  Knowledge of Wi-Fi networks helps to guide future engineering and spectrum
policy decisions. However, due to its unlicensed nature, the deployment of
Wi-Fi Access Points is undocumented meaning researchers are left making
educated guesses as to the prevalence of these assets through remotely
collected or passively sensed measurements. One commonly used method is
referred to as `wardriving` essentially where a vehicle is used to collect
geospatial statistical data on wireless networks to inform mobile computing and
networking security research. Surprisingly, there has been very little
examination of the statistical issues with wardriving data, despite the vast
number of analyses being published in the literature using this approach. In
this paper, a sample of publicly collected wardriving data is compared to a
predictive model for Wi-Fi Access Points. The results demonstrate several
statistical issues which future wardriving studies must account for, including
selection bias, sample representativeness and the modifiable areal unit
problem.
