gov/view/cdc/213473/cdc_213473_DS1.pdf
• File Type:
  [PDF - 1.22 MB ]