Geolocation data recorded by consumer electronics is usually considered very helpful in criminal investigations: Every few steps, every few seconds, the location of a suspect, victim, witness or others can be pinpointed as it was automatically recorded in the background. Compared to the commonly used cell tower location data, device-local data from a
global navigation satellite system
(GNSS) has far higher precision (both spatial and temporal), but suffers from a lack of trust, because data provenance is under potential control of the user. In this paper, we propose two approaches of validating and analyzing such data with high confidence. The first approach formulates and checks internal consistency criteria of GNSS data, while the second approach also takes external data sources about the surrounding environment in the form of OpenStreetMap data into account. In both approaches, we formalize the concept of a
data anomaly
and argue that an absence of anomalies implies more trustworthy data and thus higher evidential value. This way, the vast information contained in high-density location data may actually lead to more detailed insights instead of only increasing data noise in investigations.