Self-organizing maps as an approach to exploring spatiotemporal diffusion patterns.

ABSTRACT:

Background

Self-organizing maps (SOMs) have now been applied for a number of years to identify patterns in large datasets; yet, their application in the spatiotemporal domain has been lagging. Here, we demonstrate how spatialtemporal disease diffusion patterns can be analysed using SOMs and Sammon's projection.

Methods

SOMs were applied to identify synchrony between spatial locations, to group epidemic waves based on similarity of diffusion pattern and to construct sequence of maps of synoptic states. The Sammon's projection was used to created diffusion trajectories from the SOM output. These methods were demonstrated with a dataset that reports Measles outbreaks that took place in Iceland in the period 1946-1970. The dataset reports the number of Measles cases per month in

SUBMITTER: Augustijn EW 

PROVIDER: S-EPMC3882328 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature