Statistical Perspectives On the Human Factor in Spot Data from RBN and WSPR Networks

The amateur radio reporting networks RBN and WSPRnet generate a wealth of data that can be used to great advantage in scientific research, and previous analyses of these data have shown that space weather events and ionospheric disturbances can be detected through patterns in the spot data. Although the spots recorded by the network undoubtedly reflect such changes in the natural environment, these patterns are confounded with the effects of human behaviors, such as the geographic dispersion of ham radio operators, time preferences among operators, and different levels of activity for different stations.  Statistical models have the potential to estimate these “human effects” and decouple them from the natural process that makes propagation possible. We will present a statistical modeling approach for these data that accounts for the non-probabilistic sampling methods that produce them.  We will also present Spot Watcher, an app that we are developing for visualization of spots using open source tools, and comment on some of the pre-processing challenges in statistical analysis of spot data.