  Gamma-ray spectral data were collected from sensors mounted to traffic
signals around Northern Virginia. The data were collected over a span of
approximately fifteen months. A subset of the data were analyzed manually and
subsequently used to train machine-learning models to facilitate the evaluation
of the remaining 50k anomalous events identified in the dataset. We describe
the analysis approach used here and discuss the results in terms of
radioisotope classes and frequency patterns over day-of-week and time-of-day
spans. Data from this work has been archived and is available for future and
ongoing research applications.
