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Multiscale modeling approach for hierarchical aligned aggregated small area health data

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Kevin WatjouAuthors Info & Claims

SIGSPATIAL Special, Volume 8, Issue 1

Pages 12 - 19

https://doi.org/10.1145/2961028.2961032

Published: 21 June 2016 Publication History

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Abstract

When data are aggregated from a finer to a coarser geographical level, there will be loss of information known as the scaling problem in geography. To address the scaling problem, we propose to use a joint convolution model that describes the risk variation at both the finer and coarser levels simultaneously by sharing both the correlated and the uncorrelated components. We compare our model with the naive approach that ignores the scale effect in real and simulated data in a range of criteria such as deviance information criterion (DIC), Watanabe-Akaike information criterion, and mean square prediction error (MSPE). We found that our multiscale model is better than the naive model.

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Published In

SIGSPATIAL Special Volume 8, Issue 1

March 2016

55 pages

EISSN:1946-7729

DOI:10.1145/2961028

Editor:
Chi-Yin (Ted) Chow
City University of Hong Kong

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Association for Computing Machinery

New York, NY, United States

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Published: 21 June 2016

Published in SIGSPATIAL Volume 8, Issue 1

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Bayesian multiscale modeling for aggregated disease mapping data

Multiscale finite impulse response modeling

Numerical methods for multiscale elliptic problems

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