title: 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 50 medical districts.
  
  
  RESULTS
  Both stable and incidental synchronisation between medical districts were identified as well as two distinct groups of epidemic waves, a uniformly structured fast developing group and a multiform slow developing group. Diffusion trajectories for the fast developing group indicate a typical diffusion pattern from Reykjavik to the northern and eastern parts of the island. For the other group, diffusion trajectories are heterogeneous, deviating from the Reykjavik pattern.
  
  
  CONCLUSIONS
  This study demonstrates the applicability of SOMs (combined with Sammon's Projection and GIS) in spatiotemporal diffusion analyses. It shows how to visualise diffusion patterns to identify (dis)similarity between individual waves and between individual waves and an overall time-series performing integrated analysis of synchrony and diffusion trajectories.
authors.affiliation: Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands. p.w.m.augustijn@utwente.nl.
authors.forename: Ellen-Wien
authors.initials: EW
authors.lastname: Augustijn
authors.forename: Raul
authors.initials: R
authors.lastname: Zurita-Milla
mesh_terms: D016208:Databases, Factual; D058872:Epidemics; D062305:Geographic Mapping; D006801:Humans; D007055:Iceland / Q000453:epidemiology; D008457:Measles / Q000175:diagnosis / Q000453:epidemiology*; D062211:Spatio-Temporal Analysis; D013997:Time Factors