  Large-scale location based traces, such as mobile phone data, have been
identified as a promising data source to complement or even enrich official
statistics. In many cases, a prerequisite step to deploy the massively gathered
data is the detection of home location from individual users. The problem is
that little research exists on the validation (comparison with ground truth
datasets) or the uncertainty estimation of home detection methods, not at
individual user level, nor at nation-wide levels. In this paper, we present an
extensive empirical analysis of home detection methods when performed on a
nation-wide mobile phone dataset from France. We analyze the validity of 9
different Home Detection Algorithms (HDAs), and we assess different sources of
uncertainty. Based on 225 different set-ups for the home detection of around 18
million users we discuss different measures for validation and investigate
sensitivity to user choices such as HDA parameter choice and observation period
restriction. Our findings show that nation-wide performance of home detection
is moderate at best, with correlations to ground truth maximizing at 0.60 only.
Additionally, we show that time and duration of observation have a clear effect
on performance, and that the effect of HDA criteria and parameter choice are
rather small compared to other uncertainties. Our findings and discussion offer
welcoming insights to other practitioners who want to apply home detection on
similar datasets, or who are in need of an assessment of the challenges and
uncertainties related to mobilizing mobile phone data for official statistics.
