  Non-continuous location traces inferred from Call Detail Records (CDR) at
population scale are increasingly becoming available for research and show
great potential for automated detection of meaningful places. Yet, a majority
of Home Detection Algorithms (HDAs) suffer from "blind" deployment of criteria
to define homes and from limited possibilities for validation. In this paper,
we investigate the performance and capabilities of five popular criteria for
home detection based on a very large mobile phone dataset from France (~18
million users, 6 months). Furthermore, we construct a data-driven framework to
assess the spatial uncertainty related to the application of HDAs. Our findings
appropriate spatial uncertainty in HDA and, in extension, for detection of
meaningful places. We show how spatial uncertainties on the individuals' level
can be assessed in absence of ground truth annotation, how they relate to
traditional, high-level validation practices and how they can be used to
improve results for, e.g., nation-wide population estimation.
